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.

A Continuous Local Motion Planning Framework for Unmanned Vehicles in Complex Environments

2011 ◽  
Vol 66 (4) ◽  
pp. 477-494 ◽  
Author(s):  
Andrew J. Berry ◽  
Jeremy Howitt ◽  
Da-Wei Gu ◽  
Ian Postlethwaite
Keyword(s):  
Motion Planning ◽  
Unmanned Vehicles ◽  
Complex Environments ◽  
Local Motion ◽  
Planning Framework

Decentralized Multi-Robot Motion Planning Applicable to Dynamic Environment

2019 ◽  
Author(s):  
Bin Wu ◽  
C. Steve Suh
Keyword(s):  
Motion Planning ◽  
Dynamic Environment ◽  
Sensor Data ◽  
Robot Motion ◽  
Robot Motion Planning ◽  
Large Numbers ◽  
Planning Framework ◽  
Local Path ◽  
Intelligent Robotics ◽  
Complete Observation

Abstract Multi-robots navigation in dynamic environment is a promising topic in intelligent robotics with motion planning being one of the fundamental problems. However, in practicel, multi-robots motion planning is challenging with traditional centralized approach since computational demand makes it less practical and robust for the motion planning of a large number of robots. In this paper, a decentralized distribute robots motion planning framework (DDRMPF) is discussed which addresses the specific issue. DDRMPF directly maps raw sensor data to steering command to generate optimal paths for each constituent robot. Unlike centralized method which needs a complete observation along with a center agent which processes heavy data collected from all the robots, DDRMPF allows each agent to generate an optimal local path needing only partial observation, thus rendering motion planning involving large numbers of robots more practical and robust. DDRMPF trains the policy for each robot in the complex and dynamic environment simultaneously based on the reinforcement algorithm.

Efficient and Safe Motion Planning for Quadrotors Based on Unconstrained Quadratic Programming

Robotica ◽  
2020 ◽  
pp. 1-17
Author(s):  
Yanhui Li ◽  
Chao Liu
Keyword(s):  
Path Planning ◽  
Quadratic Programming ◽  
Motion Planning ◽  
Computational Efficiency ◽  
Short Path ◽  
Trajectory Generation ◽  
Probabilistic Roadmap ◽  
Planning Framework ◽  
Unconstrained Quadratic Programming ◽  
Trajectory Problem

SUMMARY An autonomous motion planning framework is proposed, consisting of path planning and trajectory generation. Primarily, a spacious preferred probabilistic roadmap algorithm is utilized to search a safe and short path, considering kinematics and threats from obstacles. Subsequently, a minimum-snap and position-clearance polynomial trajectory problem is transformed into an unconstrained quadratic programming and solved in a two-step optimization. Finally, comparisons with other methods based on statistical simulations are implemented. The results show that the proposed method achieves computational efficiency and a safe trajectory.

scholarly journals Generation and Selection of Driver-behavior-based Transferable Motion Primitives

2020 ◽  
Author(s):  
Haijie Guan ◽  
Boyang Wang ◽  
Jiaming Wei ◽  
Yaomin Lu ◽  
Huiyan Chen ◽  
...  
Keyword(s):  
Motion Planning ◽  
Driver Behavior ◽  
Single Step ◽  
Driving Experience ◽  
Motion Primitives ◽  
Planning Framework ◽  
Planning Algorithm ◽  
Behavior Based ◽  
Motion Characteristics ◽  
Selection Of

Abstract In order to achieve the integration of driver experience and heterogeneous vehicle platform characteristics in the motion planning algorithm, based on the driver-behavior-based transferable motion primitives, a general motion planning framework for oine generation and online selection of motion primitives (MPs) is proposed. The optimal control theory is applied to solve the boundary value problems in the process of generating MPs, where the driver behaviors and the vehicle motion characteristics are integrated into the optimization in the form of constraints. Moreover, this paper proposes a layered, unequal-weighted MPs selection framework and utilizes the combination of environmental constraints, nonholonomic vehicle constraints, trajectory smoothness, and collision risk as the single-step extension evaluation index. The library of MPs generated oine demonstrates that the proposed generation method realizes the eective expansion of the MP types and achieves the diverse generation of MPs with various velocity attributes and platform types. We also present how the MP selection algorithm utilizes the unique MP library to achieve the online extension of MP sequences. The results show that the proposed motion planning framework can not only improve the eciency and rationality of the algorithm based on driving experience but also can transfer between heterogeneous vehicle platforms and highlight the unique motion characteristics of the platform.

scholarly journals Generation and Selection of Driver-Behavior-Based Transferable Motion Primitives

2021 ◽  
Author(s):  
Haijie Guan ◽  
Boyang Wang ◽  
Jiaming Wei ◽  
Yaomin Lu ◽  
Huiyan Chen ◽  
...  
Keyword(s):  
Motion Planning ◽  
Driver Behavior ◽  
Single Step ◽  
Driving Experience ◽  
Motion Primitives ◽  
Planning Framework ◽  
Planning Algorithm ◽  
Behavior Based ◽  
Motion Characteristics ◽  
Selection Of

Abstract In order to achieve the integration of driver experience and heterogeneous vehicle platform characteristics in the motion planning algorithm, based on the driver-behavior-based transferable motion primitives, a general motion planning framework for offline generation and online selection of motion primitives (MPs) is proposed. The optimal control theory is applied to solve the boundary value problems in the process of generating MPs, where the driver behaviors and the vehicle motion characteristics are integrated into the optimization in the form of constraints. Moreover, this paper proposes a layered, unequal-weighted MPs selection framework and utilizes the combination of environmental constraints, nonholonomic vehicle constraints, trajectory smoothness, and collision risk as the single-step extension evaluation index. The library of MPs generated offline demonstrates that the proposed generation method realizes the effective expansion of the MP types and achieves the diverse generation of MPs with various velocity attributes and platform types. We also present how the MP selection algorithm utilizes the unique MP library to achieve the online extension of MP sequences. The results show that the proposed motion planning framework can not only improve the efficiency and rationality of the algorithm based on driving experience but also can transfer between heterogeneous vehicle platforms and highlight the unique motion characteristics of the platform.

Herding by caging: a formation-based motion planning framework for guiding mobile agents

2021 ◽  
Author(s):  
Haoran Song ◽  
Anastasiia Varava ◽  
Oleksandr Kravchenko ◽  
Danica Kragic ◽  
Michael Yu Wang ◽  
...  
Keyword(s):  
Motion Planning ◽  
Mobile Agents ◽  
Planning Framework
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