Hybrid Path Planning Based on Safe A* Algorithm and Adaptive Window Approach for Mobile Robot in Large-Scale Dynamic Environment

Zhong, Xiangnan; Tian, Jun; Hu, Huosheng; Peng, Xiafu

doi:10.1007/s10846-019-01112-z

Cited by 158 publications

(81 citation statements)

References 18 publications

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“…There are two kinds of path planning algorithms: traditional search algorithms and intelligent evolution algorithms. Traditional search algorithms include the Floyd algorithm [ 6 ], Dijkstra algorithm [ 7 ], artificial potential field method [ 8 ], and A∗ algorithm [ 9 ]. Floyd algorithm [ 6 ] is easy to understand and simple to design, but Floyd algorithm is not suitable for a large amount of data because of its high time complexity.…”

Section: Introductionmentioning

confidence: 99%

“…The artificial potential field method [ 8 ] has a simple structure and has been widely used in real-time obstacle avoidance and smooth trajectory control, but the artificial potential field method has the problem of unreachable target near the obstacle and local minimum. A ∗ algorithm [ 9 ] is suitable for simple maps. In complex maps, the program running time of A ∗ algorithm will increase rapidly.…”

Section: Introductionmentioning

confidence: 99%

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The Application of an Adaptive Genetic Algorithm Based on Collision Detection in Path Planning of Mobile Robots

Hao

Zhao

Wang

et al. 2021

Computational Intelligence and Neuroscience

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An adaptive genetic algorithm based on collision detection (AGACD) is proposed to solve the problems of the basic genetic algorithm in the field of path planning, such as low convergence path quality, many iterations required for convergence, and easily falling into the local optimal solution. First, this paper introduces the Delphi weight method to evaluate the weight of path length, path smoothness, and path safety in the fitness function, and a collision detection method is proposed to detect whether the planned path collides with obstacles. Then, the population initialization process is improved to reduce the program running time. After comprehensively considering the population diversity and the number of algorithm iterations, the traditional crossover operator and mutation operator are improved, and the adaptive crossover operator and adaptive mutation operator are proposed to avoid the local optimal solution. Finally, an optimization operator is proposed to improve the quality of convergent individuals through the second optimization of convergent individuals. The simulation results show that the adaptive genetic algorithm based on collision detection is not only suitable for simulation maps with various sizes and obstacle distributions but also has excellent performance, such as greatly reducing the running time of the algorithm program, and the adaptive genetic algorithm based on collision detection can effectively solve the problems of the basic genetic algorithm.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Application of an Adaptive Genetic Algorithm Based on Collision Detection in Path Planning of Mobile Robots

Hao

Zhao

Wang

et al. 2021

Computational Intelligence and Neuroscience

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show abstract

“…The method was first proposed by Dijkstra in [7], and several variants have since been reported. A* [13,37,10] is a heuristic method that reduces the computational cost by introducing an estimation of the future cost. Considering that the grid representation of maps constrains the possible headings of the paths, the paths formed by grid edges can be suboptimal.…”

Section: Introductionmentioning

confidence: 99%

RimJump*: Tangent Based Shortest Path Planning for Cluttered 3D Environment

Zhang¹

2021

Preprint

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Path planning in 3D environment is a fundamental research area for robots and autonomous vehicles. Based on the principle ``the shortest path consists of tangents'', RimJump* is proposed as a tangent-based path planning method suitable for finding the shortest path (both off-ground and on-ground) in 3D space (e.g., octomap and point cloud) for mobile platform to follow. RimJump* searches the tangent graph in the form of a path tree and considers the geometrical properties of the locally shortest path. Therefore, the method can provide all of the locally shortest paths that connect the starting point and the target, including the globally shortest path. And the time cost of RimJump* is insensitive to map scale increases in comparison to methods that search the whole passable space rather than the surface of the obstacle, e.g., Dijkstra and A*. In the Results, RimJump* is compared with other methods in terms of path length and time cost.

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“…Then, once a path is specified, another process is executed to calculate the motion plan using the kinodynamic properties of the robot. Usually, for a path planning process, the robot is considered as a point in the configuration space (C space ), which is the space generated by all feasible positions that it can reach [3,5,6]. Then, C space is divided into free configuration space (C free ) for valid positions and obstacles space (C obs ) for all forbidden configurations.…”

Section: Introductionmentioning

confidence: 99%

Multiple-Target Homotopic Quasi-Complete Path Planning Method for Mobile Robot Using a Piecewise Linear Approach

Diaz-Arango

Vázquez-Leal

Hernandez-Martinez

et al. 2020

Sensors

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The ability to plan a multiple-target path that goes through places considered important is desirable for autonomous mobile robots that perform tasks in industrial environments. This characteristic is necessary for inspection robots that monitor the critical conditions of sectors in thermal, nuclear, and hydropower plants. This ability is also useful for applications such as service at home, victim rescue, museum guidance, land mine detection, and so forth. Multiple-target collision-free path planning is a topic that has not been very studied because of the complexity that it implies. Usually, this issue is left in second place because, commonly, it is solved by segmentation using the point-to-point strategy. Nevertheless, this approach exhibits a poor performance, in terms of path length, due to unnecessary turnings and redundant segments present in the found path. In this paper, a multiple-target method based on homotopy continuation capable to calculate a collision-free path in a single execution for complex environments is presented. This method exhibits a better performance, both in speed and efficiency, and robustness compared to the original Homotopic Path Planning Method (HPPM). Among the new schemes that improve their performance are the Double Spherical Tracking (DST), the dummy obstacle scheme, and a systematic criterion to a selection of repulsion parameter. The case studies show its effectiveness to find a solution path for office-like environments in just a few milliseconds, even if they have narrow corridors and hundreds of obstacles. Additionally, a comparison between the proposed method and sampling-based planning algorithms (SBP) with the best performance is presented. Furthermore, the results of case studies show that the proposed method exhibits a better performance than SBP algorithms for execution time, memory, and in some cases path length metrics. Finally, to validate the feasibility of the paths calculated by the proposed planner; two simulations using the pure-pursuit controlled and differential drive robot model contained in the Robotics System Toolbox of MATLAB are presented.

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Hybrid Path Planning Based on Safe A* Algorithm and Adaptive Window Approach for Mobile Robot in Large-Scale Dynamic Environment

Cited by 158 publications

References 18 publications

The Application of an Adaptive Genetic Algorithm Based on Collision Detection in Path Planning of Mobile Robots

The Application of an Adaptive Genetic Algorithm Based on Collision Detection in Path Planning of Mobile Robots

RimJump*: Tangent Based Shortest Path Planning for Cluttered 3D Environment

Multiple-Target Homotopic Quasi-Complete Path Planning Method for Mobile Robot Using a Piecewise Linear Approach

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