A Review on Path Planning and Obstacle Avoidance Algorithms for Autonomous Mobile Robots

Rafai, Anis Naema Atiyah; Adzhar, Noraziah; Jaini, Nor Izzati

doi:https://doi.org/10.1155/2022/2538220

Journal of Robotics

On this page

Abstract Introduction Discussion Conclusions Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

Review Article | Open Access

Volume 2022 | Article ID 2538220 | https://doi.org/10.1155/2022/2538220

A Review on Path Planning and Obstacle Avoidance Algorithms for Autonomous Mobile Robots

Anis Naema Atiyah Rafai,¹Noraziah Adzhar,¹and Nor Izzati Jaini¹

Academic Editor: Bingxiao Ding

Received22 Aug 2022

Revised04 Oct 2022

Accepted06 Oct 2022

Published01 Dec 2022

Abstract

Mobile robots have been widely used in various sectors in the last decade. A mobile robot could autonomously navigate in any environment, both static and dynamic. As a result, researchers in the robotics field have offered a variety of techniques. This paper reviews the mobile robot navigation approaches and obstacle avoidance used so far in various environmental conditions to recognize the improvement of path planning strategists. Taking into consideration commonly used classical approaches such as Dijkstra algorithm (DA), artificial potential field (APF), probabilistic road map (PRM), cell decomposition (CD), and meta-heuristic techniques such as fuzzy logic (FL), neutral network (NN), particle swarm optimization (PSO), genetic algorithm (GA), cuckoo search algorithm (CSO), and artificial bee colony (ABC). Classical approaches have limitations of trapping in local minima, failure to handle uncertainty, and many more. On the other hand, it is observed that heuristic approaches can solve most real-world problems and perform well after some modification and hybridization with classical techniques. As a result, many methods have been established worldwide for the path planning strategy for mobile robots. The most often utilized approaches, on the other hand, are offered below for further study.

1. Introduction

Since the last decade, mobile robots have been widely used in various industrial sectors. Not limited only to manufacturing industries, mobile robots are now expended in agriculture, space, military, medicine, education, rescue, and many more [1]. A mobile robot can navigate intelligently in any environment [2]: static, dynamic, uncluttered, uncertain, and many more. Navigation is a technique for moving a robot from one location to another in a variety of settings. Path planning entails determining the most efficient and collision-free route from the starting point to the destination. It is an essential task for mobile robot navigation [3]. Finding a safe path planning for a mobile robot that travels the shortest distance, follows the smoothest path, and consumes the least amount of energy and time is a prerequisite for developing mobile robot systems. Thus, the appropriate choice of optimization performance is a crucial phase in mobile robot path planning.

This research uncovers a literature review of alternative path planning and mobile robot navigation methodologies. The central difficulties in mobile robots are navigation and path planning, which have been the subject of decades of research. As a result, several methodologies were presented and utilized in the mobile robot path planning problem. The strategies for optimizing mobile robots may be divided into two groups: nondeterministic or classical approach and deterministic or heuristic approach. The classical techniques are Dijkstra algorithm, artificial potential field, probabilistic roadmap, and cell decomposition. The heuristic approaches are, for instance, fuzzy logic, neutral network, particle swarm optimization, cuckoo search optimization, genetic algorithm, and artificial bee colony. Figure 1 shows the overall organization of the optimization techniques used by numerous researchers.

In addition, hybridization approaches have recently been used in path planning issues for mobile robots to get better results than any other method, called evolutionary algorithms, such as hybridization of fuzzy and nondeterministic algorithms or hybridization of neural networks and nondeterministic algorithms [4].

2. Classical Approach

Classical methods were presented and established in the 1980s and 1990s. Before developing the heuristic approach, classical approaches were often used by researchers to solve path planning problems [5]. However, the main issue with this method is that it has a high processing cost and fails to adapt to the environment’s unpredictability, which is why it is rarely adopted for concurrent performance [1]. This section presents a few classical approach methods that are popular in its class such as Dijkstra algorithm, artificial potential field, probabilistic road map, and cell decomposition.

2.1. Dijkstra’s Algorithm

Edsger Wybe Dijkstra introduced Dijkstra’s algorithm (DA), the Dutch scientist, back in 1956 and published in 1959 [6]. The shortest path issue between one node and another in a directed graph is solved using this method, one of the most commonly used techniques [7], representing discretized workspace roadmaps [7]. This algorithm is regularly applied in path planning. DA is a well-known strategy; however, it is less valuable when the starting and goal sites are far apart [8]. Nevertheless, Nato and Sato [9] devise an expanded DA to counter the disadvantage. This method is also used in an algorithm to find the shortest evacuation path. It works by finding the maximum value. The weight of each edge in the graph, the distance between points, must have a positive value (weight more and equal to zero). Below are the following steps:(1)Set all distances to infinity and space to zero (0) except for the beginning point.(2)Make all nodes, including the initial point, nonvisited.(3)Assign the current node “C” to the nonvisited node with the shortest current distance.(4)For each of the current node’s neighbours “N,” multiply the current distance of “C” by the weight of the edges linking “C” and “N”. Set it as the new current distance of “N” if it is less than the current distance of “N”.(5)Check the box next to the current node “C” to indicate that it has been visited.(6)Repeat steps 3–6 until the desired point has been reached.

Kirono [10] used DA to determine the vehicle routes on toll roads. The pseudocode used in [10] is as in Figure 2.

Amaliyah et al. [11] use the DA to determine the shortest distance between cities on the island of Java. In Broumi et al. [12], this method has been redesigned to deal with the situation when most network parameters are unknown and expressed as neutrosophic values. Fuhou and Jiping [13] presented a shortest path algorithm for massive data based on DA. They proved that DA could save much memory and be suitable for use in a network with massive nodes. An improvement of this method and its application in path planning have been presented in Fan and Shi [14], and it has been shown that the upgrades are reasonable and practical. Finally, in [15], the path planning problem in rectangular mesh networks was addressed by finding the shortest link between each pair of sites using a Dijkstra-based greedy algorithm.

2.2. Artificial Potential Field

Khatib [16] introduced the artificial potential field (APF) method for the mobile robotics problem by believing the divergences between attraction and repulsion forces help robot movement inside the environment. The idea of the APF is that the mobile robot movements are within the field of forces. The attractive force attracts the robots to reach the goal position and repulsive force keeps them away from each obstacle [17], as shown in Figure 3.

One drawback of this APF method is that local minima are present in the field, so the planner might get trapped in local minima. Thus, to keep the robot from being stuck in local minima, Cheng and Zelinsky [18] used high-magnitude attraction forces for brief periods of time at random places. A multipoint APF has been utilized in [19] to solve obstacle avoidance problems. The use of potential fields has been performed out by Luis and Tanner [20] for obstacle avoidance as well as motion planning in a nonholonomic mobile robot. The author in [21] suggested using repulsing force to lessen the oscillations and reduce a collision when the obstacles are too close to the target. The APF was used in [22] to offer a chain of communication relays in a dynamically changing environment with obstacles present between unmanned aerial vehicles (UAVs). Dai et al. [23] proposed a hierarchical APF method for UAVs’ path planning in clustered environments.

2.3. Probabilistic Roadmap

Kavraki et al. [24] first introduced the probabilistic roadmap (PRM) in 1996 with the central objective mind providing a path for a robot in a static workspace. PRM has the ability for multiquery planning [25]. Road maps refer to the connectivity of the robot’s configuration free space plotted on a 1-dimension curve or lines. The roadmap has likewise been named highway strategy [1], withdrawal approach, and skeleton system [3]. It is used to construct road maps over the visibility graph as well as the voronoi graph [1] to establish the shortest path from the initial to the destination place. A PRM visibility graph and a Voronoi graph are shown in Figure 4 below.

In the visibility graph, the obstacles are represented as polygons [15]. The vertical of the polygonal obstacles have nodes connected. Therefore, road maps will be derived as near as possible to obstacles, and path length will be minimized. In the Voronoi graph, the plane’s edges are separated by using two nearby places on the obstacle’s edges as central points, and then the region is split into subregions. The road maps keep as distant as possible away from obstacles; thus, the path is safe but longer than the visibility graph [2]. A slight variation in PRM was produced to determine the shortest path using the slow collision checking approach in [26]. In [27], a hybrid method to achieve path optimality was provided by combining the Voronoi diagram with the visibility graph in PRM. Also, [28] combined the Voronoi diagram and visibility graph to get the optimal path. An elastic PRM was presented in [29] for autonomous mobile robot motion planning. Mika [30] proposed enhanced sampling in PRM to minimize the configuration space. The PRM was utilized to test the navigation in a 3D environment for UAVs [31] and simulate multirobot motion planning [32].

2.4. Cell Decomposition

The cell decomposition (CD) technique provides a fundamental idea for classifying the free space and occupied space by obstacles between geometric areas or cells [2, 3]. The breakdown of open space into a series of simple sections is called a cell [2]. The goal of CD is to reduce the search area by adopting a cell-based representation. In addition, the aim is to produce a succession of collision-free cells from the beginning position to the target point [33]. The initial CD steps can be summarized as follows [31]:(1)Divide open regions into basic, related areas known as cells.(2)Next, figure out which open cells are connected and create an availability graph.(3)Next, identify the cells of the underlying and objective units and search the availability chart for a route that connects the underlying and objective cells.(4)Finally, using an appropriate searching method, find a path within each cell from the cells grouping.

Seda [34] in 2007 recommended the following steps for CD:(1)Separate the search area into cells, which are corresponding regions.(2)Use neighbouring cells to make a chart. Vertices signify cells and edge link cells with a standard limit in such a graph.(3)Create a succession of collision-free cells from the start to the objective cells by identifying the goal and start cells.(4)Provide a route from the cell series that has been constructed.

In short, this method differs the area into nonoverlapping grids called cells and uses connectivity grids for crossing from one cell to another cell from start cells to goal. This method is classified as exact, approximate, and probabilistic CD depending on the assignment of the borders between cells [3]. In an exact CD, the decomposition is lossless [35], and the form and size of cells are not fixed [1]. On the other hand, the approximate CD has an approximated decomposition result as actual maps [36], and the grid has a specific shape and size [1]. Finally, the probabilistic CD is like an approximate CD, excluding the cell borders, which do not represent any physical meaning [37]—Figures 5 and 6 show CD systems in three classes.

The probabilistic CD was presented in [37] for a mobile robot path planning that brought milk from the fridge to the kitchen table and provided a comparison study between rapidly random trees. The CD was compared with PRM in [34] and showed that PRM could purge CD disadvantages. Tunggal et al. [38] produced the fuzzy logic and CD to work in an ambiguous environment for real-time operation. In [39], a sensor-based CD model was developed to cope with an unknown workplace for mobile robot duties by integrating it with a laser scanning approach to avoid obstacles. Gonzalez et al. [8] presented comparative studies on the trajectories resulting from cell decomposition methods. Finally, a hybridization of fuzzy logic with the CD method was shown in [40] to apply to aerial vehicles. Table 1 summarizes a few works done in the mobile robot navigation field using a classical approach.

The table above investigates the benefits and drawbacks of classical approaches. Dijkstra’s approach is thought to be simple and efficient enough to be used for somewhat big problems. However, it cannot be employed in negative weight without costing a significant amount of time and wasting critical nodes. Artificial potential field, on the other hand, have a basic structure and are straightforward to execute. It may also be highly efficient. Local minima might occur when the robot passes through a bounding area. Roadmaps are possible in both simulated and real-life conditions for known environments, but they are inefficient when dynamic constraints and diverse limitations exist. As with APF, cell decomposition is simple to construct but results in infeasible solutions and complexity. As a result, numerous researchers have employed classical algorithms, which may be applied in a wide range of situations. Generally, classical approaches lack flexibility and adaptability and are inadequate for dynamic environments. Therefore, heuristic approaches are provided to solve the flaws of these approaches.

3. Heuristic Approach

A heuristic approach is created for cracking the problems more quickly [41]. The method has proven its effectiveness and has become widely used in autonomous navigation [6].

3.1. Fuzzy Logic

Fuzzy logic (FL) is a technique for persuading a person's intellect. FL is a unified approximation (linguistic) method for concluding uncertain facts using uncertain rules [42]. In 1965, Lotfi A. Zadeh was the first person who introduced the idea of the FL system [43]. Yet, his vision was expanded later in many fields as FL serves as a formal plan to represent and execute human experts’ heuristic intelligence and observation based manners [3, 33]. Figure 7 is an example of a primary FL controller used in [1].

Hex Moor [44] was the first to apply the FL concept to robotic route planning and obstacle avoidance. The Takagi-Sugeno technique was then used to construct fuzzy robot motion planning [45]. Similarly, in [46], fuzzy robot motion planning was proposed utilizing the genetic algorithm with a fuzzy critic. A hybridization algorithm employs FL and GA for mobile robot path planning problems given in [47]. Wang and Liu [48] created the FL route planning approach in an unknown environment. A mobile robot path planning algorithm based on FL and neural networks was designed in [49]. Finally, Chelsea and Kelly [7] presented an FL controller for UAVs in a 2-dimensional environment, while Lei et al. [50] did so in 3-dimensional space with full cell or battery hybrid power. Then, the navigation in 3-dimensional space also was presented in [51] using FL for aerial robots and [52] for underwater robots. In addition, the Mamdani type-based FL controller tracks the moving obstacles for a nonholonomic wheeled mobile robot [53].

3.2. Neural Network

A neural network (NN) is an intelligent system inspired by the natal sensory system, which was initially developed by [54] for mobile robot route planning. It mimics the neurons in the human brain [55]. The NN has been used in various domains, including discovery, search optimization, pattern recognition, image processing, mobile robot route planning, signal processing, and many more. NN consists of different plain and highly interconnected processing elements that relocate the data to external inputs [1]. The input layer, the target, and the output of the NN path planning are shown below in Figure 8.

Yangmin and Xin [56] combine the adaptive NN with the particle swarm optimization to apply for mobile robots where particle swarm optimization works as a path planner that provides a smooth path for the adaptive NN controller, which drives the mobile robot. Zhu and Yang [57] proposed the NN method for dynamic task assignment for multirobot. Singh and Parhi presented the NN technique to enable mobile robots to travel in a known or unknown environment in [58] and designed multilayer feed forward NN that controls the steering angle in [59]. A novel hybrid approach combining the NN with the FL was made in [60] for mobile robot navigation. Likewise, in [61], the author presented a hybridization approach between the FL and the NN for manifold mobile robot navigation in a chaotic condition. The basic NN was modified in [62] to form a guided autowave pulse coupled neural network (GAPCNN) for mobile robot movement in static and dynamic environments. The GAPCNN’s purpose is to get fast parameter convergence for the mobile robot. Otherwise, the NN method has been utilized in mobile robot path planning problems for aerial robots [63] by using MATLAB as shown in Figure 9, humanoid robots [64], underwater robots [65], and industrial robots [66], respectively.

3.3. Particle Swarm Optimization

Particle swarm optimization (PSO) is an algorithm in path planning established by Kennedy and Eberhart in [67]. The social behaviour of a group of birds, a school of fish, or a flock of animals finding food and adjusting their environment as well as dealing with predators [68] is used to simulate this nature-based heuristics approach—these mimics label every population member as particles, representing a potential solution for the problem. PSO is identical to GA in that the procedure starts with a randomly initialized population [69]. However, unlike GA, each possible solution is given a random velocity. Thus, the attainable solutions referred to as particles move around the problem space. As shown in Figure 10, the basic PSO concept is specifically based on their placement and velocity in the search area.

The PSO is used in numerous fields of mobile robot path planning for navigation by a humanoid robot [70], an industrial robot [71], a wheeled robot [72], an aerial robot [73], and an underwater robot [74] in an unknown and complex 3D environment. Zhang and Li [75] applied PSO to the mobile robot in the dynamic environment to optimize its travel time. A new path planning method based on the binary PSO algorithm has been presented in [76]. The authors have proven that this approach rectifies the premature convergence problem and obtains a shorter path length and convergence speed near GA. The exact contribution was proposed in [77]. The simultaneous localization and mapping (SLAM) issue for a MR was solved in [78] using a modified PSO and a fuzzy evolutionary approach. Tang, Li, and Jiang [79] addressed the SLAM problem for MR navigation by using a multi-agent particle filter in an unknown environment. The PSO algorithm for multiple MR was presented based on navigation in [80]. The PSO and Darwinian PSO (DPSO) systems were modified in [81] for multiple MR navigation in the real world to derive mutual communication problems and collision-free paths. Then, Chen et al. [82] discovered that a human expert’s control strategy could learn depending on the wall-following robot navigating in the vague environment using a multi-category classifier. In [83], a sensor-based PSO-fuzzy type 2 model for multiple MR navigation was presented. At the same time, [84] derived an enhanced PSO and a gravitational search algorithm that have been combined for multiple MR navigation to develop an optimal path in a cluttered environment. Yang et al. [85], on the other hand, improve the PSO approach by taking into consideration the identification of the parameter for the Preisach model as the hanging issue for decades. Li and Chou [86] developed a self-adaptive system PSO technique (SLPSO) for solving the MR a minimization multiobjective optimization problem and path planning issue.

3.4. Genetic Algorithm

Genetic algorithm (GA) is an optimization technique that refers to natural genetics and natural selections that Bremermann first discovered in 1958 [87]. This fundamental idea led the GA to mimic the concept of the survival of the fittest. The fittest value represents every member of the population. The most vital members of the population will survive, while the weaker members will be abandoned. As per their fitness value, the surviving members allow the genes to be passed down to the next generation by bio-inspired operators like crossover, mutation, and selection. This randomized structure information was combined to create a search algorithm that produced solutions to the given problems to develop feasible paths. The population can be said to be in a binary string arrangement (refer to chromosomes). Any bio-inspired operator must sustain population diversity. By preventing early convergence, the chromosome’s population is switched to another. Once the populace converges, the algorithm will be terminated [1]. The basic GA is outlined as follows:(1)Primary population - Random populations are initiated with string and the objective function.(2)Produce a new population based on a Darwinian evolutionary theory with three genetic operators, as shown in Figure 11.(3)Continue to produce a new population until stop conditions are reached as follows [15]: Time limit: It has been operating for a certain amount of time Fitness value: It has reached the satisfactory fitness level required for the algorithm Generations: It has been generated the maximum number of generations

Professor J. Holland initially proposed the application of GA in computer science in 1975 [88]. Shibata and Fukuda [89] provided GA in a static environment for the navigation of MR. The path planning strategy in a static environment using GA for multiple MR is presented [90, 91]. A matric-binary codes-based GA was proposed in [92] for a static and dynamic environment for MR navigation. Navigation in a dynamic environment with static and obstacles was addressed in [93] for a multi-MR. In the face of shifting impediments, navigation in an uncertain environment is presented in [94]. The GA is used in numerous fields of mobile robot path planning problems for navigating a humanoid robot [95] and the underwater robot navigation challenge in 3D path planning [96] and aerial robot [97, 98]. The researchers also utilized the hybridization of GA with other approaches for MR navigation for better outcomes in path planning problems like GA-PSO [99], GA-FL [100], and GA-NN [101].

3.5. Cuckoo Search Algorithm

Yang and Deb [102] acknowledged the cuckoo search algorithm (CSA) as a path planning method in 2009. This method mimics the cuckoo species’ swarm intelligence or lackadaisical conduct when depositing their eggs in other host nests. Three main behaviours of CSA are as follows:(a)In each iteration, each cuckoo is only permitted to lay one egg at a time in a randomly selected nest based on Levy flights.(b)The nest is full of high-quality eggs passed down to the next generation (elitism).(c)The available host nests are rigid. The probability for the host birds to discover the egg laid by a cuckoo in each nest is . The host bird has the option of discarding the egg, dumping the nest, and starting over.

CSA is considered a freshly developed heuristic algorithm in mobile robot path planning; hence, its use is limited. A hybrid approach between CSA and differential evolution (DE) for a 3D world that is unknown was proposed by Wang [103] to increase the rate of global convergence. Xie and Zheng [104] also suggested the hybrid approach by combining the CSA and DE algorithm to solve the 3D aerial vehicle path planning. In an unfamiliar environment for multiple mobile robots (MR) navigation, [105] introduced a hybridization of CSA and an adaptive neuro-fuzzy inference system to create improved outcomes. The algorithm is shown in Figure 12.

The authors proposed a stand-alone CSA to navigate the wheeled MR in a static, partially unknown environment. The simulation and experimental in real-time only showed a small deviation error. Besides, the application of CSA can be found in vehicle path planning problems [107] and scheduling [108].

3.6. Artificial Bee Colony

Karaboga [109] developed the artificial bee colony (ABC) technique in 2005, swarm-based intelligent foraging behaviour of honeybees activities for searching their food. Three rules in the ABC model are as follows:(a)Employed foraging bees: Employed bees will be sent to food sources (the nearest colony) and scanned for food quality.(b)Unemployed foraging bees: After receiving information from employed bees, unemployed bees observe the preferred food sources and evaluate the rich sustenance sources.(c)Food sources. The forager’s bees belonging to rich sustenance will distribute to the feasible food sources while foragers surrender with weak food sources causing objectionable criticism.

Saffari and Mahjoob [110] used the ABC algorithm for MR navigation in a static environment through simulational experiments. The author [111] proposed using MR interior navigation in a static environment to determine the best path in real-time.

Ma and Lei [112] proposed the algorithm for a real-time, dynamic environment. The application of the ABC algorithm in a static environment for multiple MR can be seen in [113, 114]; wheeled MR was tested underwater [115]; autonomous vehicle routing problem [116]; and aerial robot [117]. The modified ABC algorithm has been performed for the unmanned combat aerial vehicle (UCAV) navigation problem in [118] to obtain an optimal path in a 3D world and for the unmanned helicopter in [119] to undergo the challenge mission. With the help of swarm intelligence, [120] offered a strategy for the approach to the improvement of ABC to tackle the necessary difficulty in mobile robot path planning. Table 2 summarizes a few works carried out in the mobile robot navigation field by using a heuristic approach.

Although the heuristic technique was developed to address the limitations of the classical approach, it still has advantages and disadvantages. Fuzzy logic may lessen reliance on environmental information while also providing strong adaptability and performance. However, because fuzzy rules are frequently set by experts, robots are incapable of learning and have limited flexibility. Neural networks may perform tasks that a linear programme cannot, but large networks require a long processing time. Particle Swarm Optimization is simple to build and requires fewer parameters; however, it has a problem regulating parameters. Multiobjective optimization and stability are supported by genetic algorithms. It derives from a high computation time. Cuckoo Search is also simple to implement because only one parameter is required. However, it can only be utilized to remedy persistent issues. The search space is restricted by the initial solution in a bee colony.

4. Discussion

Among the earlier cultivated methods, heuristic approaches are comparatively novel and have significant scope in mobile robot navigation. It has been observed that researchers are now working on many optimization criteria in order to get the best possible outcomes. Hybridization of the algorithms with the optimal combination of optimization criteria results in the greatest possible performance. A long time ago, most of the studies were conducted using classical approaches. A few limitations were identified for the classical approach, including trapping in local minima, computational intensiveness, demand in various environments, and the inability to handle maximum uncertainty. Therefore, to overcome these drawbacks, researchers come up with heuristic approaches. Classical approaches are usually utilized in a known environment, while heuristic approaches are adopted in an unknown environment due to their efficiency over classical approaches. It is observed that heuristic methods can solve most real-world problems and perform well after some modification and hybridization with classical methods for better results. Heuristic approaches have been widely used over a 3D workspace for underwater, unmanned, aerial, and humanoid, while classical approaches are not intelligent enough for independent path planning in a 3D environment.

5. Conclusions

This paper discusses the most often used classical and heuristic techniques for autonomous mobile robot path planning. The Dijkstra algorithm, artificial potential field, probabilistic roadmap, and cell decomposition were among the classical approaches presented, as were heuristic approaches such as fuzzy logic, neural network, particle swarm optimization, genetic algorithm, cuckoo search algorithm, and artificial bee colony. There is a lack of rigorous study in the prior literature. The previous research has focused primarily on static and dynamic obstacles, respectively. Very little research has been carried out on a variety of obstacles to evaluate the problem adequately. Thus, the previous work can be extended by focusing on the type, shape, or location of obstacles using appropriate approaches.

Data Availability

No data were used to support the findings of this study.

Conflicts of Interest

The authors declare that they have no conflicts of interest regarding the publication of this paper.

Acknowledgments

The authors would like to thank the Ministry of Higher Education for providing financial support under Fundamental Research Grant Scheme (FRGS) No. FRGS/1/2019/STG06/UMP/02/16 (University reference RDU1901214).

References

B. K. Patle, G. Babu L, A. Pandey, D. R. Parhi, and A. Jagadeesh, “A review: on path planning strategies for navigation of mobile robot,” Defence Technology, vol. 15, no. 4, pp. 582–606, 2019.
View at: Publisher Site | Google Scholar
K. V. Prakash, V. R. Anto, and M. Gnanaprakash, “Study on mobile robot path planning - a review,” International Journal of Applied Engineering Research, vol. 10, no. 57, 2015.
View at: Google Scholar
M. J. Gilmartin, “Introduction to autonomous mobile robots roland siegwart and illah R. Nourbakhsh,” Robotica, MIT Press, vol. 23, no. 2, pp. 271-272, 2005.
View at: Publisher Site | Google Scholar
A. Pandey, S. Pandey, and D. Parhi, “Mobile robot navigation and obstacle avoidance techniques: a review,” International Robotics & Automation Journal, vol. 2, no. 3, pp. 96–105, 2017.
View at: Publisher Site | Google Scholar
O. I. Guneri and B. Durmus, “Classical and heuristic algorithms used in solving the shortest path problem and time complexity,” Konuralp Journal of Mathematics, vol. 8, no. 2, pp. 279–283, 2020.
View at: Google Scholar
E. W. Dijkstra, “A note on two problems in connexion with graphs,” Numerische Mathematik, vol. 1, no. 1, pp. 269–271, 1959.
View at: Publisher Site | Google Scholar
S. Chelsea and C. Kelly, “Fuzzy Logic unmannered aerial vehicle motion planning,” Advances in Fuzzy Systems, Article ID 989051, 2012.
View at: Publisher Site | Google Scholar
R. Gonzalez, M. Kloetzer, and C. Mahulea, “Comparative study of trajectories resulted from cell decomposition path planning approaches,” in Proceedings of the 2017 21st International Conference on System Theory, Control and Computing (ICSTCC), pp. 49–54, Sinaia, Romania, October 2017.
View at: Publisher Site | Google Scholar
M. Noto and H. Sato, “A Method for the Shortest Path Search by Extended Dijkstra Algorithm,” in Proceedings of the Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. “cybernetics evolving to systems, humans, organizations, and their complex interactions” cat. no.0, Nashville, TN, USA, October 2002.
View at: Google Scholar
S. Kirono, M. Arifianto, R. Putra, and A. Musoleh, “Graph-Based Modelling and Dijkstra algorithm for searching vehicle routes on highways,” International Journal of Mechanical Engineering & Technology, vol. 9, no. 8, Article ID 12731280, 2018.
View at: Google Scholar
B. Amaliah, C. Fatichah, and O. Riptianingdyah, “Finding the shortest paths among cities in java island using node combination based on Dijkstra algorithm,” International Journal on Smart Sensing and Intelligent Systems, vol. 9, no. 4, pp. 2219–2236, 2016.
View at: Publisher Site | Google Scholar
S. Broumi, A. Bakali, M. Talea, and F. Smarandache, “Applying Dijkstra algorithm for solving neutrosophic shortest path problem,” in Proceedings of the 2016 International Conference on Advanced Mechatronic Systems, November 2016.
View at: Publisher Site | Google Scholar
Z. Fuhao and L. Jiping, “An Algorithm of Shortest Path Based on Dijkstra for Huge Data,” in Proceedings of the 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, August 2009.
View at: Publisher Site | Google Scholar
D. Fan and P. Shi, “Improvement of Dijkstra’s algorithm and its application in route planning,” in Proceedings of the 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, Yantai, China, August 2010.
View at: Publisher Site | Google Scholar
N. Adzhar, S. Salleh, Y. Yusof, and M. A. Ahmad, “Routing problem in rectangular mesh network using shortest path based Greedy method,” Journal of Physics: Conference Series, vol. 1358, no. 1, Article ID 012079, 2019.
View at: Publisher Site | Google Scholar
O. Khatib, “Real-time obstacle avoidance for manipulators and mobile robots,” Proceedings. 1985 IEEE International Conference on Robotics and Automation, vol. 25, pp. 500–505.
View at: Publisher Site | Google Scholar
R. Abiyev, D. Ibrahim, and B. Erin, “Navigation of mobile robots in the presence of obstacles,” Advances in Engineering Software, vol. 41, no. 10-11, pp. 1179–1186, 2010.
View at: Publisher Site | Google Scholar
G. Cheng and A. Zelinsky, “A physically grounded search in a behavior based robot,” in Proceedings of the Eighth Australian Joint Conference on Artificial Intelligence, pp. 547–554, Wollongong, NSW, Australia, November 1995.
View at: Google Scholar
S. Saravanakumar, G. Thomas, and T. Asokan, “Obstacle avoidance using multi-point potential field approach for an underactuated flat-fish type UAV in a dynamic environment,” Trends in Intelligent Robotic, Automation and Manufacturing, Communication in Computer and Information Science, vol. 330, pp. 20–27, 2012.
View at: Publisher Site | Google Scholar
L. Valbuena and H. G. Tanner, “Hybrid Potential Field-based control of Differential drive mobile robots,” Journal of Intelligent and Robotic Systems, vol. 68, no. 3-4, pp. 307–322, 2012.
View at: Publisher Site | Google Scholar
J. Sfeir, M. Saad, and H. Saliah-Hassane, “An improved Artificial Potential Field approach to real-time mobile robot path planning in an unknown environment,” in Proceedings of the 2011 IEEE International Symposium on Robotic and Sensors Environments (ROSE), Montreal, QC, Canada, September 2011.
View at: Publisher Site | Google Scholar
O. Cetin, I. Zagli, and G. Yilmaz, “Establishing obstacle and collision-free communication relay for UAVs with Artificial potential fields,” Journal of Intelligent and Robotic Systems, vol. 69, no. 1-4, pp. 361–372, 2013.
View at: Publisher Site | Google Scholar
J. Dai, Y. Wang, C. Wang, J. Ying, and J. Zhai, “Research on Hierarchical Potential Field Method on Path Planning for UAV,” in Proceedings of the 2018 2nd IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC), Xi'an, China, May 2018.
View at: Publisher Site | Google Scholar
L. Kavraki, P. Svestka, J. C. Latombe, and M. Overmars, “Probabilistic roadmaps for path planning in high-dimensional configuration spaces,” IEEE Transactions on Robotics and Automation, vol. 12, no. 4, pp. 566–580, 1996.
View at: Publisher Site | Google Scholar
M. N. A. Wahab, S. Nefti-Meziani, and A. Atyabi, “A comparative review on mobile robot path planning: classical or meta-heuristic methods?” Annual Reviews in Control, vol. 50, pp. 233–252, 2020.
View at: Publisher Site | Google Scholar
G. Sanchez and J. Latombe, “A single-query bidirectional probabilistic roadmap planner with lazy collision checking,” Springer Tracts in Advanced Robotics, vol. 6, pp. 403–417, 2001.
View at: Publisher Site | Google Scholar
E. Masehian and M. R. Amin-Naseri, “A Voronoi diagram-visibility graph-potential field compound algorithm for robot path planning,” Journal of Robotic Systems, vol. 21, no. 6, pp. 275–300, 2004.
View at: Publisher Site | Google Scholar
R. Wein, J. V. Berg, and D. Halperin, “The visibility-Voronoi complex and its application,” Computational Geometry, vol. 36, pp. 66–87, 2007.
View at: Publisher Site | Google Scholar
Y. Yuandong and B. Oliver, “Elastic roadmaps motion generation for autonomous mobile,” Autonomous Robotics, vol. 28, Article ID 113130, 2010.
View at: Publisher Site | Google Scholar
T. Mika, “A connectivity-based method for enhanced sampling in probabilistic roadmap planners,” Journal of Intelligent and Robotic Systems, vol. 64, no. 2, pp. 161–178, 2011.
View at: Publisher Site | Google Scholar
S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, Prentice-Hall International, Hoboken, New Jersey, United States, 1995.
Y. Zhi, J. Nicolas, and A. Arab, “ACS-PRM: adaptive cross sampling-based probabilistic roadmap or multi-robot motion planning. Int. Autonomous systems 12,” Advances in Intelligent Systems and Computing, vol. 193, pp. 846–851, 2013.
View at: Google Scholar
A. Muhammad, M. A. Ali, and I. H. Shanono, “Path planning methods for mobile robots: a systematic and bibliometric review,” ELEKTRIKA, vol. 19, no. 3, pp. 14–34, 2020.
View at: Google Scholar
M. Seda, “Roadmap method vs. cell decomposition in robot motion planning,” in Proceedings of the 6th WSEAS International Conf. On Signal Processing, Robotics and Automation. Greece, Stevens Point, Wisconsin, United States, February 2007.
View at: Google Scholar
M. Barbehenn and S. Hutchinson, “Toward an exact incremental geometric robot motion planner,” Proceedings of the 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots, pp. 39–44.
View at: Publisher Site | Google Scholar
D. Zhu and J.-C. Latombe, “New heuristic algorithms for efficient hierarchical path planning,” IEEE Transactions on Robotics and Automation, vol. 7, no. 1, pp. 9–20, 1991.
View at: Publisher Site | Google Scholar
F. Lingelbach, “Path planning using probabilistic cell decomposition,” in Proceedings of the IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004, pp. 467–472, New Orleans, LA, USA, May 2004.
View at: Publisher Site | Google Scholar
T. Padang Tunggal, A. Supriyanto, N. M. Zaidatur Rochman, I. Faishal, I. Pambudi, and I. Iswanto, “Pursuit algorithm for robot trash can based on fuzzy-cell decomposition,” International Journal of Electrical and Computer Engineering, vol. 6, no. 6, pp. 2863–2869, 2016.
View at: Publisher Site | Google Scholar
B. Dugarjav, S. G. Lee, D. Kim, J. H. Kim, and N. Y. Chong, “Scan matching online cell decomposition for coverage path planning in an unknown environment,” International Journal of Precision Engineering and Manufacturing, vol. 14, no. 9, pp. 1551–1558, 2013.
View at: Publisher Site | Google Scholar
I. Iswanto, O. Wahyunggoro, and A. Imam Cahyadi, “Quadrotor path planning based on modified fuzzy cell decomposition algorithm,” Telkomnika, vol. 14, no. 2, p. 655, 2016.
View at: Publisher Site | Google Scholar
D. Rachmawati and L. Gustin, “Analysis of Dijkstra's algorithm and Aalgorithm in shortest path problem,” Journal of Physics: Conference Series, vol. 1566, no. 1, Article ID 012061, 2020.
View at: Publisher Site | Google Scholar
S. G. Tzafestas, “Mobile robot control and navigation: a global overview,” Journal of Intelligent and Robotic Systems, vol. 91, no. 1, pp. 35–58, 2018.
View at: Publisher Site | Google Scholar
L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, no. 3, pp. 338–353, 1965.
View at: Publisher Site | Google Scholar
G. Vachtsevanos and H. Hexmoor, “A fuzzy logic approach to robotic path planning with obstacle avoidance,” 1986 25th IEEE Conference on Decision and Control, vol. 25, no. 1, pp. 1262–1264, 1986.
View at: Publisher Site | Google Scholar
K. Walker and A. C. Esterline, “Fuzzy motion planning using the Takagi-Sugeno method,” Proceeding of IEEE conference, pp. 56–59, 2000.
View at: Publisher Site | Google Scholar
T. Shibata and T. Fukuda, “Intelligent motion planning by genetic algorithm with fuzzy critic,” Proceedings of 8th IEEE International Symposium on Intelligent Control, pp. 565–570.
View at: Publisher Site | Google Scholar
H. Iraj and S. Sadigh, “Path planning for a mobile robot using fuzzy logic controller tuned by GA,” in Proceedings of the 2009 6thThe International Symposium on Mechatronics and its Applications (ISMA09), UEA, Sharjah, United Arab Emirates, March 2009.
View at: Google Scholar
M. Wang and J. N. Liu, “Fuzzy Logic-based robot path planning in an unknown environment,” in Proceedings of the The International Conference Machine Learning and Cybernetics, Guangzhou, China, August 2005.
View at: Publisher Site | Google Scholar
Z. Qingfu, S. Jianyong, X. Gaozi, and Edward, “Evolutionary algorithm refining a heuristic: a hybrid method for shared-path protection in wdm networks under srlg constraints,” IEEE Trans. On Systems, Man and Cybernetics, Part B, vol. 37, no. 1, pp. 51–61, 2007.
View at: Google Scholar
T. Lei, Y. Wang, X. Jin, Z. Min, X. Zhang, and X. Zhang, “An optimal fuzzy logic-based energy management strategy for a fuel cell/battery hybrid power unmanned aerial vehicle,” Aerospace, vol. 9, no. 2, p. 115, 2022.
View at: Publisher Site | Google Scholar
Y. Abbasi, S. Mosavian, and A. Novinzadeh, “Formation control of aerial robots using the virtual structure and new fuzzy-based self-tuning synchronization,” Transactions of the Institute of Measurement and Control, vol. 39, no. 12, pp. 1906–1919, 2017.
View at: Publisher Site | Google Scholar
X. Xiang, C. Yu, L. Lapierre, J. Zhang, and Q. Zhang, “Survey on fuzzy logic-based guidance and control of marine surface vehicles and underwater vehicles,” International Journal of Fuzzy Systems, vol. 20, no. 2, pp. 572–586, 2018.
View at: Publisher Site | Google Scholar
D. Nazari Maryam Abadi and M. H. Khooban, “Design of optimal Mamdani-type fuzzy controller for nonholonomic wheeled mobile robots,” Journal of King Saud University - Engineering Sciences, vol. 27, no. 1, pp. 92–100, 2015.
View at: Publisher Site | Google Scholar
M. Zacksenhouse, R. deFigueiredo, and D. Johnson, “A neural network architecture for cue-based motion planning,” in Proceedings of the 27th IEEE Conference on Decision and Control, pp. 324–327, Austin, TX, USA, December 1988.
View at: Publisher Site | Google Scholar
A. Adham and M. David, “Review of classical and heuristic-based navigation and path planning approaches,” International Journal of Advancements in Computing Technology, vol. 5, no. 14, pp. 1–15, 2013.
View at: Google Scholar
L. Yangmin and C. Xin, “Mobile robot navigation using particle swarm optimization and adaptive NN,” in Proceedings of the Advances in Natural Computing, pp. 628–631, Berlin, Heidelberg, August 2005.
View at: Publisher Site | Google Scholar
A. Zhu and S. Yang, “A neural network approach to dynamic task assignment of multi robots,” IEEE Transactions on Neural Networks, vol. 17, no. 5, pp. 1278–1287, 2006.
View at: Publisher Site | Google Scholar
M. K. Singh and D. R. Parhi, “Intelligent neuro-controller for navigation of mobile robot,” Proceedings of the International Conference on Advances in Computing, Communication and Control - ICAC3 '09, pp. 123–128, 2009.
View at: Publisher Site | Google Scholar
M. Singh and D. Parhi, “Path optimisation of a mobile robot using an artificial neural network controller,” International Journal of Systems Science, vol. 42, no. 1, pp. 107–120, 2011.
View at: Publisher Site | Google Scholar
A. A. Baker, “A novel mobile robot navigation system using neuro-fuzzy rule-based optimization technique,” Research Journal of Applied Sciences, Engineering and Technology, vol. 4, no. 15, pp. 2577–2583, 2012.
View at: Google Scholar
J. K. Pothal and D. R. Parhi, “Navigation of multiple mobile robots in a highly clutter terrains using adaptive neuro-fuzzy inference system,” Robotics and Autonomous Systems, vol. 72, pp. 48–58, 2015.
View at: Publisher Site | Google Scholar
U. A. Syed, F. Kunwar, and M. Iqbal, “Guided auto wave pulse coupled neural network (GAPCNN) based real-time path planning and an obstacle avoidance scheme for mobile robots,” Robotics and Autonomous Systems, vol. 62, no. 4, pp. 474–486, 2014.
View at: Publisher Site | Google Scholar
C. Zhang, H. Hu, and J. Wang, “An Adaptive neural network approach to the tracking control of micro aerial vehicles in constrained space,” International Journal of Systems Science, vol. 48, no. 1, pp. 84–94, 2017.
View at: Publisher Site | Google Scholar
C. Sun, W. He, W. Ge, and C. Chang, “Adaptive neural network control of biped robots,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, no. 2, pp. 1–12, 2016.
View at: Publisher Site | Google Scholar
D. Zhu, C. Tian, B. Sun, and C. Luo, “Complete coverage path planning of autonomous underwater vehicle based on GBNN algorithm,” Journal of Intelligent and Robotic Systems, vol. 94, pp. 237–249, 2018.
View at: Publisher Site | Google Scholar
C. Sun, W. He, and J. Hong, “Neural network control of a flexible robotic manipulator using the lumped spring-mass model,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, no. 8, pp. 1863–1874, 2017.
View at: Publisher Site | Google Scholar
J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks IV, Perth, WA, Australia, November 1995.
View at: Publisher Site | Google Scholar
M. Ellips and S. Davoud, “Classic and heuristic approach in robot motion planning - a chronological review,” International Journal of Mechanical & Mechatronics Engineering, vol. 1, no. 5, pp. 228–233, 2007.
View at: Google Scholar
F. Rubio, F. Valero, and C. Llopis-Albert, “A review of mobile robots: concepts, methods, theoretical framework, and applications,” International Journal of Advanced Robotic Systems, vol. 16, no. 2, Article ID 172988141983959, 22 pages, 2019.
View at: Publisher Site | Google Scholar
P. Kumar, K. Pandey, C. Sahu, A. Chhotray, and D. Parhi, “A Hybridized RA-APSO Approach for Humanoid Navigation,” in Proceedings of the 2017 Nirma University Int. Conf. on Eng. (NUiCONE), Ahmedabad, India, November 2017.
View at: Publisher Site | Google Scholar
M. Gao, P. Ding, and Y. Yang, “Time-optimal trajectories planning of industrial robots based on particle swarm optimization,” in Proceedings of the 2015 Fifth Int. Conf. On Instrumentation and Measurement, Computer, Communication and Control (IMCCC), Qinhuangdao, China, September 2015.
View at: Publisher Site | Google Scholar
O. Castillo, R. Martinez-Marroquin, P. Melin, F. Valdez, and e. al, “Comparative study of bio-inspired algorithms applied to the optimization of Type-1 and Type-2 fuzzy controller for an autonomous mobile robot,” ELSEVIER Information Sciences, vol. 192, pp. 19–38, 2012.
View at: Publisher Site | Google Scholar
M. A. Rendón and F. F. Martins, “Path following control tuning for an autonomous unmanned quadrotor using particle swarm optimization,” IFAC-Papers on Line, vol. 50, no. 1, pp. 325–330, 2017.
View at: Publisher Site | Google Scholar
B. He, L. Ying, S. Zhang et al., “Autonomous navigation based on unscented-Fast SLAM using particle swarm optimization for autonomous underwater vehicles,” Measurement, vol. 71, pp. 89–101, 2015.
View at: Publisher Site | Google Scholar
Q. Zhang and S. Li, “A global path planning approach based on particle swarm optimization for a mobile robot,” in Proceedings of the 7th WSEAS International Conference on Robotics, Control & Manufacturing Technology, Stevens Point, Wisconsin, United States, April 2007.
View at: Google Scholar
Z. Q. Guochang and Z. Qiaorong, “Path planning based on improved binary particle swarm optimization algorithm,” Proceedings of the IEEE Conf, pp. 462–466, 2008.
View at: Google Scholar
E. Masehian and D. Sedighizadeh, “A multi-objective PSO-based algorithm for robot path planning,” in Proceedings of the 2010 IEEE International Conference on Industrial Technology, pp. 465–470, Via del Mar, Chile, March 2010.
View at: Publisher Site | Google Scholar
A. Chatterjee and F. Matsuno, “A geese PSO tuned fuzzy supervisor for EKF based solutions of Simultaneous Localization and Mapping (SLAM) problems in mobile robots,” Expert Systems with Applications, vol. 37, no. 8, pp. 5542–5548, 2010.
View at: Publisher Site | Google Scholar
X. l. Tang, L. m. Li, and B. j. Jiang, “Mobile robot SLAM method based on multi-agent particle swarm optimized particle filter,” The Journal of China Universities of Posts and Telecommunications, vol. 21, no. 6, pp. 78–86, 2014.
View at: Publisher Site | Google Scholar
S. Ahmadzadeh and M. Ghanavati, “Navigation of mobile robot using the PSO particle swarm optimization,” Journal of Academic and Applied Studies (JAAS), vol. 2, no. 1, pp. 32–38, 2012.
View at: Google Scholar
M. S. Couceiro, R. P. Rocha, and N. M. Ferreira, “A PSO multi-robot exploration approach over unreliable MANETs,” Advanced Robotics, vol. 27, no. 16, pp. 1221–1234, 2013.
View at: Publisher Site | Google Scholar
Y. L. Chen, J. Cheng, C. Lin, X. Wu, Y. Ou, and Y. Xu, “Classification-based learning by particle swarm optimization for wall-following robot navigation,” Neurocomputing, vol. 113, pp. 27–35, 2013.
View at: Publisher Site | Google Scholar
Z. T. Allawi and T. Y. Abdalla, “A PSO-optimized type-2 fuzzy logic controller for navigation of multiple mobile robots,” in Proceedings of the 2014 19th International Conference on Methods and Models in Automation and Robotics (MMAR), pp. 33–39, Miedzyzdroje, Poland, September 2014.
View at: Publisher Site | Google Scholar
P. Das, H. Behera, and B. Panigrahi, “A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning,” Swarm and Evolutionary Computation, vol. 28, pp. 14–28, 2016.
View at: Publisher Site | Google Scholar
L. Yang, B. Ding, W. Liao, and Y. Li, “Identification of Preisach model parameters based on an improved particle swarm optimization method for piezoelectric actuators in micro-manufacturing stages,” Micromachines, vol. 13, p. 698, 2022.
View at: Publisher Site | Google Scholar
M. Luo, X. Hou, and J. Yang, “Surface optimal path planning using an extended Dijkstra algorithm,” IEEE Access, vol. 8, pp. 147827–147838, 2020.
View at: Publisher Site | Google Scholar
H. Bremermann, “The evolutionary of intelligence,” The Nervous System as a Model of its Environment. Mathematics, Washington, Seattle, 1958.
View at: Google Scholar
J. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Aebor. MI, 1975.
T. Shibata and T. Fukuda, “Robot motion planning by genetic algorithm with the fuzzy critic,” Proc. 8th IEEE Int. Symp. on Int. Control, vol. 127, pp. 180–189, 1993.
View at: Google Scholar
R. Kala, “Coordination in navigation of multiple mobile robots,” Cybernetics & Systems, vol. 45, no. 1, pp. 1–24, 2014.
View at: Publisher Site | Google Scholar
F. Liu, S. Liang, and X. Xian, “Optimal robot path planning for multiple goals visiting based on tailored genetic algorithm,” International Journal of Computational Intelligence Systems, vol. 7, no. 6, pp. 1109–1122, 2014.
View at: Publisher Site | Google Scholar
B. Patle, D. Parhi, A. Jagadeesh, and S. K. Kashyap, “Matrix-binary codes-based genetic algorithm for path planning of mobile robot,” Computers & Electrical Engineering, vol. 67, pp. 708–728, 2018.
View at: Publisher Site | Google Scholar
S. Yang, Y. Hu, and M. Meng, “A knowledge based GA for path planning of multiple mobile robots in dynamic environments,” in Proceedings of the 2006 IEEE Conference on Robotics, Automation and Mechatronics, pp. 1–6, Bangkok, Thailand, June 2006.
View at: Publisher Site | Google Scholar
P. Shi and Y. Cui, “Dynamic path planning for mobile robots based on genetic algorithm in an unknown environment,” in Proceedings of the Chinese Control and Decision Conference, pp. 4325–4329, May 2010.
View at: Google Scholar
A. Kumar, P. B. Kumar, and D. R. Parhi, “Intelligent navigation of humanoids in cluttered environments using regression analysis and genetic algorithm,” Arabian Journal for Science and Engineering, vol. 43, no. 12, pp. 7655–7678, 2018.
View at: Publisher Site | Google Scholar
C. JiaWang, H. Zhu, L. Zhang, and Y. Sun, “Research on fuzzy control of path tracking for underwater vehicles based on genetic algorithm optimization,” Ocean Engineering, vol. 156, pp. 217–223, 2018.
View at: Publisher Site | Google Scholar
V. Roberge, M. Tarbouchi, and G. Labonte, “Fast genetic algorithm path planner for fixed-wing military UAV using GPU,” IEEE Transactions on Aerospace and Electronic Systems, vol. 54, no. 5, pp. 2105–2117, 2018.
View at: Publisher Site | Google Scholar
V. Roberge and M. Tarbouchi, “Massively parallel hybrid algorithm on embedded graphics processing unit for unmanned aerial vehicle path planning,” International Journal of Digital Signals and Smart Systems, vol. 2, no. 1, pp. 68–93, 2018.
View at: Publisher Site | Google Scholar
X. Wang, Y. Shi, D. Ding, and X. Gu, “Double global optimum genetic algorithm particle swarm optimization-based welding robot path planning,” Engineering Optimization, vol. 48, no. 2, pp. 299–316, 2016.
View at: Publisher Site | Google Scholar
D. K. Pratihar, K. Deb, and A. Ghosh, “A fuzzy-genetic algorithm and time-optimal obstacle-free path generation for mobile robots,” Engineering Optimization, vol. 32, no. 1, pp. 117–142, 1999.
View at: Publisher Site | Google Scholar
N. B. Hui and D. K. Pratihar, “A comparative study on some navigation schemes of a real robot tackling moving obstacles,” Robotics and Computer-Integrated Manufacturing, vol. 25, no. 4-5, pp. 810–828, 2009.
View at: Publisher Site | Google Scholar
X. Yang and S. Deb, in Proceedings of the Cuckoo Search via Levy Flights. Nature and Biologically Inspired Computing, NaBIC 2009, pp. 210–214, IEEE world congress, Coimbatore, India, December 2009.
View at: Publisher Site
G. Wang, L. Guo, H. Duan, H. Wang, L. Liu, and M. Shao, “A hybrid metaheuristic DE/CS algorithm for UCAV three-dimension path planning,” The Scientific World Journal, vol. 2012, Article ID 583973, pp. 1–11, 2012.
View at: Publisher Site | Google Scholar
C. Xie and H. Zheng, “Application of improved Cuckoo search algorithms to path planning unmanned aerial vehicle,” in Proceedings of the Intelligent Computing Theories and Application, pp. 722–729, Springer, Cham, Switzerland, August 2016.
View at: Publisher Site | Google Scholar
P. K. Mohanty and D. R. Parhi, “A new hybrid optimization algorithm for multiple mobile robots’ navigation based on the CS-ANFIS approach,” Memetic Computing, vol. 7, no. 4, pp. 255–273, 2015.
View at: Publisher Site | Google Scholar
P. K. Mohanty and D. R. Parhi, “Optimal path planning for a mobile robot using cuckoo search algorithm,” Journal of Experimental & Theoretical Artificial Intelligence, vol. 28, no. 1-2, pp. 35–52, 2016.
View at: Publisher Site | Google Scholar
L. Xiao, A. Hajjam-El-Hassani, and M. Dridi, “An application of extended cuckoo search to vehicle routing problem,” in Proceedings of the 2017 International Colloquium on Logistics and Supply Chain Management: Competitiveness and Innovation in Automobile and Aeronautics Industries, Logistiqua, Rabat, Morocco, April 2017.
View at: Publisher Site | Google Scholar
K. Bibiks, Y. F. Hu, J. P. Li, P. Pillai, and A. Smith, “Improved discrete cuckoo search for the resource-constrained project scheduling problem,” Applied Soft Computing, vol. 69, pp. 493–503, 2018.
View at: Publisher Site | Google Scholar
D. Karaboga, “An idea based on honey bee swarm for numerical optimization,” Erciyes Univesity, Engineering Faculty, Computer Engineering Department, 2005.
View at: Google Scholar
M. Saffari and M. Mahjoob, “Bee colony Algorithm for Real-Time Optimal Path Planning of mobile Robots. Soft Computing,” in Proceedings of the Computing with Words and Perceptions in System Analysis, Decision and Control 2019, pp. 1–4, ICSCCW, Famagusta, North Cyprus, September 2009.
View at: Publisher Site | Google Scholar
M. A. Contreras-Cruz, V. Ayala-Ramirez, and U. H. Hernandez-Belmonte, “Mobile robot path planning using artificial bee colony and evolutionary programming,” Applied Soft Computing, vol. 30, pp. 319–328, 2015.
View at: Publisher Site | Google Scholar
Q. Ma and X. Lei, “Dynamic path planning of mobile robots based on the ABC algorithm,” in Proceedings of the Int Conf on Artificial Intelligence and Computational Intelligence, October 2010.
View at: Publisher Site | Google Scholar
P. Bhattacharjee, P. Rakshit, I. Goswami, A. Konar, and A. Nagar, “Multi-robot path planning using artificial bee colony optimization algorithm,” in Proceedings of the 2011 Third World Congress Nature and Biologically Inspired Computing (NaBIC), pp. 219–224, October 2011.
View at: Publisher Site | Google Scholar
J. H. Liang and C. H. Lee, “Efficient collision-free path-planning of multiple mobile robots system using efficient artificial bee colony algorithm,” Advances in Engineering Software, vol. 79, pp. 47–56, 2015.
View at: Publisher Site | Google Scholar
B. Li, R. Chiong, and L. g. Gong, “Search-evasion path planning for submarines using the artificial bee colony algorithm,” in Proceedings of the 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 528–535, Beijing, China, July 2014.
View at: Publisher Site | Google Scholar
A. Bhagade and P. Puranik, “Artificial bee colony (ABC) algorithm for vehicle routing optimization problem,” International Journal of Soft Computing and Engineering, vol. 2, no. 2, pp. 329–333, 2012.
View at: Google Scholar
C. Xu, H. Duan, and F. Liu, “Chaotic artificial bee colony approach to Uninhabited Combat Air Vehicle (UCAV) path planning,” Aerospace Science and Technology, vol. 14, no. 8, pp. 535–541, 2010.
View at: Publisher Site | Google Scholar
B. Li, L. g. Gong, and W. l. Yang, “An improved artificial bee colony algorithm based on balance-evolution strategy for unmanned combat aerial vehicle path planning,” The Scientific World Journal, vol. 2014, Article ID 232704, 10 pages, 2014.
View at: Publisher Site | Google Scholar
I. Ding, H. Wu, and Y. Yao, “Chaotic artificial bee colony algorithm for system identification of a small-scale unmanned helicopter,” International Journal of Aerospace Engineering, vol. 2015, Article ID 801874, 2015.
View at: Google Scholar
A. Ćuk, J. Gavrilović, M. Tanasković, and M. Stanković, “Mobile robot path planning optimization by artificial bee colony,” International Scientific Conference On Information Technology And Data Related Research, vol. 30, pp. 399–403, 2022.
View at: Google Scholar

Copyright

Copyright © 2022 Anis Naema Atiyah Rafai et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation

Download other formats

Order printed copies

Views

3789

Downloads

1967

Citations