Distributed swarm collision avoidance based on angular calculations

IF 3.7 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Autonomous Robots Pub Date : 2023-02-22 DOI:10.1007/s10514-022-10081-6

SeyedZahir Qazavi, Samaneh Hosseini Semnani

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引用次数: 0

Abstract

Collision avoidance is one of the most important topics in the robotics field. In this problem, the goal is to move the robots from initial locations to target locations such that they follow the shortest non-colliding paths in the shortest time and with the least amount of energy. Robot navigation among pedestrians is an example application of this problem which is the focus of this paper. This paper presents a distributed and real-time algorithm for solving collision avoidance problems in dense and complex 2D and 3D environments. This algorithm uses angular calculations to select the optimal direction for the movement of each robot and it has been shown that these separate calculations lead to a form of cooperative behavior among agents. We evaluated the proposed approach on various simulation and experimental scenarios and compared the results with ORCA one of the most important algorithms in this field. The results show that the proposed approach is at least 25% faster than ORCA while is also more reliable. The proposed method is shown to enable fully autonomous navigation of a swarm of Crazyflies.

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基于角度计算的分布式群体避碰算法

避免碰撞是机器人领域中最重要的课题之一。在这个问题中，目标是将机器人从初始位置移动到目标位置，以便它们在最短的时间内以最少的能量遵循最短的非碰撞路径。机器人在行人中的导航是该问题的一个应用实例，也是本文的重点。本文提出了一种分布式实时算法，用于解决密集复杂的二维和三维环境中的防撞问题。该算法使用角度计算来选择每个机器人运动的最佳方向，并且已经表明，这些单独的计算会导致代理之间的某种形式的协作行为。我们在各种模拟和实验场景中评估了所提出的方法，并将结果与该领域最重要的算法之一ORCA进行了比较。结果表明，所提出的方法比ORCA至少快25%，同时也更可靠。所提出的方法被证明能够实现Crazyflies群的完全自主导航。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Autonomous Robots 工程技术-机器人学

CiteScore

7.90

自引率

5.70%

发文量

审稿时长

3 months

期刊介绍： Autonomous Robots reports on the theory and applications of robotic systems capable of some degree of self-sufficiency. It features papers that include performance data on actual robots in the real world. Coverage includes: control of autonomous robots · real-time vision · autonomous wheeled and tracked vehicles · legged vehicles · computational architectures for autonomous systems · distributed architectures for learning, control and adaptation · studies of autonomous robot systems · sensor fusion · theory of autonomous systems · terrain mapping and recognition · self-calibration and self-repair for robots · self-reproducing intelligent structures · genetic algorithms as models for robot development. The focus is on the ability to move and be self-sufficient, not on whether the system is an imitation of biology. Of course, biological models for robotic systems are of major interest to the journal since living systems are prototypes for autonomous behavior.