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Multi-Robot Localization and Target Tracking with Connectivity Maintenance and Collision Avoidance
Conference proceeding   Open access

Multi-Robot Localization and Target Tracking with Connectivity Maintenance and Collision Avoidance

Rahul Zahroof, Jiazhen Liu, Lifeng Zhou and Vijay Kumar
2023 American Control Conference (ACC), v 2023-, pp 1331-1338
31 May 2023
url
https://arxiv.org/abs/2210.03300View

Abstract

Collision avoidance Greedy algorithms Location awareness Sensors Target tracking Task analysis Uncertainty
We study the problem that requires a team of robots to perform joint localization and target tracking task while ensuring team connectivity and collision avoidance. The problem can be formalized as a nonlinear, non-convex optimization program, which is typically hard to solve. To this end, we design a two-staged approach that utilizes a greedy algorithm to optimize the joint localization and target tracking performance and applies control barrier functions to ensure safety constraints, i.e., maintaining connectivity of the robot team and preventing inter-robot collisions. Simulated Gazebo experiments verify the effectiveness of the proposed approach. We further compare our greedy algorithm to a non-linear optimization solver and a random algorithm, in terms of the joint localization and tracking quality as well as the computation time. The results demonstrate that our greedy algorithm achieves high task quality and runs efficiently.

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4 citations in Scopus

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Collaboration types
Domestic collaboration
Web of Science research areas
Automation & Control Systems
Computer Science, Interdisciplinary Applications
Engineering, Electrical & Electronic
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