Obstacle Avoidance Path Planning of a 4-DOF Weapon Arm Based on Improved RRT (RRT-H) Algorithm

Zou, Kaifan; Guan, Xiaorong; Li, Zhong; Li, Huibin; Jiang, Changlong; Zhu, Zihui

doi:https://doi.org/10.1155/2024/3866834

Mathematical Problems in Engineering

On this page

Abstract Introduction Conclusion Data Availability Conflicts of Interest Authors’ Contributions Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2024 | Article ID 3866834 | https://doi.org/10.1155/2024/3866834

Obstacle Avoidance Path Planning of a 4-DOF Weapon Arm Based on Improved RRT (RRT-H) Algorithm

Kaifan Zou,¹Xiaorong Guan,¹Zhong Li,¹Huibin Li,¹Changlong Jiang,¹and Zihui Zhu¹

Academic Editor: Rohit Salgotra

Received10 Oct 2023

Revised02 Dec 2023

Accepted09 Feb 2024

Published22 Feb 2024

Abstract

To improve soldiers’ combat capability, weapon arms have a good development prospect. However, due to special work scenarios and tasks, new requirements are exerted on. Based on the fast-expanding random tree algorithm (RRT), path algorithm optimization (RRT-H) is proposed for the path planning of weapon arms. Overall path optimization is achieved by reducing the local path length with a closer path point planning to the obstacle. In a complex environment, the RRT-H algorithm can avoid local traps by guiding the new path extension direction and exploring multiple different paths in the map. The superiority of this algorithm is verified with 2D plane obstacle avoidance and pathfinding simulation experiments. Compared to , smart, and information , the RRT-H can obtain high-quality calculation results in a shorter time. After setting degrees of freedom (DOF) as that of variables, the algorithm is applied to the 4-DOF weapon arm, which confirms an effective reduction to the 4-DOF weapon arm’s motion costs.

1. Introduction

As a kind of individual intelligent equipment, wearable weapon arms gain much academic attention since they can provide wearers with accurate striking ability and a fire cover, while reducing soldiers’ training periods and training costs. Compared with traditional manipulators (mechanical arms) mainly used in manufacturing processes, wearable weapon arms are equipped with lethal weapons and coordinate with human beings in battlefield environments. A good path-planning method can effectively improve man-manipulator cooperation and, therefore, soldiers’ target tracking capacity and self-protection. For a weapon arm, it requires the good path planning to accurately avoid the human body, while efficiently aiming and tracking the target. Weapon arm researches are still at the initial stage, and there is no comprehensive theoretical support, so it is necessary to carry out relevant researches in this field. Current algorithms for path planning include artificial potential field method, ant colony optimization, genetic algorithm, A-Star (A), probabilistic roadmaps, and fast expanding random tree algorithm (usually referred to as RRT) [1–8]. Not requiring a detailed map to find a path, the RRT algorithm has computing advantages over other algorithms in the case of a multidimensional path solution of a manipulator. Yet, due to its random space exploration, there may not be a unique final solution, and most probably, they are not the optimal solutions [9]. In order to overcome these path planning limitations, many researches have improved RRT by optimizing various aspects such as the path search processes, path results, and path smoothness. The further repairs the path obtained by RRT, making the final path closer to the optimal solution [10]. Informed limits the new expansion points in the space where new expansion points can optimize the existing path [11]. Islam et al. [12] presented Smart, which limits new expansion points near the obstacles around the path, resulting in a further optimized path than those by those obtained by the former two algorithms. Burget et al. [13] introduced a bidirectional extended random tree search algorithm, which can better deal with the complex environment and improve the search efficiency. Zhou et al. [14] used the Dubins curve strategy to process the informed path and improved the path generated by the RRT algorithm. All of these improved RRT algorithms have been applied to many fields, manipulator path planning being one prominent area. Cai et al. [15] proposed an improved rapidly exploring RRT for manipulator path planning, taking the target point as the sampling point with a certain probability. The algorithm improved the directivity of path planning. To improve obstacle avoidance, the step size can be changed according to the number of obstacles in the surrounding environment [15]. Similarly, Ma et al. [16] improved the advantage of in time and path, optimizing for the manipulator. You et al. [17] preserved the initial tree, took it as the basis of pathfinding, and applied it to 9-degrees of freedom (DOF) manipulator pathfinding, with a much improved the obstacle avoidance efficiency. Yang et al. [18] combined RRT with an improved artificial potential field, provided avoid-rapidly exploring random tree. This algorithm was applied to the path planning of the forestry harvesting manipulator, which improved the smoothness of the path and the calculation time [14].

As a peculiar subcategory in manipulators, path planning of weapon arms requires a shorter calculation time and a shorter path to reduce energy loss. To better adapting to weapon arms, RRT algorithm efficiency needs tailor-made improvements, with a further reduced calculation time and optimized final path results. This paper provides an algorithm optimization called RRT-H, which can be close to the optimal solution in a shorter time. , smart, and informed are selected for comparison. The path planning in 2D space is verified to prove that the RRT-H can obtain a more efficient path in less time and less points. In this algorithm verification operation, a 4-DOF weapon arm ensures that it does not affect soldiers in space, so it can be used as the main weapon arm structure. The 4-DOF weapon arm’s path planning can effectively avoid obstacles along its movement with a shorter Euclidean distance of the end of the 4-DOF weapon arm simulation model.

In this paper, Section 2 introduces RRT and its improved algorithms, with RRT-H is introduced in more detail. Section 3 illustrates the performance of , smart, in-formed , and RRT-H in three maps, and the respective advantages and disadvantages. Section 4 proposes a tailor-made escape from local traps method is proposed, making up for a weakness in RRT-H. Section 5 conducts a simulation model of a 4-DOF weapon arm. Based on the 4-DOF weapon arm’s characteristics. Section 6 first designs a collision detection model, and applies this 4-DOF weapon arm for the path planning simulation. Concluding remarks are made in Section 7.

2. RRT-Based Obstacle Avoidance Algorithms

This section introduces and analyzes RRT algorithms, with , informed , and smart being follow-ups, and the latter of which will be compared with optimized RRT-H algorithm.

2.1. RRT Algorithm

RRT algorithm is a sampling process-based search algorithm. Its random expansion process is shown in Figure 1.

The expansion principle is as follows: P_start and P_goal are the starting and ending points of the path. The point set T is used to store points of the tree, and T’s first point is P_start. The algorithm randomly generates P_random in the map. The point closest to P_random in T is marked as P_near, which is P_new is generated between P_random and P_near. If there is no collision between P_new and P_near, this P_new will be added to T, and this P_near will be marked as P_parent to P_near. When a generated P_new is close enough to P_goal, the new path will take the P_new and P_goal as the last two points, and P_goal’s P_parent is the P_new. Starting from the P_goal, the path is generated by finding each point’s parent until the P_start is found.

Despite the great advantages of higher dimension obstacle avoidance, the path generated by the basic RRT algorithm is usually tortuous, with the possible failure of an optimal solution in the environment, a lengthened time, and unstable results.

2.2. Improved RRT Algorithms

To overcome the abovementioned weaknesses, many studies [9–12, 13] are being conducted to improve the RRT algorithm. is to add paths and points to optimize the original RRT-generated path (Figure 2).

After generating a new P_new, P_parent, and P_next will be selected to reduce the path cost. The points around P_new in T are marked as potential P_parents. The path length of each potential P_parent as parent to P_new are calculated and used to select the shortest P_parent as the best P_parent. Again, points around P_new in T are marked as potential P_nexts. By calculating the path length of each potential P_next and comparing the path of the potential P_next with the original parent point, the shortest path will be selected, and this P_new will be updated as the parent point to P_next.

Informed and smart further optimize the algorithm, both intelligently selecting the P_random (Figure 3). Simulational result from informed is shown in Figure 3(a). Informed algorithm immediately calculates the path cost as soon as it obtains the first path. Path cost sum from subsequent P_random –P_start and from the subsequent P_random–P_goal should not be higher than that of the existing path. In this way, a space is obtained, and the generated points in the space can optimize the path. For smart, after obtaining the first path smart algorithm simplifies the path. With reference to simplified results, it then analyzes the obstacle position according to the simplification results and generates a new P_random near the obstacle. smart simulation is shown in Figure 3(b). Both of them further optimize RRT algorithm but increase the calculation cost and slow down the pathfinding speed.

To maintain the previous advantages and overcome disadvantages, once the first is found, it will be directly and immediately optimized in RRT-H. It functions as if placing a curve between obstacles; stretching the two ends of a line will shorten the path within (Figure 4).

It takes three steps of optimization processes, and step one is illustrated in Figure 5.

As is shown in Figure 5, the first step is to optimize the existing path. Step 1 takes three consecutive points: P_i, P_i + 1, and P_i + 2 in the existing path. Multiple points distributed along the lines of P_i + 1 and P_i + 2, respectively, are marked as P_Ts. P_Ts are connected with P_i to generate connection lines L_Ts. According to the lengths of L_Ts, step one evenly distributes point P_Cs on each L_T. If any of P_Cs has no collision with the obstacles, delete P_i + 1 and the path between P_i,P_i + 1, and P_i + 2, connect P_i and P_i + 2 as the new path, mark P_i as a new P_i + 1, i = i + 1. If there is a collision in P_T (n), mark P_T (n − 1) as P_i + 1, i = i + 1. The optimization operation goes on. Reaching the P_goal, the first step of optimization is over.

Then, starting from the P_goal, the second round of path optimization is carried out in the same way. The first two steps lay a solid foundation for the third step, optimization. The third step is shown in Figure 6.

As is shown in Figure 6, step three selects three consecutive points P_i, P_i + 1, and P_i + 2 in the path, similar to that of the first two optimization steps. The path of P_i + 1 and P_i + 2 generates multiple points P_T, which are, respectively, connected with P_i, and then the points P_Cs are evenly distributed on L_T. Collision detection is carried out for each P_C. Assuming that the first collision position is the P_C (m) on L_T (n), and L_T (n) is the path between P_T(n) and P_i, enter P_C (m) of L_T (n − 2) into the path set, and mark P_C (m of L_T(n − 2) as P_i, P_T (n) as P_i + 1, then optimize P_i, P_i + 1, and P_i + 2 again. Distribution gaps between detection points on the path make it liable for sharp obstacles permeate in between, as is shown in Figure 7, which gives misjudgment of “no collision.” In this case, the optimization will fall into an endless loop, and the distance between P_i and P_i + 1 will be reduced close to 0. In order to avoid this problem, the judgment points distribution should be set as dense as possible. In order to avoid the occurrence of extreme conditions when the density of judgment points distribution is small enough, k is set as the minimum distance permitted between P_i and P_i + 1. If the calculated distance between P_i and P_i + 1 is less than k, then i = i + 1. Meanwhile, the path passes through the sharp parts of the obstacle. An endless loop is thus avoided, and a new path optimization starts, until getting the final optimal solution. The calculation time of the algorithm will increase with the increase of P_C density and the decrease of the k value. A balanced distribution density of P_C and a value of k are helpful for the algorithm to achieve satisfactory results (Figure 7).

According to the obstacle avoidance strategy of weapon arms in this paper (Section 5), there will be a certain fault tolerance space between the edge of obstacles and the human body. The method for jumping out of the endless loop will not affect the obstacle avoidance effect if the distribution density of points used for collision detection is reasonably planned.

3. Comparison of Path Planning Effects

With referring to the maps used in other papers [19–22], three kinds of maps are designed in this paper, which are C-shaped, spiral-shaped, and fence-shaped, respectively. These maps in this paper are only used to prove the algorithm’s superiority, ignoring map complexity influences (Figure 8).

In this paper, the algorithms are compared in terms of calculation time, number of P_randoms, and the generated path length. Both RRT and RRT-H algorithm in this paper stop the calculation after finding the path, while , smart, and informed stop the calculation after reaching 2,000 steps. Figure 9 displays the results of the five algorithms in the above three different environments.

As is shown in Figure 9, all algorithms can obtain a basic path in a short time; the paths of smart and RRT-H are shorter, that of and informed algorithms being approximately longer. In the above three types of maps, the first path obtained by informed is large. According to the principle of informed algorithm, the optimization space is planned according to the first path’s length. In the above three maps, the optimization space covers almost the entire map. Therefore, and informed would show similar performances within 2,000 steps. Thus, the informed algorithm does not have any obvious advantage in the tortuous path planning.

The path cost of , RRT-H, smart, and informed vary with the number of P_randoms (Figure 10(a)). In order to highlight the advantages of the informed , new P_start and P_goal are defined, and the path planning effect is shown in Figure 10(b).

(a)

(b)

RRT-H algorithm does not need a new P_random once it finds a path. In the above simulation experiment, the RRT-H algorithm can reach the last P_random within 200 points without any new P_random. Also, the RRT-H algorithm has the smallest P_random number in the four algorithms. RRT-H has a longer path than the other three before optimization, while a shorter one after optimization.

Because the asymptotic optimality of the , smart, and informed , the longer the time consumed, the better the result they have. The time consumption of smart, informed , and RRT-H here is compared with that of informed algorithm, to show the relationship between their respective path cost and time. Based on RRT algo, the RRT-H algorithm has a high cost at the beginning and gradually decreases with the three-step optimization. After the optimization, it can quickly exceed the other three ones and reaches a smaller path cost (Figure 11).

Adopting the common practice, each algorithm takes ten groups of data for analysis. Of all four algorithms, the average path cost and time consumption are obtained from 10 experimental data groups and compared within the same map. Then, RRT-H result histograms are compared with the 2,000 steps results of the other three algorithms results. RRT-H algorithm can optimize the path to a better result in a shorter time compared with other algorithms (Figure 12).

4. A Tailor-Made Escape from Local Traps by RRT-H

, smart, and informed have the ability to avoid local traps. In particular, the informed algorithm can effectively jump out of local traps and obtain globally optimal solutions. algorithm is not limited by local traps due to P_randoms, but it needs a large number of P_random to complete path comparison. smart is based on the paths found by . It is easier to fall into local traps, but there is still a certain probability to jump out of local traps due to the probability distribution of P_randoms out of optimization space. Because the RRT-H algorithm directly optimizes the existing path, it is easy to fall into the local traps, so it optimizes the P_randoms generating method here (Figure 13).

(a)

(b)

As is shown in Figure 13, the points on the existing path (before optimization) are entered into the path club. When a new P_random is generated, this P_random will be filtered according to the probability. The method searches a range near the P_new. When the number of points in the path club is large, the new P_random is invalid, and it generates a new P_random again. The optimization effect of this method is shown in Figure 14.

The algorithm will plan scattered multiple paths, which improves the map exploration efficiency and avoids falling into local traps to a certain extent.

Because RRT-H is easy to fall into local traps, the problem can be effectively avoided by this escape method in a complex environment, which can provide a better path planning effect when applied to the weapon arm.

5. Path Planning Modeling of a 4-DOF Weapon Arm

This 4-DOF weapon arm model is based on preliminary researches in the Lab for Individual Equipment Technology, Nanjing University of Science and Technology. According to weapon arm requirements, 4-DOF is defined to provide the weapon arm with an omnidirectional strike capability (Figure 15).

As long as a target point is on the outside ballistic track, the 4-DOF weapon arm can hit the target, regardless of the position and posture of the weapon itself to which a weapon arm is attached. Therefore, the weapon system at the end of the 4-DOF weapon arm is regarded as a translational DOF, and the 4-DOF weapon arm’s joints are regarded as rotational DOF. When the end of a translational DOF coincides with the target position, the position and posture of the weapon arm are regarded as the achievable strike posture. The arm can thus be regarded as a 5-DOF manipulator for kinematics modeling. The 5-DOF manipulator model is only used for the path planning algorithm here, and for other situations, such as actual prototypes, it is a 4-DOF model.

In this paper, the weapon arm space posture is described with the method of modified Denavit–Hartenberg (D–H) parameters. The D–H coordinate modeling is shown in Figure 16, and the D–H parameters are shown in Table 1. Then, the kinematics simulation model of the 4-DOF weapon arm is constructed and adopted for obstacle avoidance path planning, together with the obstacle position information.

6. RRT-H-based 4-DOF Weapon Arm Path Planning Algorithm

In order to apply the RRT-H algorithm to the 4-DOF weapon arm, algorithm variables are increased from 2 in the 2D space to 5 in the weapon arm simulation model. A collision detection model is set, aiming at the working conditions of the 4-DOF weapon arm. Simulation results confirm that the algorithm can be applied to an efficient path planning of the 4-DOF weapon arm.

6.1. Collision Detection of the 4-DOF Weapon Arm

With a relative position stability of both the weapon arm and the human body, it is unnecessary to build a detailed human body model as avoidance obstacles. The weapon arm is simplified as a lever structure without volume, and the human body shape is simplified as a bounding tree composed of multiple regular cuboids. The whole obstacle avoidance issue thus is simplified as a lever–cuboid collision detection. Considering the safety and its volume ignorance in the weapon arm simplification, the cuboid representing the human body is moderately extended a certain distance larger than the actual human body. The collision simplification method is shown in Figure 17.

Figure 18 is the schematic diagram of the collision detection principle. The midpoint P_O of each cuboid face was taken as the starting point. Each face generates a vector perpendicular to the face and pointing to the outside of the cuboid. P_Cs on the manipulator are used as collision detection points and are taken as the vectors D_Ss’ starting points, with the direction P_O–P_C. The dot products P_S of S_S and D_S are used to judge whether there is a collision as follows:where m is the number of points in P_C. Only when col = 6m, there is no collision between the weapon arm and the human body in this posture.

6.2. Obstacle Avoidance Strategy of the 4-DOF Weapon Arm

Given that the weapon arm is equipped with lethal weapons at its end and that it coordinates with the human wearer, the human body must be avoided, and its gun muzzle should never point at the human body in its operation process. Therefore, it is necessary to consider extending a certain distance forward at the gun muzzle. Since the length of the weapon arm in the model is short, 1 m is taken in front of the muzzle to add 10 points as P_C to participate in the collision detection in the path planning process (Figure 19).

The algorithm transforms the path from the folded state to the target-directed state by setting the same target position. The algorithm is applied to the path planning of a wearable weapon arm system, and eight path plannings are performed. The paths are then subjected to interpolation and smoothing. The resulting path is shown in Figure 20.

As shown in Figure 20, the RRT algorithm has some randomness in its computation results, while RRT-H is optimized and has relatively more stable results. The average cumulative angle changes of the 4-DOF in the path for RRT are 181.28, 172.57, 212.57, and 140.38, respectively, while for RRT-H, they are 88.38, 69.16, 92.00, and 93.89, respectively. RRT-H exhibits significantly higher path quality compared to the traditional RRT algorithm. The average optimization degree for the 4-DOF is 51.24%, 59.92%, 56.72%, and 33.11%, respectively, resulting in an overall optimization effect of 51.4%. The optimization effect is evident. In the workspace, the planning effects of the two path planning algorithms for multi-DOF manipulators are shown in Figure 21. It can be concluded that the RRT-H algorithm is meaningful for obstacle avoidance planning in multi-DOF robotic arms.

7. Conclusion

In this paper, a new RRT optimization algorithm (RRT-H) is proposed. It has obvious advantages for the environment with obstacles. RRT-H can effectively improve the pathfinding efficiency and obstacle avoidance in the given three maps. It can find the shortest obstacle avoidance path in a short time, and can avoid the local traps by tailor-made method in a complex environment. The algorithm is applied to a 4-DOF weapon arm system, which can effectively reduce the path search time and path length. The RRT-H algorithm in this paper caters to the rapid reaction needs of weapon arms with their better integration. Yet, due attention must be paid to the simplicity of the maps in this paper. RRT-H algorithm may process meaningless computations within complex maps, owing to numerous judgment points generated on the path. Increased time cost becomes inevitable. In future studies, more complex environmental information will be considered when generating collision detection points. Improvements should be directed at RRT-H’s adaptability and computational efficiency for different maps. Besides, RRT-H only considers the muzzle safety, so further studies are called for to deal with issues like more complex weapon arm characteristics and the ejecting process. In further research, the algorithm will optimize the Prandoms generation position and collision detection point distribution, further improve the algorithm’s speed and the complex environment adaptability.

Data Availability

The Python codes used and analyzed during the study are available from the first author upon reasonable request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Authors’ Contributions

Kaifan Zou, Zhong Li, Huibin Li, Zihui Zhu, and Changlong Jiang contributed to the design and implementation of the research and the analysis of the results. Kaifan Zou contributed to the writing of the first draft of the manuscript. Xiaorong Guan commented on previous versions of the manuscript.

Acknowledgments

This research was funded by the National Defense Basic Scientific Research Program of China, grant number JCKY2019209B003. Thanks to the experiment participants for their help and the Nanjing University of Science and Technology for its support to this study.

References

M. Brand, M. Masuda, N. Wehner, and X.-H. Yu, “Ant Colony Optimization algorithm for robot path planning,” in 2010 International Conference on Computer Design and Applications, pp. V3-436–V3-440, IEEE, Qinhuangdao, China, 2010.
View at: Publisher Site | Google Scholar
M. S. A. D. Ali, N. R. Babu, and K. Varghese, “Collision free path planning of cooperative crane manipulators using genetic algorithm,” Journal of Computing in Civil Engineering, vol. 19, no. 2, pp. 182–193, 2005.
View at: Google Scholar
Y. Peng and W. Wei, “A new trajectory planning method of redundant manipulator based on adaptive simulated annealing genetic algorithm (ASAGA),” in 2006 International Conference on Computational Intelligence and Security, pp. 262–265, IEEE, Guangzhou, China, 2006.
View at: Publisher Site | Google Scholar
P. E. Hart, N. J. Nilsson, and B. Raphael, “A formal basis for the heuristic determination of minimum cost paths,” IEEE Transactions on Systems Science and Cybernetics, vol. 4, no. 2, pp. 100–107, 1968.
View at: Publisher Site | Google Scholar
S. Q. Zheng, “Path planning for space manipulator to avoid obstacle based on algorithm,” Journal of Mechanical Engineering, vol. 46, no. 13, Article ID 109, 2010.
View at: Google Scholar
J. Fei, G. Chen, Q. Jia, and D. Liu, “Obstacle avoidance path planning for space manipulator based on improved probability roadmap method,” in 14th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 1963–1968, IEEE, Xi’an, China, 2019.
View at: Publisher Site | Google Scholar
L. E. Kavraki, P. Svestka, J.-C. Latombe, and M. H. 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
S. M. Lavalle, “Rapidly-exploring random trees: a new tool for path planning,” 1999, Research report.
View at: Google Scholar
M. Du, J. Chen, P. Zhao, H. Liang, Y. Xin, and T. Mei, “An improved RRT-based motion planner for autonomous vehicle in cluttered environments,” in 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 4674–4679, IEEE, Hong Kong, China, 2014.
View at: Publisher Site | Google Scholar
J. D. Gammell, S. S. Srinivasa, and T. D. Barfoot, “Informed : Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic,” in 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2997–3004, IEEE, Chicago, IL, USA, 2014.
View at: Publisher Site | Google Scholar
S. Karaman and E. Frazzoli, “Sampling-based algorithms for optimal motion planning,” The International Journal of Robotics Research, vol. 30, no. 7, pp. 846–894, 2011.
View at: Publisher Site | Google Scholar
F. Islam, J. Nasir, U. Malik, Y. Ayaz, and O. Hasan, “-Smart: rapid convergence implementation of towards optimal solution,” in 2012 IEEE International Conference on Mechatronics and Automation, pp. 1651–1656, IEEE, Chengdu, China, 2012.
View at: Publisher Site | Google Scholar
F. Burget, M. Bennewitz, and W. Burgard, “BI2: an efficient sampling-based path planning framework for task-constrained mobile manipulation,” in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3714–3721, IEEE, Daejeon, Korea (South), 2016.
View at: Publisher Site | Google Scholar
H. X. Zhou, Y. Cheng, and W. C. Liu, “Manipulator motion planning based on dubins-informed algorithm,” Techniques of Automation and Applications, vol. 39, no. 10, pp. 67–74, 2020.
View at: Google Scholar
W. T. Cai, Q. Deng, J. Zhang, Y. B. Zhang, S. Rao, and K. Yang, “Manipulator path planning based on improved RRT algorithm,” Transducer and Microsystem Technologies, vol. 38, no. 5, pp. 121–124, 2019.
View at: Google Scholar
H. L. Ma, Z. Q. Lu, and S. T. Wang, “Research on manipulator path planning based on improved algorithms,” Machine Design and Research, vol. 36, no. 4, pp. 42–46, 2020.
View at: Google Scholar
H. Y. You, S. He, H. W. Liu, and Z. B. Xu, “The mechanical arm of 9-DOF path planning based on bi-RRT algorithm,” Computer Simulation, vol. 36, no. 7, pp. 308–313, 2019.
View at: Google Scholar
Y. Yang, J. Liu, Y. Zheng, and Q. Huang, “Obstacle avoidance path planning of manipulator of forestry felling & cultivation machine,” Scientia Silvae Sinicae, vol. 57, no. 2, pp. 179–192, 2021.
View at: Publisher Site | Google Scholar
R. Mashayekhi, M. Y. I. Idris, M. H. Anisi, and I. Ahmedy, “Hybrid RRT: a semi-dual-tree rrt-based motion planner,” IEEE Access, vol. 8, pp. 18658–18668.
View at: Publisher Site | Google Scholar
J. Wang, M. Q.-H. Meng, and O. Khatib, “EB-RRT: optimal motion planning for mobile robots,” IEEE Transactions on Automation Science and Engineering, vol. 17, no. 4, pp. 2063–2073, 2020.
View at: Publisher Site | Google Scholar
Y. Li, W. Wei, Y. Gao, D. Wang, and Z. Fan, “: an improved path planning algorithm for mobile robots,” Expert Systems with Applications, vol. 152, pp. 1–11, 2020.
View at: Publisher Site | Google Scholar
O. Adiyatov and H. A. Varol, “Rapidly-exploring random tree based memory efficient motion planning,” in 2013 IEEE International Conference on Mechatronics and Automation, pp. 354–359, IEEE, Takamatsu, Japan, 2013.
View at: Publisher Site | Google Scholar

Copyright

Copyright © 2024 Kaifan Zou 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

136

Downloads

135

Citations