E³MoP: Efficient Motion Planning Based on Heuristic-Guided Motion Primitives Pruning and Path Optimization With Sparse-Banded Structure

2021 ◽  
pp. 1-14
Author(s):  
Jian Wen ◽  
Xuebo Zhang ◽  
Haiming Gao ◽  
Jing Yuan ◽  
Yongchun Fang
Keyword(s):  
Motion Planning ◽  
Path Optimization ◽  
Banded Structure ◽  
Motion Primitives

scholarly journals E3MoP: Efficient Motion Planning Based on Heuristic-Guided Motion Primitives Pruning and Path Optimization With Sparse-Banded Structure

2021 ◽  
Author(s):  
Jian Wen ◽  
Xuebo Zhang ◽  
Haiming Gao ◽  
Yongchun Fang
Keyword(s):  
Motion Planning ◽  
Computational Efficiency ◽  
Path Optimization ◽  
System Structure ◽  
Optimization Approach ◽  
Complex Environments ◽  
Planning Stage ◽  
Motion Primitives ◽  
Planning Framework ◽  
Local Path

To solve the autonomous navigation problem in complex environments, an efficient motion planning approach is newly presented in this paper. Considering the challenges from large-scale, partially unknown complex environments, a three-layer motion planning framework is elaborately designed, including global path planning, local path optimization, and time-optimal velocity planning. Compared with existing approaches, the novelty of this work is twofold: 1) a novel heuristic-guided pruning strategy of motion primitives is proposed and fully integrated into the state lattice-based global path planner to further improve the computational efficiency of graph search, and 2) a new soft-constrained local path optimization approach is proposed, wherein the sparse-banded system structure of the underlying optimization problem is fully exploited to efficiently solve the problem. We validate the safety, smoothness, flexibility, and efficiency of our approach in various complex simulation scenarios and challenging real-world tasks. It is shown that the computational efficiency is improved by 66.21\% in the global planning stage and the motion efficiency of the robot is improved by 22.87\% compared with the recent quintic B\'{e}zier curve-based state space sampling approach. We name the proposed motion planning framework E$ \mathbf{^3} $MoP, where the number 3 not only means our approach is a three-layer framework but also means the proposed approach is efficient in three stages.

scholarly journals EffMoP: Efficient Motion Planning Based on Heuristic-Guided Motion Primitives Pruning and Path Optimization With Sparse-Banded Structure

2021 ◽  
Author(s):  
Jian Wen ◽  
Xuebo Zhang ◽  
Haiming Gao ◽  
Jing Yuan ◽  
Yongchun Fang
Keyword(s):  
Motion Planning ◽  
Autonomous Navigation ◽  
Large Scale ◽  
Path Optimization ◽  
System Structure ◽  
Optimization Approach ◽  
Complex Environments ◽  
Optimal Velocity ◽  
Motion Primitives ◽  
Local Path

To solve the autonomous navigation problem in complex environments, an efficient motion planning approach called EffMoP is presented in this paper. Considering the challenges from large-scale, partially unknown complex environments, a three-layer motion planning framework is elaborately designed, including global path planning, local path optimization, and time-optimal velocity planning. Compared with existing approaches, the novelty of this work is twofold: 1) a novel heuristic-guided pruning strategy of motion primitives is proposed and fully integrated into the state lattice-based global path planner to further improve the computational efficiency of graph search, and 2) a new soft-constrained local path optimization approach is proposed, wherein the sparse-banded system structure of the underlying optimization problem is fully exploited to efficiently solve the problem. We validate the safety, smoothness, flexibility, and efficiency of EffMoP in various complex simulation scenarios and challenging real-world tasks.

scholarly journals EffMoP: Efficient Motion Planning Based on Heuristic-Guided Motion Primitives Pruning and Path Optimization With Sparse-Banded Structure

2021 ◽  
Author(s):  
Jian Wen ◽  
Xuebo Zhang ◽  
Haiming Gao ◽  
Jing Yuan ◽  
Yongchun Fang
Keyword(s):  
Motion Planning ◽  
Autonomous Navigation ◽  
Large Scale ◽  
Path Optimization ◽  
System Structure ◽  
Optimization Approach ◽  
Complex Environments ◽  
Optimal Velocity ◽  
Motion Primitives ◽  
Local Path

To solve the autonomous navigation problem in complex environments, an efficient motion planning approach called EffMoP is presented in this paper. Considering the challenges from large-scale, partially unknown complex environments, a three-layer motion planning framework is elaborately designed, including global path planning, local path optimization, and time-optimal velocity planning. Compared with existing approaches, the novelty of this work is twofold: 1) a novel heuristic-guided pruning strategy of motion primitives is proposed and fully integrated into the state lattice-based global path planner to further improve the computational efficiency of graph search, and 2) a new soft-constrained local path optimization approach is proposed, wherein the sparse-banded system structure of the underlying optimization problem is fully exploited to efficiently solve the problem. We validate the safety, smoothness, flexibility, and efficiency of EffMoP in various complex simulation scenarios and challenging real-world tasks.

scholarly journals EffMoP: Efficient Motion Planning Based on Heuristic-Guided Motion Primitives Pruning and Path Optimization With Sparse-Banded Structure

2021 ◽  
Author(s):  
Jian Wen ◽  
Xuebo Zhang ◽  
Haiming Gao ◽  
Jing Yuan ◽  
Yongchun Fang
Keyword(s):  
Motion Planning ◽  
Autonomous Navigation ◽  
Large Scale ◽  
Path Optimization ◽  
System Structure ◽  
Optimization Approach ◽  
Complex Environments ◽  
Optimal Velocity ◽  
Motion Primitives ◽  
Local Path

To solve the autonomous navigation problem in complex environments, an efficient motion planning approach called EffMoP is presented in this paper. Considering the challenges from large-scale, partially unknown complex environments, a three-layer motion planning framework is elaborately designed, including global path planning, local path optimization, and time-optimal velocity planning. Compared with existing approaches, the novelty of this work is twofold: 1) a novel heuristic-guided pruning strategy of motion primitives is proposed and fully integrated into the state lattice-based global path planner to further improve the computational efficiency of graph search, and 2) a new soft-constrained local path optimization approach is proposed, wherein the sparse-banded system structure of the underlying optimization problem is fully exploited to efficiently solve the problem. We validate the safety, smoothness, flexibility, and efficiency of EffMoP in various complex simulation scenarios and challenging real-world tasks.

Tool path optimization for robotic surface machining by using sampling-based motion planning algorithms

2020 ◽  
pp. 1-28
Author(s):  
Lu Lei ◽  
Jiong Zhang ◽  
Xiaoqing Tian ◽  
Jiang Han ◽  
Hao Wang
Keyword(s):  
Motion Planning ◽  
Configuration Space ◽  
Tool Path ◽  
Path Optimization ◽  
Machining Process ◽  
Axis Orientation ◽  
Surface Machining ◽  
Tool Path Optimization ◽  
Tool Axis ◽  
Planning Algorithms

Abstract This paper develops a tool path optimization method for robot surface machining by sampling-based motion planning algorithms. In the surface machining process, the tool-tip position needs to strictly follow the tool path curve and the posture of the tool axis should be limited in a certain range. But the industrial robot has at least six degrees of freedom (Dof) and has redundant Dofs for surface machining. Therefore, the tool motion of surface machining can be optimized using the redundant Dofs considering the tool path constraints and limits of the tool axis orientation. Due to the complexity of the problem, the sampling-based motion planning method has been chosen to find the solution, which randomly explores the configuration space of the robot and generates a discrete path of valid robot state. During the solving process, the joint space of the robot is chosen as the configuration space of the problem and the constraints for the tool-tip following requirements are in the operation space. Combined with general collision checking, the limited region of the tool axis vector is used to verify the state's validity of the configuration space. In the optimization process, the sum of path length of each joint of the robot is set as the optimization objective. The algorithm is developed based on the open motion planning library (OMPL) which contains the state-of-the-art sampling-based motion planners. Finally, two examples are used to demonstrate the effectiveness and optimality of the method.

Motion planning with adaptive motion primitives for modular robots

2015 ◽  
Vol 34 ◽  
pp. 678-692 ◽  
Author(s):  
Vojtěch Vonásek ◽  
Martin Saska ◽  
Karel Košnar ◽  
Libor Přeučil
Keyword(s):  
Motion Planning ◽  
Modular Robots ◽  
Motion Primitives ◽  
Adaptive Motion
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