An Efficient RRT Algorithm for Motion Planning of Live-Line Maintenance Robots

Feng, Jiabo; Wei-jun, Zhang

doi:10.3390/app112210773

Cited by 6 publications

(3 citation statements)

References 28 publications

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“…In the realm of autonomous vehicles, path planning is a key issue. RRT algorithms can be used for route planning in autonomous vehicle navigation to generate safe and efficient driving paths considering factors such as traffic rules and obstacles [15] . This is of great significance for realizing autonomous driving technology.…”

Section: Introductionmentioning

confidence: 99%

BI-RRT*: An improved path planning algorithm for secure and trustworthy mobile robots systems

Fan,

Huang,

Huang

et al. 2024

Heliyon

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Section: Introductionmentioning

confidence: 99%

BI-RRT*: An improved path planning algorithm for secure and trustworthy mobile robots systems

Fan,

Huang,

Huang

et al. 2024

Heliyon

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“…Over the past few decades, a considerable amount of path planning algorithms have been proposed, such as artificial potential fields (APF) [7], genetic algorithm (GA) [8,9], harmony search algorithm (HSA) [10], A* algorithm [10][11][12], particle swarm optimization (PSO) [13,14], ant colony optimization (ACA) [15], rapidly exploring random tree. (RRT) [16,17], neural networks [18], etc. Pak et al [19] proposed a path planning algorithm for smart farms by using simultaneous localization and mapping (SLAM) technology.…”

Section: Introductionmentioning

confidence: 99%

A Multigoal Path-Planning Approach for Explosive Ordnance Disposal Robots Based on Bidirectional Dynamic Weighted-A* and Learn Memory-Swap Sequence PSO Algorithm

Qiao

Jiang

2023

Symmetry

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In order to protect people’s lives and property, increasing numbers of explosive disposal robots have been developed. It is necessary for an explosive ordinance disposal (EOD) robot to quickly detect all explosives, especially when the location of the explosives is unknown. To achieve this goal, we propose a bidirectional dynamic weighted-A star (BD-A*) algorithm and a learn memory-swap sequence particle swarm optimization (LM-SSPSO) algorithm. Firstly, in the BD-A* algorithm, our aim is to obtain the shortest distance path between any two goal positions, considering computation time optimization. We optimize the computation time by introducing a bidirectional search and a dynamic OpenList cost weight strategy. Secondly, the search-adjacent nodes are extended to obtain a shorter path. Thirdly, by using the LM-SSPSO algorithm, we aim to plan the shortest distance path that traverses all goal positions. The problem is similar to the symmetric traveling salesman problem (TSP). We introduce the swap sequence strategy into the traditional PSO and optimize the whole PSO process by imitating human learning and memory behaviors. Fourthly, to verify the performance of the proposed algorithm, we begin by comparing the improved A* with traditional A* over different resolutions, weight coefficients, and nodes. The hybrid PSO algorithm is also compared with other intelligent algorithms. Finally, different environment maps are also discussed to further verify the performance of the algorithm. The simulation results demonstrate that our improved A* algorithm has superior performance by finding the shortest distance with less computational time. In the simulation results for LM-SSPSO, the convergence rate significantly improves, and the improved algorithm is more likely to obtain the optimal path.

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“…In the process of tree sampling growth, pruning redundant paths can improve the planning efficiency [3]. In the movement of the robot and the manipulator, the RRT algorithm is combined with the artificial potential field method and the extended algorithm, which can remove redundant branches, select appropriate nodes and complete the path planning [4,5].…”

Section: Introductionmentioning

confidence: 99%

Research on Path Planning Algorithm for Driverless Vehicles

Pei

Zhang

2022

Mathematics

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In a complex environment, although the artificial potential field (APF) method of improving the repulsion function solves the defect of local minimum, the planned path has an oscillation phenomenon which cannot meet the vehicle motion. In order to improve the efficiency of path planning and solve the oscillation phenomenon existing in the improved artificial potential field method planning path. This paper proposes to combine the improved artificial potential field method with the rapidly exploring random tree (RRT) algorithm to plan the path. First, the improved artificial potential field method is combined with the RRT algorithm, and the obstacle avoidance method of the RRT algorithm is used to solve the path oscillation; The vehicle kinematics model is then established, and under the premise of ensuring the safety of the vehicle, a model predictive control (MPC) trajectory tracking controller with constraints is designed to verify whether the path planned by the combination of the two algorithms is optimal and conforms to the vehicle motion. Finally, the feasibility of the method is verified in simulation. The simulation results show that the method can effectively solve the problem of path oscillation and can plan the optimal path according to different environments and vehicle motion.

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An Efficient RRT Algorithm for Motion Planning of Live-Line Maintenance Robots

Cited by 6 publications

References 28 publications

BI-RRT*: An improved path planning algorithm for secure and trustworthy mobile robots systems

BI-RRT*: An improved path planning algorithm for secure and trustworthy mobile robots systems

A Multigoal Path-Planning Approach for Explosive Ordnance Disposal Robots Based on Bidirectional Dynamic Weighted-A* and Learn Memory-Swap Sequence PSO Algorithm

Research on Path Planning Algorithm for Driverless Vehicles

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