论文标题
多机器人本地化和目标跟踪，并避免连接性维护和碰撞
Multi-Robot Localization and Target Tracking with Connectivity Maintenance and Collision Avoidance
论文作者
论文摘要
我们研究了需要一个机器人团队执行联合定位和目标跟踪任务的问题，同时确保团队的连接性和避免碰撞。该问题可以正式化为非线性，非凸优化程序，通常很难解决。为此，我们设计了一种两阶段的方法，该方法利用贪婪的算法来优化关节定位和目标跟踪性能，并应用控制屏障功能，以确保安全限制，即维持机器人团队的连通性并防止机器人间碰撞。模拟的凉亭实验验证了所提出的方法的有效性。我们将贪婪算法与非线性优化求解器和随机算法进行比较，从关节定位和跟踪质量以及计算时间方面。结果表明，我们的贪婪算法可实现高任务质量并有效运行。
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.
