A Generalized Voronoi Diagram-Based Efficient Heuristic Path Planning Method for RRTs in Mobile Robots

Chi, Wenzheng; Ding, Zhiyu; Wang, Jiankun; Chen, Guodong; Sun, Lining

doi:10.1109/tie.2021.3078390

Cited by 87 publications

(33 citation statements)

References 27 publications

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“…Umari et al [17] proposed to use a rapidly exploring random tree [18] to extract the frontier. Generally, RRT and its variant algorithms [19][20][21][22] are used for path planning, which can quickly grow in the unknown area and the nodes of the treetop at the boundary are then recorded as the candidate frontiers. The RRT-based frontier extraction method is widely used since it does not need the precise construction of the whole map and thus saves computing resources.…”

Section: Related Workmentioning

confidence: 99%

Estimated path information gain-based robot exploration under perceptual uncertainty

et al. 2022

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At present, the frontier-based exploration has been one of the mainstream methods in autonomous robot exploration. Among the frontier-based algorithms, the method of searching frontiers based on rapidly exploring random trees consumes less computing resources with higher efficiency and performs well in full-perceptual scenarios. However, in the partially perceptual cases, namely when the environmental structure is beyond the perception range of robot sensors, the robot often lingers in a restricted area, and the exploration efficiency is reduced. In this article, we propose a decision-making method for robot exploration by integrating the estimated path information gain and the frontier information. The proposed method includes the topological structure information of the environment on the path to the candidate frontier in the frontier selection process, guiding the robot to select a frontier with rich environmental information to reduce perceptual uncertainty. Experiments are carried out in different environments with the state-of-the-art RRT-exploration method as a reference. Experimental results show that with the proposed strategy, the efficiency of robot exploration has been improved obviously.

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Section: Related Workmentioning

confidence: 99%

Estimated path information gain-based robot exploration under perceptual uncertainty

et al. 2022

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“…Optimal path selection needs to be determined based on the flight performance constraints, the specific mission requirements, and the flight environment constraints [5,6]. Scholars have conducted a lot of research on the UAV path planning problem and proposed a series of algorithms, such as graph-based optimization methods, including the visibility graph (VG) algorithm [7] and Voronoi diagrams [8]; the searching-based methods, including the Dijkstra [9] algorithm, A* algorithm [10] and D* algorithm [11]; the sampling-based methods, such as PRM algorithm [12] and RRT algorithm [13]; the nature-inspired methods, such as genetic algorithm (GA) [14], ant colony optimization (ACO) [15], artificial potential field algorithm [16], particle swarm optimization (PSO) [17] and fluid-based algorithm [18]; and other methods, such as control theory-based methods [19].…”

Section: Introductionmentioning

confidence: 99%

FC-RRT*: An Improved Path Planning Algorithm for UAV in 3D Complex Environment

Guo

Liu

et al. 2022

IJGI

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In complex environments, path planning is the key for unmanned aerial vehicles (UAVs) to perform military missions autonomously. This paper proposes a novel algorithm called flight cost-based Rapidly-exploring Random Tree star (FC-RRT*) extending the standard Rapidly-exploring Random Tree star (RRT*) to deal with the safety requirements and flight constraints of UAVs in a complex 3D environment. First, a flight cost function that includes threat strength and path length was designed to comprehensively evaluate the connection between two path nodes. Second, in order to solve the UAV path planning problem from the front-end, the flight cost function and flight constraints were used to inspire the expansion of new nodes. Third, the designed cost function was used to guide the update of the parent node to allow the algorithm to consider both the threat and the length of the path when generating the path. The simulation and comparison results show that FC-RRT* effectively overcomes the shortcomings of standard RRT*. FC-RRT* is able to plan an optimal path that significantly improves path safety as well as maintains has the shortest distance while satisfying flight constraints in the complex environment. This paper has application value in UAV 3D global path planning.

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“…Several path-planning algorithms were proposed in terms of various theories that were divided into three types [6]. It has been noticed that the main issue of path planning is how to find the optimal (the shortest) path between the start and the goal points while avoiding collision with obstacles.…”

Section: Introductionmentioning

confidence: 99%

Static and Dynamic Path Planning Algorithms Design for a Wheeled Mobile Robot Based on a Hybrid Technique

2022

IJIES

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During the previous years, the number of researchers who discussed the field of path planning algorithms has increased. In this paper, a proposed hybrid technique for path planning based on the benefits of sampling-based algorithms and heuristic path planning algorithms was enhanced. Rapidly-exploring Random Tree star (RRT*) was used to generate the candidate nodes that represent a collision-free path. These candidate nodes were applied to the Particle Swarm Optimization (PSO) algorithm to generate the shortest and the smoothest path planning (the optimal path planning) for the mobile robot. The RRT*PSO algorithm was applied in two scenarios (static and dynamic environments) with a workspace of [500×500] cm using MATLAB 2021a. In the static environment, a workspace full of obstacles was chosen to find the shortest collision-free path. In addition, a reference path equation was found to calculate the reference linear and angular velocities of the mobile robot as well as the linear and angular velocities of the right and left wheels. In this work, the suggested hybrid RRT*PSO algorithm improves path length 34.27% compared to the A* algorithm and the fuzzy analytic hierarchy process (A*-FAHP hybrid) algorithm and 0.35% compared to the Self-adaptive evolutionary game-based particle swarm optimization (SAEGBPSO) algorithm. In the dynamic environment, an algorithm named (contour path down and contour path up) was suggested to avoid collisions with dynamic obstacles. By using this suggested algorithm, the reference linear and angular velocities of the mobile robot as well as the linear and angular velocities of the right and left wheels were calculated to obtain a smooth path followed and free-navigation with dynamic obstacles in an environment.

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A Generalized Voronoi Diagram-Based Efficient Heuristic Path Planning Method for RRTs in Mobile Robots

Cited by 87 publications

References 27 publications

Estimated path information gain-based robot exploration under perceptual uncertainty

Estimated path information gain-based robot exploration under perceptual uncertainty

FC-RRT*: An Improved Path Planning Algorithm for UAV in 3D Complex Environment

Static and Dynamic Path Planning Algorithms Design for a Wheeled Mobile Robot Based on a Hybrid Technique

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