scholarly journals Extended rapidly exploring random tree–based dynamic path planning and replanning for mobile robots

2018 ◽  
Vol 15 (3) ◽  
pp. 172988141877387 ◽  
Author(s):  
Devin Connell ◽  
Hung Manh La
Keyword(s):  
Path Planning ◽  
Search Tree ◽  
Random Tree ◽  
Complex Environments ◽  
Operational Environment ◽  
Moving Obstacles ◽  
Dynamic Path Planning ◽  
Current Location ◽  
Current Plan ◽  
Multi Robot

It is necessary for a mobile robot or even a multi-robot team to be able to efficiently plan a path from its starting or current location to a desired goal location. This is a trivial task when the environment is static. However, the operational environment of the robot is rarely static, and it often has many moving obstacles. The robot may encounter one, or many, of these unknown and unpredictable moving obstacles. The robot will need to decide how to proceed when one of these obstacles is obstructing its path. In this article, a new method of dynamic replanning is proposed to allow the robot to efficiently plan a path in such complex environments. Our proposed replanning method is based on an extended rapidly exploring random tree. The robot will modify its current plan when unknown random moving obstacles obstruct the path. We extend the proposed replanning method to multi-robot scenarios in which the ability to share path planning and search tree information is valuable. An efficient method of node sharing is proposed to allow the multi-robot team to quickly develop path plans. Various experimental results in both single and multi-robot scenarios show the effectiveness of the proposed methods.

scholarly journals Optimal randomized path planning for redundant manipulators based on Memory-Goal-Biasing

2018 ◽  
Vol 15 (4) ◽  
pp. 172988141878704 ◽  
Author(s):  
Dong Han ◽  
Hong Nie ◽  
Jinbao Chen ◽  
Meng Chen
Keyword(s):  
Path Planning ◽  
Search Tree ◽  
Random Tree ◽  
Complex Environments ◽  
Redundant Manipulator ◽  
Optimal Method ◽  
Key Points ◽  
Industrial Manipulators ◽  
Novel Method ◽  
Better Than

Planning path rapidly and optimally is one of the key technologies for industrial manipulators. A novel method based on Memory-Goal-Biasing–Rapidly-exploring Random Tree is proposed to solve high-dimensional manipulation planning more rapidly and optimally. The tree extension of Memory-Goal-Biasing–Rapidly-exploring Random Tree can be divided into random extension and goal extension. In the goal extension, the nodes extended to the goal are recorded in a memory, and then the node closest to the goal is selected in the search tree excepting the nodes in the memory for overcoming the local minimum. In order to check collisions efficiently, the manipulator is simplified into several key points, and the obstacle area is appropriately enlarged for safety. Taking the redundant manipulator of Baxter robot as an example, the proposed algorithm is verified through MoveIt! software. The results show that Memory-Goal-Biasing–Rapidly-exploring Random Tree only takes a few seconds for the path planning of the redundant manipulator in some complex environments, and within an acceptable time, its optimization performance is better than that of traditional optimal method in terms of the obtained path costs and the corresponding standard deviation.

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 Hybrid metaheuristic approach for robot path planning in dynamic environment

2021 ◽  
Vol 10 (4) ◽  
pp. 2152-2162
Author(s):  
Lina Basem Amar ◽  
Wesam M. Jasim
Keyword(s):  
Path Planning ◽  
Hybrid Algorithm ◽  
Optimization Problem ◽  
Dynamic Environment ◽  
Target Point ◽  
Complex Environments ◽  
Robot Path Planning ◽  
Moving Obstacles ◽  
Starting Point ◽  
Robot Path

Recently robots have gained great attention due to their ability to operate in dynamic and complex environments with moving obstacles. The path planning of a moving robot in a dynamic environment is to find the shortest and safe possible path from the starting point towards the desired target point. A dynamic environment is a robot's environment that consists of some static and moving obstacles. Therefore, this problem can be considered as an optimization problem and thus it is solved via optimization algorithms. In this paper, three approaches for determining the optimal pathway of a robot in a dynamic environment were proposed. These approaches are; the particle swarming optimization (PSO), ant colony optimization (ACO), and hybrid PSO and ACO. These used to carry out the path planning tasks effectively. A set of certain constraints must be met simultaneously to achieve the goals; the shortest path, the least time, and free from collisions. The results are calculated for the two algorithms separately and then that of the hybrid algorithm is calculated. The effectiveness and superiority of the hybrid algorithm were verified on both PSO and ACO algorithms.

An Adaptive Rapidly-Exploring Random Tree Algorithm for Assembly Path Planning in Complex Environments

2011 ◽  
Author(s):  
Wei Shang ◽  
Jian-hua Liu
Keyword(s):  
Path Planning ◽  
Performance Improvement ◽  
Random Tree ◽  
Complex Environments ◽  
Tree Algorithm ◽  
The Future ◽  
Constrained Environments ◽  
Significant Performance

We present a refined Rapidly-exploring Random Tree (RRT) algorithm for assembly path planning in complex environments. This algorithm adapts its expansion automatically to explore complex environments with narrow passages and cluttered obstacles more efficiently. In this algorithm, the nodes in the tree are classified by various criterions and different extending values are assigned on them indicating the nearby environment and are used to control the future expansion. A series of tree extending schemes are designed and selectively used based on the attributes of the node and the extending result in each step. We show that the algorithm becomes greedy in constrained environments and promising nodes have higher priority to extend than the non-promising ones. The algorithm is evaluated and applied in assembly path planning. The results show significant performance improvement over the standard RRT planner.

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