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.

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 LMD-TShip*: Vision Based Large-Scale Maritime Ship Tracking Benchmark for Autonomous Navigation Applications

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Yunxiao Shan ◽  
Shanghua Liu ◽  
Yunfei Zhang ◽  
Min Jing ◽  
Huawei Xu
Keyword(s):  
Autonomous Navigation ◽  
Large Scale

ADD-RRV for motion planning in complex environments

Robotica ◽  
2021 ◽  
pp. 1-18
Author(s):  
Peng Cai ◽  
Xiaokui Yue ◽  
Hongwen Zhang
Keyword(s):  
Principal Component Analysis ◽  
Motion Planning ◽  
Configuration Space ◽  
Principal Component ◽  
Component Analysis ◽  
Planning Method ◽  
Complex Environments ◽  
Local Configuration ◽  
Narrow Passage

Abstract In this paper, we present a novel sampling-based motion planning method in various complex environments, especially with narrow passages. We use online the results of the planner in the ADD-RRT framework to identify the types of the local configuration space based on the principal component analysis (PCA). The identification result is then used to accelerate the expansion similar to RRV around obstacles and through narrow passages. We also propose a modified bridge test to identify the entrance of a narrow passage and boost samples inside it. We have compared our method with known motion planners in several scenarios through simulations. Our method shows the best performance across all the tested planners in the tested scenarios.

scholarly journals Purcell’s Three-Link Swimmer: Assessment of Geometry and Gaits for Optimal Displacement and Efficiency

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1088
Author(s):  
Cristina Nuevo-Gallardo ◽  
José Emilio Traver ◽  
Inés Tejado ◽  
Blas M. Vinagre
Keyword(s):  
Reynolds Number ◽  
Multiobjective Optimization ◽  
The Other ◽  
Optimal Method ◽  
Optimal Velocity ◽  
Motion Primitives ◽  
Motion Primitive ◽  
Control Objective ◽  
Optimal Efficiency ◽  
The One

This paper studies the displacement and efficiency of a Purcell’s three-link microswimmer in low Reynolds number regime, capable of moving by the implementation of a motion primitive or gait. An optimization is accomplished attending to the geometry of the swimmer and the motion primitives, considering the shape of the gait and its amplitude. The objective is to find the geometry of the swimmer, amplitude and shape of the gaits which make optimal the displacement and efficiency, in both an individual way and combined (the last case will be referred to as multiobjective optimization). Three traditional gaits are compared with two primitives proposed by the authors and other three gaits recently defined in the literature. Results demonstrate that the highest displacement is obtained by the Tam and Hosoi optimal velocity gait, which also achieves the best efficiency in terms of energy consumption. The rectilinear and Tam and Hosoi optimal efficiency gaits are the second optimum primitives. Regarding the multiobjective optimization and considering the two criteria with the same weight, the optimum gaits turn out to be the rectilinear and Tam and Hosoi optimal efficiency gaits. Thus, the conclusions of this study can help designers to select, on the one hand, the best swimmer geometry for a desired motion primitive and, on the other, the optimal method of motion for trajectory tracking for such a kind of Purcell’s swimmers depending on the desired control objective.

scholarly journals The Influence of Operating Strategies regarding an Energy Optimized Driving Style for Electrically Driven Railway Vehicles

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 583
Author(s):  
Lukas Pröhl ◽  
Harald Aschemann ◽  
Roberto Palacin
Keyword(s):  
Load Distribution ◽  
Optimization Approach ◽  
Total Energy Consumption ◽  
Driving Style ◽  
Optimal Velocity ◽  
Railway Vehicles ◽  
Operating Modes ◽  
Detailed Simulation ◽  
Operating Strategies ◽  
Optimal Driving

The aim of this paper is the optimization of velocity trajectories for electrical railway vehicles with the focus on total energy consumption. On the basis of four fundamental operating modes—acceleration, cruising, coasting, and braking—energy-optimal trajectories are determined by optimizing the sequence of the operating modes as well as the corresponding switching points. The optimization approach is carried out in two consecutive steps. The first step ensures compliance with the given timetable, regarding both time and position constraints. In the second step, the influence of different operating strategies, such as load distribution and the switch-off of traction components during low loads, are analyzed to investigate the characteristics of the energy-optimal velocity trajectory. A detailed simulation model has been developed to carry out the analysis, including an assessment of its capabilities and advantages. The results suggest that the application of load-distribution techniques, either by a switch-off of parallel traction units or by a load-distribution between active units, can affect the energy-optimal driving style.

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