Optimal path planning in cluttered environment using RRT*-AB

Noreen, Iram; Khan, Amna; Ryu, Hyejeong; Doh, Nakju Lett; Habib, Zulfiqar

doi:10.1007/s11370-017-0236-7

Cited by 73 publications

(48 citation statements)

References 26 publications

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“…A graphical simulation environment was built using MATLAB version 2017 to perform numerical and graphical comparison and analysis. We have compared the performance parameters of time, path length, and number of turns for MEA* [20] and RRT*-AB [12]. Both algorithms are implemented and tested in MATLAB.…”

Section: Methodsmentioning

confidence: 99%

“…Another RRT* variant named RRT*-Smart [31] resolved this issue to some extent and acquired a near-optimal path in shorter time than RRT*. RRT*-AB [12] is a recent variation of RRT* which rapidly converges to an optimal or near-optimal path as compared to other RRT*-based approaches [12].…”

Section: Related Workmentioning

confidence: 99%

“…Nature-inspired meta-heuristic approaches such as Genetic Algorithm (GA) [10], and Artificial Bee Colony (ABC) algorithm [11] are suitable for the optimization of multi-objective planning problems. A major drawback of these approaches is pre-mature convergence, trapping in a local optimum, high computational cost, and complex mapping of data [3,[11][12][13]. Recently, reinforcement learning has also emerged, but it is more suitable for robots learning new skills than for path-planning applications [14,15].…”

Section: Introductionmentioning

confidence: 99%

“…A major limitation of sampling-based algorithms is their slow convergence rate [3,13]. RRT*-AB [12] is a recent sampling-based planner which has resolved this issue and has improved convergence rate as compared to other SBP variants [12]. Grid-based methods are another class of planning technique applicable to low-dimensional space.…”

Section: Introductionmentioning

confidence: 99%

“…This paper is an extended version of the work presented in [20] with a detailed evaluation of performance parameters for MEA*. The purpose of this paper is to perform a comparative analysis of MEA* [20] with A*, HPA*, RRT, RRT*, and state-of-the-art sampling-based planning algorithm RRT*-AB [12] in a 2D environment as per defined metrics. The main focus of result discussion is MEA* and RRT*-AB.The assumption of application for mobile robots are as follows: 1.…”

Section: Introductionmentioning

confidence: 99%

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A Path-Planning Performance Comparison of RRT-AB with MEA in a 2-Dimensional Environment

et al. 2019

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With the advent of mobile robots in commercial applications, the problem of path-planning has acquired significant attention from the research community. An optimal path for a mobile robot is measured by various factors such as path length, collision-free space, execution time, and the total number of turns. MEA* is an efficient variation of A* for optimal path-planning of mobile robots. RRT*-AB is a sampling-based planner with rapid convergence rate, and improved time and space requirements than other sampling-based methods such as RRT*. The purpose of this paper is the review and performance comparison of these planners based on metrics, i.e., path length, execution time, and memory requirements. All planners are tested in structured and complex unstructured environments cluttered with obstacles. Performance plots and statistical analysis have shown that MEA* requires less memory and computational time than other planners. These advantages of MEA* make it suitable for off-line applications using small robots with constrained power and memory resources. Moreover, performance plots of path length of MEA* is comparable to RRT*-AB with less execution time in the 2D environment. However, RRT*-AB will outperform MEA* in high-dimensional problems because of its inherited suitability for complex problems.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Path-Planning Performance Comparison of RRT-AB with MEA in a 2-Dimensional Environment

et al. 2019

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Fast‐RRT*: An Improved Motion Planner for Mobile Robot in Two‐Dimensional Space

Wang

et al. 2021

IEEJ Transactions Elec Engng

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Path planning for mobile robot aims to solve the problem of creating a collision‐free path from the start state to the goal state in the given space, which is a key supporting technology for unmanned work. In order to solve the problems of the asymptotic optimal rapidly extended random trees star (RRT*) algorithm, such as its slow convergence rate, the low efficiency of planning and the high cost of path, an improved motion planner (Fast‐RRT*) was proposed based on hybrid sampling strategy and choose parent based on backtracking. Firstly, the goal bias strategy and constraint sampling are combined in the sampling stage to reduce the blindness of sampling. Secondly, to obtain a path with lower cost than the RRT* algorithm, the ancestor of the nearest node is considered until the initial state in the process of choose parent for new node. To ensure the feasibility of the path, the path is smoothed by cubic B‐spline curve. The effectiveness of Fast‐RRT* algorithm was verified based on MATLAB and V‐rep platform. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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Bi‐RRT: An Improved Bidirectional RRT Path Planner for Robot in Two‐Dimensional Space

Wang,

Ju,

et al. 2023

IEEJ Transactions Elec Engng

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Path planning is one of the fundamental issues in the research field of robot. In the last decades, Rapidly‐exploring Random Tree star (RRT*) was one of the chosen path planner for robot with its probabilistically complete. In this paper, an improved bidirectional RRT* path planner for robot is proposed (Bi‐RRT*), which has planned path with safety and smoothly guarantee. The main contributions of this paper include, firstly, combining the backtracking idea of the Quick‐RRT algorithm with the greedy search strategy of the RRT‐Connect algorithm, which can reduce the path cost and improve the operation efficiency of the algorithm. Second, in order to further reduce the running time of the algorithm, a new collision detection algorithm based on cross‐product is proposed to replace the conventional collision detection strategy. To ensure the quality of the path, two post‐processing strategies, path optimization strategy and path smoothing strategy, are proposed. Path optimization strategy is based on triangle inequality, and two optimization methods are proposed to effectively reduce the path cost. The path smoothing strategy based on the Bezier curve improves the continuity of a smooth arc and is better applied to path smoothing. Simulation results for both virtual and real environments show the advantages of combining Quick‐RRT and RRT‐Connect and verify the effectiveness of the proposed collision detection algorithm. Compared with similar algorithms, the proposed Bi‐RRT algorithm has higher operational efficiency, smaller path cost, smoother planned paths, and improved path quality. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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Optimal path planning in cluttered environment using RRT*-AB

Cited by 73 publications

References 26 publications

A Path-Planning Performance Comparison of RRT-AB with MEA in a 2-Dimensional Environment

A Path-Planning Performance Comparison of RRT-AB with MEA in a 2-Dimensional Environment

Fast‐RRT*: An Improved Motion Planner for Mobile Robot in Two‐Dimensional Space

Bi‐RRT: An Improved Bidirectional RRT Path Planner for Robot in Two‐Dimensional Space

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Optimal path planning in cluttered environment using RRT*-AB

Cited by 73 publications

References 26 publications

A Path-Planning Performance Comparison of RRT*-AB with MEA* in a 2-Dimensional Environment

A Path-Planning Performance Comparison of RRT*-AB with MEA* in a 2-Dimensional Environment

Fast‐RRT*: An Improved Motion Planner for Mobile Robot in Two‐Dimensional Space

Bi‐RRT*: An Improved Bidirectional RRT* Path Planner for Robot in Two‐Dimensional Space

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A Path-Planning Performance Comparison of RRT-AB with MEA in a 2-Dimensional Environment

A Path-Planning Performance Comparison of RRT-AB with MEA in a 2-Dimensional Environment

Bi‐RRT: An Improved Bidirectional RRT Path Planner for Robot in Two‐Dimensional Space