Obstacle Avoidance and Navigation Planning of a Wheeled Mobile Robot using Amended Artificial Potential Field Method

Sudhakara, Priyanka; Ganapathy, Velappa; Priyadharshini, B.; Karthika, S.

doi:10.1016/j.procs.2018.07.076

Cited by 56 publications

(31 citation statements)

References 9 publications

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“…Traditional manual detection methods have some problems, such as low efficiency, high risk and high labor intensity, which can not meet the requirements of current transmission line detection [9] . In order to ensure the hierarchical fuzzy obstacle avoidance effect of omnidirectional mobile robot, the inspection and control platform of omnidirectional mobile robot is standardized [10] . The main process includes inspection and debugging, information transmission, path selection, path inspection, etc.…”

Section: Hierarchical Fuzzy Control Methods For Omnidirectional Mobilementioning

confidence: 99%

Hierarchical fuzzy obstacle avoidance control of omnidirectional mobile robot for substation inspection

Ma¹

2021

E3S Web Conf.

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There are some limitations in the practical application of robot obstacle avoidance control methods. In order to realize the high-speed planning and obstacle avoidance processing of mobile robot, the path model, inspection route and path obstacles of the robot must be fully considered. Through feature extraction, the reasonable planning of substation inspection obstacle avoidance route is realized, and the control quantity set satisfying substation inspection is given. The hierarchical fuzzy obstacle avoidance control method of mobile robot is realized by using the optimal control set design. The simulation results show that the control method can solve various complex problems when the robot contour collides with obstacles, and can realize safe and fast control. The flexibility and robustness of obstacle avoidance are improved, and the motion state and speed of obstacles are also improved.

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Section: Hierarchical Fuzzy Control Methods For Omnidirectional Mobilementioning

confidence: 99%

Hierarchical fuzzy obstacle avoidance control of omnidirectional mobile robot for substation inspection

Ma¹

2021

E3S Web Conf.

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“…From managerial insights, Sarkar et al have focused on increasing the safety factors and reducing the setting time [9]. Some well-known path-planning techniques like A * [10,11], Dijkstra [12], distance conversion [13,14], potential field [15][16][17][18][19], sampling-based [20,21], and piano stimulation problem [22][23][24] need more information and sometimes they require a full map. This weakness shows that in unknown environments, point-to-point guidance is necessary.…”

Section: Introductionmentioning

confidence: 99%

Robot Motion Planning in an Unknown Environment with Danger Space

et al. 2019

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This paper discusses the real-time optimal path planning of autonomous humanoid robots in unknown environments regarding the absence and presence of the danger space. The danger is defined as an environment which is not an obstacle nor free space and robot are permitted to cross when no free space options are available. In other words, the danger can be defined as the potentially risky areas of the map. For example, mud pits in a wooded area and greasy floor in a factory can be considered as a danger. The synthetic potential field, linguistic method, and Markov decision processes are methods which have been reviewed for path planning in a free-danger unknown environment. The modified Markov decision processes based on the Takagi–Sugeno fuzzy inference system is implemented to reach the target in the presence and absence of the danger space. In the proposed method, the reward function has been calculated without the exact estimation of the distance and shape of the obstacles. Unlike other existing path planning algorithms, the proposed methods can work with noisy data. Additionally, the entire motion planning procedure is fully autonomous. This feature makes the robot able to work in a real situation. The discussed methods ensure the collision avoidance and convergence to the target in an optimal and safe path. An Aldebaran humanoid robot, NAO H25, has been selected to verify the presented methods. The proposed methods require only vision data which can be obtained by only one camera. The experimental results demonstrate the efficiency of the proposed methods.

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“…In [12], several path planning and navigation algorithms commonly employed in the domain of unmanned aerial vehicles (UAVs) were studied. The commonly-used methods are the probabilistic roadmap method (PRM) [13], the artificial potential field method (ARF) [14], the rapidly-exploring random tree method (RRT) [15], A* and its variants [16,17], and so on. In [18], the fast marching method (FMM) was applied to the path planning problem.…”

Section: Introductionmentioning

confidence: 99%

Mobile Robot Path Planning with a Non-Dominated Sorting Genetic Algorithm

Yang

2018

Applied Sciences

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In many areas, such as mobile robots, video games and driverless vehicles, path planning has always attracted researchers’ attention. In the field of mobile robotics, the path planning problem is to plan one or more viable paths to the target location from the starting position within a given obstacle space. Evolutionary algorithms can effectively solve this problem. The non-dominated sorting genetic algorithm (NSGA-II) is currently recognized as one of the evolutionary algorithms with robust optimization capabilities and has solved various optimization problems. In this paper, NSGA-II is adopted to solve multi-objective path planning problems. Three objectives are introduced. Besides the usual selection, crossover and mutation operators, some practical operators are applied. Moreover, the parameters involved in the algorithm are studied. Additionally, another evolutionary algorithm and quality metrics are employed for examination. Comparison results demonstrate that non-dominated solutions obtained by the algorithm have good characteristics. Subsequently, the path corresponding to the knee point of non-dominated solutions is shown. The path is shorter, safer and smoother. This path can be adopted in the later decision-making process. Finally, the above research shows that the revised algorithm can effectively solve the multi-objective path planning problem in static environments.

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Obstacle Avoidance and Navigation Planning of a Wheeled Mobile Robot using Amended Artificial Potential Field Method

Cited by 56 publications

References 9 publications

Hierarchical fuzzy obstacle avoidance control of omnidirectional mobile robot for substation inspection

Hierarchical fuzzy obstacle avoidance control of omnidirectional mobile robot for substation inspection

Robot Motion Planning in an Unknown Environment with Danger Space

Mobile Robot Path Planning with a Non-Dominated Sorting Genetic Algorithm

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