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Journal ArticleDOI

Multi-obstacle path planning and optimization for mobile robot

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TLDR
A multi-obstacle path planning and optimization method designed by the multi-objective D* Lite algorithm for distance and smoothness in order to get reasonable and optimized path in a complex environment.
Abstract
In the past few decades, many results have been achieved in the research of mobile robot path planning, and they have been applied in simple scenarios, such as factory AGV, bank guide robot. However, path planning in highly dense and complex scenarios has become an important challenge for applications. Robots face dense map and complex obstacles and hardly find out an optimal path within a reasonable period, such as unmanned vehicles in freight ports and rescue robots in earthquake environment. Therefore, a multi-obstacle path planning and optimization method is proposed. In order to simplify complex environmental obstacles, the obstacles will be divided into basis obstacles and extension obstacles. Firstly, the basis obstacles and their contour point sets are determined according to the starting point and goal point. Furthermore, the basis obstacles are optimized by convex hulls, and then the corresponding basis point set is obtained. Secondly, the extension obstacles are determined by the basis point set, starting point and goal point, and then the corresponding extension point set is generated. After that, a path planner is designed by the multi-objective D* Lite algorithm for distance and smoothness in order to get reasonable and optimized path in a complex environment. Moreover, the path is smoothed by cubic bezier curves to fit the kinematic model of the robot. Finally, The proposed method conduct comparative experiments with other algorithms to verify its accuracy and computational efficiency of planning in complex environments.

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Citations
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Journal ArticleDOI

Genetic Algorithm-Based Trajectory Optimization for Digital Twin Robots

TL;DR: The contribution of this work is the use of genetic algorithms for path planning of robots, which enables trajectory optimization of mobile robots by reducing the error in the movement trajectory of physical robots through the interaction of virtual and real data.
Journal ArticleDOI

An Improved Grey Wolf Optimization with Multi-Strategy Ensemble for Robot Path Planning

TL;DR: An improved grey wolf optimization (IGWO) is presented to ameliorate some disadvantages in avoiding prematurity and falling into local optimum, and the applicability of I GWO is verified by a robot global path planning problem, and simulation results demonstrate that IGWO can plan shorter and safer paths.
Journal ArticleDOI

A smooth path planning method for mobile robot using a BES-incorporated modified QPSO algorithm

TL;DR: In this article , a modified QPSO algorithm is proposed to solve the path planning problem with the benefit of a high-order continuous Bezier curve, which is more efficient compared to traditional algorithms.
Journal ArticleDOI

Full Coverage Path Planning Methods of Harvesting Robot with Multi-Objective Constraints

TL;DR: A full coverage path planning model for the irregular quadrilateral farmland is established, which significantly improve the adaptability of path planning algorithm to working environment and performance of the harvesting robot.
References
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Journal ArticleDOI

Sampling-based algorithms for optimal motion planning

TL;DR: In this paper, the authors studied the asymptotic behavior of the cost of the solution returned by stochastic sampling-based path planning algorithms as the number of samples increases.
Proceedings ArticleDOI

RRT-connect: An efficient approach to single-query path planning

TL;DR: A simple and efficient randomized algorithm is presented for solving single-query path planning problems in high-dimensional configuration spaces by incrementally building two rapidly-exploring random trees rooted at the start and the goal configurations.
Posted Content

Sampling-based Algorithms for Optimal Motion Planning

TL;DR: The main contribution of the paper is the introduction of new algorithms, namely, PRM and RRT*, which are provably asymptotically optimal, i.e. such that the cost of the returned solution converges almost surely to the optimum.
Book

The complexity of robot motion planning

TL;DR: John Canny resolves long-standing problems concerning the complexity of motion planning and, for the central problem of finding a collision free path for a jointed robot in the presence of obstacles, obtains exponential speedups over existing algorithms by applying high-powered new mathematical techniques.
Journal ArticleDOI

Fast replanning for navigation in unknown terrain

TL;DR: D/sup */ Lite is introduced, a heuristic search method that determines the same paths and thus moves the robot in the same way but is algorithmically different, and is at least as efficient as D/Sup */.
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