Robot Motion Planning Method Based on Deterministic Sampling

2010 ◽  
Author(s):  
Yulan Hu ◽  
Qisong Zhang
Keyword(s):  
Motion Planning ◽  
Planning Method ◽  
Robot Motion ◽  
Robot Motion Planning ◽  
Deterministic Sampling

scholarly journals Motion Planning for Mobile Robot with Modified BIT* and MPC

2021 ◽  
Vol 11 (1) ◽  
pp. 426
Author(s):  
Puyong Xu ◽  
Ning Wang ◽  
Shi-Lu Dai ◽  
Lei Zuo
Keyword(s):  
Model Predictive Control ◽  
Mobile Robot ◽  
Motion Planning ◽  
Objective Function ◽  
Free Path ◽  
Predictive Control ◽  
Planning Method ◽  
Robot Motion ◽  
Robot Motion Planning ◽  
Simulation Results

In this paper, a mobile robot motion planning method with modified BIT* (batch informed trees) and MPC (Model Predictive Control) is presented. The conventional BIT* was modified here by integrating a stretch method that improves the path points connections, to get a collision-free path more quickly. After getting a reference path, the MPC method is employed to determine the motion at each moment with a given objective function. In the objective function, a repulsive function based on the direction and distance of the obstacles is introduced to avoid the robot being too close to the obstacle, so the safety can be ensured. Simulation results show the good navigation performance of the whole framework in different scenarios.

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

2021 ◽  
Vol 11 (22) ◽  
pp. 10773
Author(s):  
Jiabo Feng ◽  
Weijun Zhang
Keyword(s):  
Motion Planning ◽  
Planning Method ◽  
Robot Motion ◽  
Robot Motion Planning ◽  
Manual Work ◽  
Operation Efficiency ◽  
Planning Space ◽  
Dynamic Sampling ◽  
Low Uncertainty

The application of robots to replace manual work in live-line working scenes can effectively guarantee the safety of personnel. To improve the operation efficiency and reduce the difficulties in operating a live-line working robot, this paper proposes a multi-DOF robot motion planning method based on RRT and extended algorithms. The planning results of traditional RRT and extended algorithms are random, and obtaining sub-optimal results requires a lot of calculations. In this study, a sparse offline tree filling the planning space are generated offline through the growing–withering method. In the process of expanding the tree, by removing small branches, the tree can fully wiring in the planning space with a small number of nodes. Optimize wiring through a large number of offline calculations, which can improve the progressive optimality of the algorithm. Through dynamic sampling and pruning, the growth of trees in undesired areas is reduced and undesired planning results are avoided. Based on the offline tree, this article introduces the method of online motion planning. Experiments show that this method can quickly complete the robot motion planning and obtain efficient and low-uncertainty paths.

scholarly journals Learning Interaction-Aware Trajectory Predictions for Decentralized Multi-Robot Motion Planning in Dynamic Environments

2021 ◽  
Vol 6 (2) ◽  
pp. 2256-2263
Author(s):  
Hai Zhu ◽  
Francisco Martinez Claramunt ◽  
Bruno Brito ◽  
Javier Alonso-Mora
Keyword(s):  
Motion Planning ◽  
Dynamic Environments ◽  
Robot Motion ◽  
Robot Motion Planning ◽  
Multi Robot
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