Path planning of mobile robot with PSO-based APF and fuzzy-based DWA subject to moving obstacles

Lin, Zenan; Yue, Ming; Chen, Guangyi; Sun, Jinwei

doi:10.1177/01423312211024798

Cited by 61 publications

(31 citation statements)

References 35 publications

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“…In contrast to most studies, the evaluation coefficients of the robot are adaptively adjusted according to the sea state, where the weights of velocity are reduced and the weights of distance are increased, as the sea state is poor; for example. Lin et al [20] proposed a two-layer path-planning method based on the improved APF for global path planning and the enhanced DWA with fuzzy control for local path planning. The collision-risk index and relative distance of the robot and the obstacle are considered to dynamically adjust the weights of the evaluation function and finally obtain a reasonable motion path.…”

Section: Fusion Algorithm Applied To Single-robot Path Planningmentioning

confidence: 99%

“…After velocity space search and sampling, DWA generates predicted motion trajectories for the robot within Ns based on the sampled multiple sets of velocity (v, ω) simulations and then scores each trajectory according to the appropriate objective function. The part of traditional DWA contains three evaluation functions, velocity, heading, and dist, as shown in Equation (20).…”

Section: Evaluation Functionmentioning

confidence: 99%

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Distributed Multi-Mobile Robot Path Planning and Obstacle Avoidance Based on ACO–DWA in Unknown Complex Terrain

wang

Yang

et al. 2022

Electronics

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Multi-robot systems are popularly distributed in logistics, transportation, and other fields. We propose a distributed multi-mobile robot obstacle-avoidance algorithm to coordinate the path planning and motion navigation among multiple robots and between robots and unknown territories. This algorithm fuses the ant colony optimization (ACO) and the dynamic window approach (DWA) to coordinate a multi-robot system through a priority strategy. Firstly, to ensure the optimality of robot motion in complex terrains, we proposed the dual-population heuristic functions and a sort ant pheromone update strategy to enhance the search capability of ACO, and the globally optimal path is achieved by a redundant point deletion strategy. Considering the robot’s path-tracking accuracy and local target unreachability problems, an adaptive navigation strategy is presented. Furthermore, we propose the obstacle density evaluation function to improve the robot’s decision-making difficulty in high-density obstacle environments and modify the evaluation function coefficients adaptively by combining environmental characteristics. Finally, the robots’ motion conflict is resolved by combining our obstacle avoidance and multi-robot priority strategies. The experimental results show that this algorithm can realize the cooperative obstacle avoidance of AGVs in unknown environments with high safety and global optimality, which can provide a technical reference for distributed multi-robot in practical applications.

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Section: Fusion Algorithm Applied To Single-robot Path Planningmentioning

confidence: 99%

Section: Evaluation Functionmentioning

confidence: 99%

Distributed Multi-Mobile Robot Path Planning and Obstacle Avoidance Based on ACO–DWA in Unknown Complex Terrain

wang

Yang

et al. 2022

Electronics

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“…Even though DWA has outstanding performance in dynamic environment, the response to moving obstacles and the weight coefficients of subfunctions are still worth being optimized. So as to acquire better weight coefficients, fuzzy control was applied to DWA evaluating the danger level of collision risk index and relative distance, 18 where the mobile robot can instantly turn to avoid high-speed obstacles in overtaking condition. Besides, Chang et al 19 proposed an improved DWA based on Q-learning to balance the effectiveness and speed whose evaluation functions were also extended by adding historical trajectory information.…”

Section: Introductionmentioning

confidence: 99%

An improved target-oriented path planning algorithm for wheeled mobile robots

Yao

Yue

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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This paper proposes a target posture-oriented path planning method, where an improved A* algorithm and an optimized dynamic windows approach (DWA) are used at the global and local planning aspects, respectively. The method enables wheeled mobile robots (WMRs) to plan an optimal path under different posture situations which can shorten the time of achieving navigation task in certain scene. On the hand of global path planning, target posture and orientation information are added into A* algorithm to obtain appropriate path. At the level of local path planning, based on DWA, evaluation functions are optimized to make the robot movement adapting to curve global path and dynamic obstacle. By introducing rotation index to evaluate experiment results, the tests verify that WMRs can reasonably plan more efficient path compared with traditional path planning method, by satisfying various posture demand with the aid of the proposed target posture-oriented A* algorithm, and quickly avoid dynamic obstacles via the optimized DWA. Therefore, the proposed algorithm can be effectively applied to WMRs to execute various target posture demands navigation tasks.

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“…In previous studies, researchers mainly considered using different algorithms to solve the path planning problem of mobile robots without collision: for example, the A* algorithm [ 5 , 6 ], RRT algorithm [ 7 , 8 ], Dijkstra algorithm [ 9 , 10 ], and artificial potential field method (APF) [ 11 , 12 ] and so on.…”

Section: Introductionmentioning

confidence: 99%

An improved beetle antennae search path planning algorithm for vehicles

et al. 2022

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With the development of society, the application of mobile robots in industry and life is increasingly extensive, and the local path planning of mobile robots in unknown environments is a problem that needs to be solved. Aiming at the problem that the traditional beetle antennae search (BAS) algorithm can easily fall into local optimum and the optimization accuracy is low, we propose an improved beetle antennae search. It introduces a map safety threshold, the addition of virtual target points, and the smoothing of the path. Map safety threshold means extra space with obstacles at all times, improving path reliability by avoiding collisions. Adding virtual target points reduces situations where the vehicle gets stuck in local optima. The B-spline smoothing path reduces the original path’s straight turns to improve the path’s robustness. The effectiveness and superiority of the algorithm are verified by comparing and testing the existing path planning algorithms through simulation in different environments.

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Path planning of mobile robot with PSO-based APF and fuzzy-based DWA subject to moving obstacles

Cited by 61 publications

References 35 publications

Distributed Multi-Mobile Robot Path Planning and Obstacle Avoidance Based on ACO–DWA in Unknown Complex Terrain

Distributed Multi-Mobile Robot Path Planning and Obstacle Avoidance Based on ACO–DWA in Unknown Complex Terrain

An improved target-oriented path planning algorithm for wheeled mobile robots

An improved beetle antennae search path planning algorithm for vehicles

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