Journal of Robotics

Volume 2018, Article ID 1969834, 12 pages

https://doi.org/10.1155/2018/1969834

## A Novel Edge Detection Algorithm for Mobile Robot Path Planning

^{1}Mechanical Engineering Department, Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan^{2}Department of Electrical and Computer Engineering, Khalifa University of Science and Technology, Abu Dhabi 127788, UAE^{3}Automatic Control Engineering Institute (RST), Siegen University, Siegen, Germany

Correspondence should be addressed to Rami Al-Jarrah; oj.ude.uh@aimar

Received 13 August 2017; Accepted 5 December 2017; Published 1 January 2018

Academic Editor: Zhaojie Ju

Copyright © 2018 Rami Al-Jarrah 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.

#### Abstract

A novel detection algorithm for vision systems has been proposed based on combined fuzzy image processing and bacterial algorithm. This combination aims to increase the detection efficiency and reduce the computational time. In addition, the proposed algorithm has been tested through real-time robot navigation system, where it has been applied to detect the robot and obstacles in unstructured environment and generate 2D maps. These maps contain the starting and destination points in addition to current positions of the robot and obstacles. Moreover, the genetic algorithm (GA) has been modified and applied to produce time-based trajectory for the optimal path. It is based on proposing and enhancing the searching ability of the robot to move towards the optimal path solution. Many scenarios have been adopted in indoor environment to verify the capability of the new algorithm in terms of detection efficiency and computational time.

#### 1. Introduction

Path planning is an essential part of mobile robotic systems to find the optimal path [1–4]. The boundary values of velocity, acceleration, and deceleration of a mobile robot (MR) are defined as closed mathematical forms over the spatial path. These values can be calculated offline for any MR model [1]. In [2], an approach has been proposed to generate a time optimal velocity profile for any path in static or dynamic environments. The proposed algorithms are not time-consuming if the path is planned as smooth curves. A real-time robot path planning approach based on the utilization of inequality and optimization technique has been presented in [3]. The problem of finding a path has been transformed into finding a collision free path without calculating the configuration space obstacles. In addition, many heuristic algorithms have been developed recently to be used for path planning in real world. The fundamental similarities between these algorithms and the motivation behind each one have been introduced in [4]. However, these algorithms can be grouped in many categories such as static algorithms, anytime algorithms, and replanning algorithms [4].

However, computer vision (CV) has become an important tool in modern robot systems since it provides a useful tool for detection and decision-making during robotic navigation missions. In this context, many studies have applied CV for path planning tasks [5–9]. The object detection has been proposed for MRs to perform their missions in the environment [5]. The proposed algorithm in [5] is based on collecting data images from the surrounding environment during the training phase. Whilst the algorithm is measuring the errors in data image, it updates the classifier. As a result, it provides the means of classifying and more reliable object detection. The work in [6] addressed the problem of how making a robot learns natural terrain selectively and exploits its knowledge to estimate the terrain for optimal path planning. The author proposed a scheme that combines vision learning and interaction to give the robot an ability to understand the intricate environment. In addition, particle swarm optimization (PSO) algorithm was applied to search for the optimal path. In [7], a combination between CV and robot path planning has been presented aiming to program free robot applications. Image processing and path planning tools was presented using the Wise-Shop floor framework. The authors in [9] developed a CV system for autonomous MR navigation. A vision sensor was placed on system on-board to detect and localize another robot in the simulated environment. This proposed approach used algorithm to determine the path in order to find the best solution.

However, the classification of CV systems depends on the sophisticated degree of the system itself. Therefore, CV systems would be classified into several categories such as visual tracking and visual navigation [6–9]. Since visual tracking is highly dependent on the edge, many algorithms have been introduced in the literature to develop powerful edge detectors, that is, Canny and Sobel [10–13]. The modified Canny algorithm that uses different sigma values for different sections of the image has been proposed in [14]. Each image is divided into multi-sub-images to improve the accuracy level of the final output. The investigated pixel is calculated and processed by a Gaussian filter with different sigma values. The values of sigma were chosen carefully so that the output image has only the true edge. Moreover, it has been proved that the proposed method gives better results than the original algorithm. However, the histograms of the results were not distributed uniformly and the modified algorithm is still inappropriate for noisy images [15]. The authors of [15] proposed an edge detection process based on smoothing and morphological operations. This approach was executed based on Canny edge detection principle which is represented as a combination of erosion and dilation. In addition, they compared the results with other existing techniques and exhibited that their proposed technique can produce better results. However, the approach is based on probing an image with a 2 × 2 structuring element in which each of the pixels contains a value of zero or one. So, one of the limitations on this approach is that the frame rate cannot reach the required level for real-time applications. Although the proposed algorithms in [14, 15] can be successfully implemented to solve and optimize the edge detection problem, they have inaccurate results and some edges may be lost [14, 15].

Therefore, to avoid such limitations on these algorithms, many researchers have implemented intelligent algorithms such as the fuzzy edge detection. The method of fuzzy edge representation was proposed in [16]. This representation contains the ideal edges which are not affected by any degradation. This method is more general than the classical edge representation method because it defines the location of edges using the fuzzy set theory. Moreover, this model aims is to provide more information about the location of edges and enabling more flexibility in high level processing. However, further mathematical analysis and experiments with various membership functions are needed. In addition, fuzzy image processing is capable of dealing with the uncertainty in the given data by presenting a good mathematical framework to manipulate the drawbacks in edge detection [17, 18]. Fuzzy logic based edge detection has been considered for both smooth and noisy images in [19], where the proposed method utilizes a 3 × 3 mask guided by a set of fuzzy rules. The results were compared with other edge detection algorithms like Sobel, Canny, Laplace of Gaussian, Prewitt, and Roberts. In addition, the tools of fuzzy based edge detection have been described in [20]. In [21], an edge detection method has been proposed based on Sobel algorithm and the generalized type 2 fuzzy logic systems. In addition, to limit the complexity of handling the generalized type 2 fuzzy logic, *α*-planes theory has been applied [21]. Simulation results have been obtained with the Sobel operator and the generalized type 2 fuzzy logic system.

However, the GA is a very important technique in combinatorial optimization issues [22, 23]. The objective of such algorithm in mobile robot (MR) field is to search for the optimal path and trajectory. In [22], the authors developed the genetic operators for GA to be able to solve path planning problems. They claimed that their proposed algorithm has fast convergence towards the global optimum. Their algorithm depends on three ideas: new mutation operators, matrix coding, and visible space. Even though that algorithm is effective for static environment, it is not effective for dynamic one. Moreover, the modified algorithm has been presented in [23] in order to reduce the processing time. However, these researches showed that the GA can be improved in many ways in order to solve the path planning issues. In addition, some researchers are still investigating this problem to improve the algorithm and explore more solutions. However, all researches mentioned above were accomplished using MATLAB simulations and none of them was implemented in real-time applications. Also, most of them solved the optimal path in terms of single objective. In [24], the initial working path for the robot is obtained by using Dijkstra algorithm. Then ant colony algorithm is adopted to optimize this initial path and the global optimal path is obtained. The experiments are carried out but it was assumed to have a gas hazard area only if the gas concentration is greater than 2%. The global path planning algorithm, bidirectional simultaneous visibility graph construction, and path optimization by are presented in [25]. The algorithm is not constructing a visibility graph before the path optimization to improve the time efficiency of path planning. However, the algorithm was validated only using different simulation environments. In addition, it was assumed that the environments are known and static. Also, they assumed that obstacles positions should be known in advance.

In this paper, an intelligent edge detection algorithm is proposed for robot navigation systems. This work is extended to our previous work which takes the benefits of mixing robots system heterogeneity for navigation and path planning purposes [26]. The proposed algorithm is based on a combination between fuzzy edge detection and the bacterial algorithm. The bacterial algorithm is applied to optimize the membership functions and fuzzy rules to reduce the computational time. Moreover, the optimal path is generated by the modified GA with enhanced . This combination aims to convert the optimal path to trajectory path taking into account the most important objectives in path planning, that is, the minimum travelling time, safety, security, and energy consumption. Although the conversion process addressed in this paper has been ignored by most researchers, it is an essential part of any visual navigation system. In addition, the histogram equalization is presented to stretch the image histogram and allow the proposed approach to extract more details from the image. Unlike all researches mentioned above, real-time robot navigation system is adopted here*.*

This paper is organized as follows. After this introduction, the problem formulation is described in Section 2. The intelligent computer vision is described in Section 3. In Section 4, real-time results are shown and compared to a method used in [15]. Finally, conclusions and future work are addressed in Section 5.

#### 2. Problem Formulation

The conceptual terms related to robot navigation systems are defined as follows.

*Definition 1. *The 2D map is the map that resulted from edge detection and includes information about the target object and/or any obstacle in the environment.

*Definition 2. *The path is the geometric trace that the vehicle follows to reach its destination avoiding obstacles.

*Definition 3. *Path planning is the problem of finding the geometric path from an initial point to a given terminating point (end point).

*Definition 4. *The trajectory is represented as a sequence of states visited by the vehicle based on time, velocity, and acceleration.

*Definition 5. *The trajectory planning for a robot concerns in the transition of one feasible state to another. It is referred to motion planning that is based on time, velocity, and acceleration. In addition, it should satisfy the vehicle’s kinematic limits and avoid the obstacles.

In this study, edge detection based object recognition is considered to identify specific objects such as robot and obstacles in unstructured environment. In addition, it provides important data to reconstruct the environmental map. To detect the edge in an image more accurately and efficiently, we need to design a robust approach that should work fine and deal with the noise in the image. These edges are so helpful for real-time applications such as robot navigation. Consequently, the histogram equalization is presented to stretch the image histogram to extract more details from the image. In addition, the intelligent image processing algorithm is proposed based on a combination between fuzzy edge detection and the bacterial algorithm that is applied to optimize the membership functions and fuzzy rules and to reduce the computational time of the proposed approach. The results are used to obtain the 2D map that is used in the motion planning, which is considered as an optimization problem. This means that the next step in the research is to find the multiobjective function to minimize the navigation time and the navigation distance. Moreover, a beneficial optimization should consider the safety during the navigation and successfully achieve the robot missions. Furthermore, the optimal path is generated by the modified GA with enhanced . This combination aims to convert the optimal path to trajectory path taking into account the previously mentioned objectives in path planning.

It should be noted that, during each planning cycle, the path planner module generates several trajectories from the vehicle’s current location towards the final destination point. This process is executed depending on the speed of the vehicle’s on-board sensors. In addition, each trajectory is evaluated with respect to some cost function to determine the optimal trajectory. It is assumed that the robot is placed on a plane environment and the contacts between the wheels and the ground have pure rolling and nonslipping conditions during the mission. The robot moves according to 2D map that might be static or dynamic. Also, the obstacles could be placed at any grid point in this map. The MR’s mission is to search offline and online for the optimal path for the robot from a start point to a destination point.

#### 3. Intelligent Computer Vision

The proposed CV algorithm has two main stages as it is described in the block diagram in Figure 1. First, fuzzy based intelligent image processing algorithm is combined with the bacterial algorithm. This stage is responsible for detecting the edges in the captured images, where the bacterial algorithm is used in order to optimize the membership functions and fuzzy rule. The second stage is the modified GA and the enhanced , where this stage is applied in order to find the optimal path and optimal trajectory.