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Decentralized MPC based Obstacle Avoidance for Multi-Robot Target Tracking Scenarios

机译:基于分散式MPC的多机器人目标跟踪场景下的避障

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摘要

In this work, we consider the problem of decentralized multi-robot target tracking and obstacle avoidance in dynamic environments. Each robot executes a local motion planning algorithm which is based on model predictive control (MPC). The planner is designed as a quadratic program, subject to constraints on robot dynamics and obstacle avoidance. Repulsive potential field functions are employed to avoid obstacles. The novelty of our approach lies in embedding these non-linear potential field functions as constraints within a convex optimization framework. Our method convexifies nonconvex constraints and dependencies, by replacing them as pre-computed external input forces in robot dynamics. The proposed algorithm additionally incorporates different methods to avoid field local minima problems associated with using potential field functions in planning. The motion planner does not enforce predefined trajectories or any formation geometry on the robots and is a comprehensive solution for cooperative obstacle avoidance in the context of multi-robot target tracking. We perform simulation studies for different scenarios to showcase the convergence and efficacy of the proposed algorithm.
机译:在这项工作中,我们认为动态环境中分散的多机器人目标跟踪和避免避免的问题。每个机器人执行基于模型预测控制(MPC)的本地运动规划算法。该计划者被设计为二次程序,受机器人动态和避免避免的限制。采用排斥潜在场功能来避免障碍物。我们的方法的新颖性在于将这些非线性潜在现场功能嵌入到凸优化框架内的约束。我们的方法通过在机器人动态中将其替换为预先计算的外部输入力来凸绝非耦合约束和依赖性。所提出的算法还包括不同的方法,以避免在规划中使用潜在的场函数相关的现场局部最小问题。运动规划器不在机器人上强制执行预定义的轨迹或任何形成几何形状,并且是在多机器人目标跟踪的背景下的合作障碍避免的全面解决方案。我们对不同场景进行仿真研究,以展示所提出的算法的收敛性和功效。

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