scholarly journals Collision-Free Path Planning Method for Robots Based on an Improved Rapidly-Exploring Random Tree Algorithm

2020 ◽  
Vol 10 (4) ◽  
pp. 1381 ◽  
Author(s):  
Xinda Wang ◽  
Xiao Luo ◽  
Baoling Han ◽  
Yuhan Chen ◽  
Guanhao Liang ◽  
...  
Keyword(s):  
Path Planning ◽  
Control Mechanism ◽  
Random Tree ◽  
High Dimensional ◽  
Control Factor ◽  
Sampling Area ◽  
Planning Algorithm ◽  
Search Speed ◽  
The Many ◽  
Path Planning Algorithm

Sampling-based methods are popular in the motion planning of robots, especially in high-dimensional spaces. Among the many such methods, the Rapidly-exploring Random Tree (RRT) algorithm has been widely used in multi-degree-of-freedom manipulators and has yielded good results. However, existing RRT planners have low exploration efficiency and slow convergence speed and have been unable to meet the requirements of the intelligence level in the Industry 4.0 mode. To solve these problems, a general autonomous path planning algorithm of Node Control (NC-RRT) is proposed in this paper based on the architecture of the RRT algorithm. Firstly, a method of gradually changing the sampling area is proposed to guide exploration, thereby effectively improving the search speed. In addition, the node control mechanism is introduced to constrain the extended nodes of the tree and thus reduce the extension of invalid nodes and extract boundary nodes (or near-boundary nodes). By changing the value of the node control factor, the random tree is prevented from falling into a so-called “local trap” phenomenon, and boundary nodes are selected as extended nodes. The proposed algorithm is simulated in different environments. Results reveal that the algorithm greatly reduces the invalid exploration in the configuration space and significantly improves planning efficiency. In addition, because this method can efficiently use boundary nodes, it has a stronger applicability to narrow environments compared with existing RRT algorithms and can effectively improve the success rate of exploration.

scholarly journals Intelligent Beetle Antennae Search for UAV Sensing and Avoidance of Obstacles

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1758 ◽  
Author(s):  
Qing Wu ◽  
Xudong Shen ◽  
Yuanzhe Jin ◽  
Zeyu Chen ◽  
Shuai Li ◽  
...  
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Heuristic Algorithm ◽  
Heuristic Algorithms ◽  
Search Range ◽  
Turning Angle ◽  
Planning Algorithm ◽  
Search Speed ◽  
Path Planner ◽  
Path Planning Algorithm

Based on a bio-heuristic algorithm, this paper proposes a novel path planner called obstacle avoidance beetle antennae search (OABAS) algorithm, which is applied to the global path planning of unmanned aerial vehicles (UAVs). Compared with the previous bio-heuristic algorithms, the algorithm proposed in this paper has advantages of a wide search range and breakneck search speed, which resolves the contradictory requirements of the high computational complexity of the bio-heuristic algorithm and real-time path planning of UAVs. Besides, the constraints used by the proposed algorithm satisfy various characteristics of the path, such as shorter path length, maximum allowed turning angle, and obstacle avoidance. Ignoring the z-axis optimization by combining with the minimum threat surface (MTS), the resultant path meets the requirements of efficiency and safety. The effectiveness of the algorithm is substantiated by applying the proposed path planning algorithm on the UAVs. Moreover, comparisons with other existing algorithms further demonstrate the superiority of the proposed OABAS algorithm.

scholarly journals Hierarchical Path-Planning for Mobile Robots Using a Skeletonization-Informed Rapidly Exploring Random Tree*

2020 ◽  
Vol 10 (21) ◽  
pp. 7846
Author(s):  
Hyejeong Ryu
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Search Algorithm ◽  
Random Tree ◽  
Graph Search ◽  
Complex Environments ◽  
Sampling Process ◽  
Planning Algorithm ◽  
Local Grid ◽  
Path Planning Algorithm

An efficient, hierarchical, two-dimensional (2D) path-planning method for large complex environments is presented in this paper. For mobile robots moving in 2D environments, conventional path-planning algorithms employ single-layered maps; the proposed approach engages in hierarchical inter- and intra-regional searches. A navigable graph of an environment is constructed using segmented local grid maps and safe junction nodes. An inter-regional path is obtained using the navigable graph and a graph-search algorithm. A skeletonization-informed rapidly exploring random tree* (SIRRT*) efficiently computes converged intra-regional paths for each map segment. The sampling process of the proposed hierarchical path-planning algorithm is locally conducted only in the start and goal regions, whereas the conventional path-planning should process the sampling over the entire environment. The entire path from the start position to the goal position can be achieved more quickly and more robustly using the hierarchical approach than the conventional single-layered method. The performance of the hierarchical path-planning is analyzed using a publicly available benchmark environment.

scholarly journals Improved RRT-Connect Algorithm Based on Triangular Inequality for Robot Path Planning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 333
Author(s):  
Jin-Gu Kang ◽  
Dong-Woo Lim ◽  
Yong-Sik Choi ◽  
Woo-Jin Jang ◽  
Jin-Woo Jung
Keyword(s):  
Path Planning ◽  
Path Length ◽  
Experimental Results ◽  
Random Tree ◽  
Robot Path Planning ◽  
Planning Time ◽  
Triangular Inequality ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Robot Path

This paper proposed a triangular inequality-based rewiring method for the rapidly exploring random tree (RRT)-Connect robot path-planning algorithm that guarantees the planning time compared to the RRT algorithm, to bring it closer to the optimum. To check the proposed algorithm’s performance, this paper compared the RRT and RRT-Connect algorithms in various environments through simulation. From these experimental results, the proposed algorithm shows both quicker planning time and shorter path length than the RRT algorithm and shorter path length than the RRT-Connect algorithm with a similar number of samples and planning time.

scholarly journals Improved RRT-Connect Algorithm Based on Triangular Inequality for Robot Path Planning

2020 ◽  
Author(s):  
Jin-Gu Kang ◽  
Dong-Woo Lim ◽  
Yong-Sik Choi ◽  
Woo-Jin Jang ◽  
Jin-Woo Jung
Keyword(s):  
Path Planning ◽  
Path Length ◽  
Experimental Results ◽  
Random Tree ◽  
Robot Path Planning ◽  
Planning Time ◽  
Triangular Inequality ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Robot Path

This paper proposed a triangular inequality-based rewiring method for the Rapidly exploring Random Tree (RRT)-Connect robot path-planning algorithm that guarantees the planning time compared to the RRT algorithm, to bring it closer to the optimum. To check the proposed algorithm’s performance, this paper compared the RRT and RRT-Connect algorithms in various environments through simulation. From these experimental results, the proposed algorithm shows both quicker planning time and shorter path length than the RRT algorithm and shorter path length than the RRT-Connect algorithm with a similar number of samples and planning time.

scholarly journals Hybrid Bidirectional Rapidly Exploring Random Tree Path Planning Algorithm with Reinforcement Learning

2021 ◽  
Vol 25 (1) ◽  
pp. 121-129
Author(s):  
Junkui Wang ◽  
◽  
Kaoru Hirota ◽  
Xiangdong Wu ◽  
Yaping Dai ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Optimal Path ◽  
Random Tree ◽  
Local Optimum ◽  
Local Optima ◽  
Exploration Process ◽  
Planning Algorithm ◽  
Alternative Approaches ◽  
Path Planning Algorithm

The randomness of path generation and slow convergence to the optimal path are two major problems in the current rapidly exploring random tree (RRT) path planning algorithm. Herein, a novel reinforcement-learning-based hybrid bidirectional rapidly exploring random tree (H-BRRT) is presented to solve these problems. To model the random exploration process, a target gravitational strategy is introduced. Reinforcement learning is applied to the improved target gravitational strategy using two operations: random exploration and target gravitational exploration. The algorithm is controlled to switch operations adaptively according to the accumulated performance. It not only improves the search efficiency, but also shortens the generated path after the proposed strategy is applied to a bidirectional rapidly exploring random tree (BRRT). In addition, to solve the problem of the traditional RRT continuously falling into the local optimum, an improved exploration strategy with collision weight is applied to the BRRT. Experimental results implemented in a robot operating system indicate that the proposed H-BRRT significantly outperforms alternative approaches such as the RRT and BRRT. The proposed algorithm enhances the capability of identifying unknown spaces and avoiding local optima.

scholarly journals Path Planning Algorithm Using the Hybridization of the Rapidly-exploring Random Tree and Ant Colony Systems

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Muhammad Aria Rajasa Pohan ◽  
Bambang Riyanto Trilaksono ◽  
Sigit Puji Santosa ◽  
Arief Syaichu Rohman
Keyword(s):  
Path Planning ◽  
Ant Colony ◽  
Random Tree ◽  
Planning Algorithm ◽  
Ant Colony Systems ◽  
Path Planning Algorithm
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