Path regeneration decisions in a dynamic environment

Han, Jihee; Seo, Yoonho

doi:10.1016/j.ins.2018.03.035

Cited by 12 publications

(4 citation statements)

References 21 publications

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“…To evaluate the performance of the proposed method, comparison was made with other popular related methods including RRT-based [35], PRM-based and A * -based [17] techniques. Most path replanning algorithms usually consider either replanning at incremental distance or time interval, or execute path replanning when an obstacle is detected using the entire map [27]. Hence, the evaluation of the proposed method also consider VD based on incremental (VD-INC) and full map (VD-FM) in the comparison.…”

Section: Comparison Of Path Planning Methodsmentioning

confidence: 99%

“…Generally, these path replanning methods can be categorized into two: incremental replanning and replanning that considers the entire environment (full map) of the robot when collision threat is detected [27]. The incremental replanning approaches compute new path for navigation at every incremental distance or time during navigation of the robot.…”

Section: Introductionmentioning

confidence: 99%

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Mobile Robot Path Planning in Dynamic Environment Using Voronoi Diagram and Computation Geometry Technique

et al. 2019

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This paper presents a novel path planning method for mobile robots in complex and dynamic environments using Voronoi diagram (VD) and computation geometry technique (CGT) termed, VD-CGT. An algorithm to categorize moving obstacles based on their positions, velocities, distances, and directions to ascertain their collision threat level and possible replanning decision is introduced. The initial path computation is done using morphological dilation, VD, A-star, and cubic spline algorithms. Instead of considering the entire map of the environment, CGT is used to compute a small rectangular region estimated to enclose a detected collision-threat obstacle and the current position of the robot. The roadmap is computed in the geometrical shape using VD and nodes are added to the initial roadmap nodes to compute a new path for replanning. To avoid increasing time and space requirements, these nodes are discarded before subsequent replanning is done. The results indicate better path replanning performance in complex and dynamic environments in terms of success path computation rate, path cost, time, and the number of replanning computations compared with other five popular related path planning approaches. The proposed method is efficient, and it computes safe and shortest replan path to goal with low computation time requirement. Unnecessary replanning computations are avoided which aid in reducing time and distance to get to the goal. With the performance results, the proposed method is a promising method for achieving safe, less path cost, and time in path replanning computations in complex and dynamic environments.INDEX TERMS Mobile robot, motion planning, path planning, unmanned autonomous vehicles, Voronoi diagram.

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Section: Comparison Of Path Planning Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mobile Robot Path Planning in Dynamic Environment Using Voronoi Diagram and Computation Geometry Technique

et al. 2019

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“…Nowadays, the importance of the route optimisation in the dynamic environment is on the increase. Han et al in [19] considered the problem of the path replanning in a dynamic environment. This problem is similar to the one considered in this paper, but refers to the obstacles moving on the triangular grid instead of the road traffic.…”

Section: Transit-based Routingmentioning

confidence: 99%

Intelligent route planning system based on interval computing

Chmiel

Skalna

Jędrusik

2018

Multimed Tools Appl

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We investigate the problem of vehicle route planning in a dynamic environment. In order to better reflect real-life situations, we assume that travel times are not known exactly, but bounded from below and from above, i.e., they are given as interval quantities. Accordingly, we develop algorithms for effective route replanning in a highly dynamic road network environment that combines traffic image processing with interval data for dynamic path optimisation. The developed algorithms are integrated into a larger system for traffic management. The efficiency of the proposed algorithms and their ability to support the dynamics of road traffic is verified using real data. The experimental research was also conducted using the microscopic, time-discrete, space-continuous traffic simulator.

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“…In relation to path re-planning, various researchers have conducted several investigations and studies. The path-planning techniques in unknown and dynamic environments are provided in [6][7][8][9], and the path re-planning techniques in urgent scenarios have been researched in [10,11]. In the last century, for optimization purposes, numerous evolutionary algorithm (EA)-based heuristic algorithms have been proposed [12], including ant colony optimization (ACO) [13], artificial bee colony (ABC) [14], differential evolution (DE) [15], fireworks algorithm (FWA) [16], genetic algorithm (GA) [17], and PSO [18].…”

Section: Introductionmentioning

confidence: 99%

An Optimized Energy and Time Constraints-Based Path Planning for the Navigation of Mobile Robots Using an Intelligent Particle Swarm Optimization Technique

Raj,

Kos

2023

Applied Sciences

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Mobile robots (MRs) typically require running for many hours on one charge of the battery. Electric autonomous mobile robots (AMRs) have become increasingly common in the manufacturing process in the last few years. MRs must often complete difficult assignments while gathering information across an unknown area involving energy constraints and time-sensitive preferences. This paper estimates the information collection assignment for surveillance as a multi-objective optimization dilemma with both energy and time constraints. In this study, three main objectives during acquiring data are taken into consideration, including the greatest quantity of data acquired for surveillance, following a path where obstacles are least likely to be experienced, and traveling the smallest feasible path. To obtain the optimal path for an MR by addressing the presented issue, this approach presents an intelligent particle swarm optimization (PSO) technique that determines fitness value by simplifying the optimization task for achieving the shortest path for MR navigation. It allows particles to execute variable operations while maintaining most of the prior search information. The findings of the simulation show that this technique of the PSO algorithm can realize swift convergence and high accuracy when compared with different benchmark functions derived for PSO. A comparative discussion on various energy-efficient navigation techniques for MRs is also provided. Lastly, this study describes the possible future research directions.

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Path regeneration decisions in a dynamic environment

Cited by 12 publications

References 21 publications

Mobile Robot Path Planning in Dynamic Environment Using Voronoi Diagram and Computation Geometry Technique

Mobile Robot Path Planning in Dynamic Environment Using Voronoi Diagram and Computation Geometry Technique

Intelligent route planning system based on interval computing

An Optimized Energy and Time Constraints-Based Path Planning for the Navigation of Mobile Robots Using an Intelligent Particle Swarm Optimization Technique

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