Smart Vehicle Path Planning Based on Modified PRM Algorithm

Li, Qiongqiong; Xu, Yiqi; Bu, Shengqiang; Yang, Jiafu

doi:10.3390/s22176581

Cited by 31 publications

(16 citation statements)

References 40 publications

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“…The path planning algorithm based on the probabilistic sampling technique reduces to some extent the limitation of the search direction caused by the discretized grid map, but it is worth noting that the path planning algorithm based on sampling technique provides only weak probabilistic completeness and the generated paths are not optimal in general. Based on the above problems, [ 19 ] proposed a pseudo-random sampling strategy based on spatial principal axis as reference to further optimize the path planning method based on the sampling technique. This article improves the computational efficiency of the algorithm and the quality of the generated paths by setting the distance threshold between nodes and using a two-way incremental approach for path collision detection.…”

Section: Related Workmentioning

confidence: 99%

Path Planning for Unmanned Delivery Robots Based on EWB-GWO Algorithm

Luo

Qin

et al. 2023

Sensors

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With the rise of robotics within various fields, there has been a significant development in the use of mobile robots. For mobile robots performing unmanned delivery tasks, autonomous robot navigation based on complex environments is particularly important. In this paper, an improved Gray Wolf Optimization (GWO)-based algorithm is proposed to realize the autonomous path planning of mobile robots in complex scenarios. First, the strategy for generating the initial wolf pack of the GWO algorithm is modified by introducing a two-dimensional Tent–Sine coupled chaotic mapping in this paper. This guarantees that the GWO algorithm generates the initial population diversity while improving the randomness between the two-dimensional state variables of the path nodes. Second, by introducing the opposition-based learning method based on the elite strategy, the adaptive nonlinear inertia weight strategy and random wandering law of the Butterfly Optimization Algorithm (BOA), this paper improves the defects of slow convergence speed, low accuracy, and imbalance between global exploration and local mining functions of the GWO algorithm in dealing with high-dimensional complex problems. In this paper, the improved algorithm is named as an EWB-GWO algorithm, where EWB is the abbreviation of three strategies. Finally, this paper enhances the rationalization of the initial population generation of the EWB-GWO algorithm based on the visual-field line detection technique of Bresenham’s line algorithm, reduces the number of iterations of the EWB-GWO algorithm, and decreases the time complexity of the algorithm in dealing with the path planning problem. The simulation results show that the EWB-GWO algorithm is very competitive among metaheuristics of the same type. It also achieves optimal path length measures and smoothness metrics in the path planning experiments.

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Section: Related Workmentioning

confidence: 99%

Path Planning for Unmanned Delivery Robots Based on EWB-GWO Algorithm

Luo

Qin

et al. 2023

Sensors

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“…To avoid the conflict between the planned path and obstacles, the robot needs to plan the global optimal driving path from the starting point to the target point in advance. To solve the path planning problem of mobile robots, many algorithms have been proposed, including the probabilistic roadmap (PRM) [4,5], fast extended random tree (RRT) [6,7], artificial potential field (APF) [8], genetic algorithm (GA) [9], ant colony algorithm (ACO) [10], dynamic window method [11], etc.…”

Section: Related Workmentioning

confidence: 99%

A Global Trajectory Planning Framework Based on Minimizing the Risk Index

et al. 2023

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At present, autonomous mobile robots are widely used in industrial and commercial fields. However, although the global path searched by existing mobile robot path planning methods has no collision with obstacles, there is a problem in that the path is close to obstacles and is not smooth, and there is a collision safety risk when the robot is actually moving. To solve the above problems, this paper proposes a global path planning method based on minimizing the risk index. Firstly, the distance calculation method of the heuristic function of the traditional graph search algorithm is improved to reduce the number of nodes in the search space. Additionally, by selecting the appropriate search neighborhood, the search efficiency and path smoothness of the algorithm are improved. Thirdly, to increase the distance between the original search path and obstacles, the risk index path search strategy is proposed. Finally, the minimized snap trajectory smoothing method with a safe corridor is used to smooth the original waypoint. Both simulation and real robot experimental results show that the minimum distance between waypoints and obstacles is increased by 43.72% on average, and the number of trajectory inflection points are reduced by 75.12% on average after optimization. As such, the proposed method can fully guarantee safety and generate smooth mobile robot paths in global trajectory planning tasks.

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“…PRM is a classical path planning algorithm that models the environment as a continuous space and randomly samples points within it. By connecting these sampled points, an undirected graph is constructed to facilitate path planning [6]. The PRM algorithm consists of two phases: an offline phase and an online phase.…”

Section: Prm Algorithmmentioning

confidence: 99%

Research on path planning and obstacle avoidance for unmanned platforms based on reinforcement learning

Feng,

Wang,

Chang

et al. 2023

International Conference on Electronic Materials and Information Engineering (EMIE 2023)

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Smart Vehicle Path Planning Based on Modified PRM Algorithm

Cited by 31 publications

References 40 publications

Path Planning for Unmanned Delivery Robots Based on EWB-GWO Algorithm

Path Planning for Unmanned Delivery Robots Based on EWB-GWO Algorithm

A Global Trajectory Planning Framework Based on Minimizing the Risk Index

Research on path planning and obstacle avoidance for unmanned platforms based on reinforcement learning

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