2020
DOI: 10.1177/0037549720930082
|View full text |Cite
|
Sign up to set email alerts
|

Development and analysis of a novel obstacle avoidance strategy for a multi-robot system inspired by the Bug-1 algorithm

Abstract: This paper addresses the development and implementation of an obstacle avoidance strategy for a multi-robot system operating in an unknown environment. This novel strategy is based on the conventional Bug-1 obstacle avoidance algorithm, which is a non-heuristic method for obstacle avoidance in an unknown environment. In the Bug-1 algorithm, a robot circumnavigates the obstacle to find the coordinates of the point, having minimum distance to the goal. In the case of the new strategy, two robots will ci… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1

Citation Types

0
3
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
4
1

Relationship

0
5

Authors

Journals

citations
Cited by 5 publications
(3 citation statements)
references
References 23 publications
0
3
0
Order By: Relevance
“…It is essential to decide on an appropriate critical metric for resolving certain driving and navigation issues in various driving situations. In their article 124 authors attempted to use an integrated algorithm for predicting obstacles and estimating the state of a self-driving vehicle. The authors claim in their article 125 that the TA system performance will be stimulated by a decision-making scheme that will define the vehicle's next plan of action.…”
Section: Threat Assessment (Ta)mentioning
confidence: 99%
See 1 more Smart Citation
“…It is essential to decide on an appropriate critical metric for resolving certain driving and navigation issues in various driving situations. In their article 124 authors attempted to use an integrated algorithm for predicting obstacles and estimating the state of a self-driving vehicle. The authors claim in their article 125 that the TA system performance will be stimulated by a decision-making scheme that will define the vehicle's next plan of action.…”
Section: Threat Assessment (Ta)mentioning
confidence: 99%
“…Using a rigorous mathematical framework, authors 142 formulate and discuss the optimization algorithm for the solution and examine the main details of the implementation of the multi-vehicle motion planning problem. In the article 124 , the authors propose a new way of thinking in which agents learn collision as a single agent and then avoid multiple collisions by reversing the trained policy. Major research using quadratic mixed-integer programming (MIQP) has been conducted 143 , with others implementing B-splines 144 , polynomials 145 , elastic bands 146 , and potential fields 147 , in route planning strategies 148 .…”
Section: Dynamic and Static Threat Assessment (Dsta)mentioning
confidence: 99%
“…(2) Compared with existing results of path planning algorithms [34,39,40], a shorter path and less time consumption are obtained using the proposed algorithm…”
Section: Introductionmentioning
confidence: 99%