论文标题
用于多代理目标监视的自由空间椭圆形图
Free-Space Ellipsoid Graphs for Multi-Agent Target Monitoring
论文作者
论文摘要
我们将新颖的框架应用于环境中的自由空间的分解和推理，以持续持续监测问题。我们的分解方法表示自由空间是与加权连接图相关的椭圆形集合。在高水平计划中用于推理连通性和距离的相同椭圆形可以用作模型预测控制算法中的状态约束，以实施无碰撞运动。这种结构允许在2D和3D环境中的分布式多代理任务中简化实现。我们说明了其为监控一组目标代理的追踪代理团队的有效性。我们的算法使用椭圆形分解作为跟踪剂的协调，路径计划和控制的原始性。使用四个跟踪代理监视障碍物富裕环境中15个动态目标的模拟证明了我们的算法的性能。
We apply a novel framework for decomposing and reasoning about free space in an environment to a multi-agent persistent monitoring problem. Our decomposition method represents free space as a collection of ellipsoids associated with a weighted connectivity graph. The same ellipsoids used for reasoning about connectivity and distance during high level planning can be used as state constraints in a Model Predictive Control algorithm to enforce collision-free motion. This structure allows for streamlined implementation in distributed multi-agent tasks in 2D and 3D environments. We illustrate its effectiveness for a team of tracking agents tasked with monitoring a group of target agents. Our algorithm uses the ellipsoid decomposition as a primitive for the coordination, path planning, and control of the tracking agents. Simulations with four tracking agents monitoring fifteen dynamic targets in obstacle-rich environments demonstrate the performance of our algorithm.
