Practical deployments of coordinated fleets of mobile robots in different environments have revealed the benefits of maintaining small distances between robots, especially as they move at higher speeds. However, this is counter-intuitive in that as speed increases, reducing the amount of space between robots also reduces the time available to the robots to respond to sudden motion variations in surrounding robots. However, in certain examples, the benefits in performance due to traveling at closer distances can outweigh the potential instability issues, for instance, autonomous trucks on highways that optimize energy by vehicle ``drafting'' or smaller robots in cluttered environments that need to maintain close, line of sight communication, etc. To achieve this kind of closely coordinated fleet behavior, this work introduces a model predictive optimal control framework that directly takes non-linear dynamics of the vehicles in the fleet into account while planning motions for each robot. The robots are able to follow each other closely at high speeds by proactively making predictions and reactively biasing their responses based on state information from the adjacent robots. This control framework is naturally decentralized and, as such, is able to apply to an arbitrary number of robots without any additional computational burden. We show that our approach is able to achieve lower inter-robot distances at higher speeds compared to existing controllers. We demonstrate the success of our approach through simulated and hardware results on mobile ground robots.
翻译:在不同环境中协调部署的机动机器人车队的实际部署表明,保持机器人之间距离小是有好处的,特别是在机器人以更高速度移动时。然而,这是反直觉的,因为随着速度的增加,减少机器人之间的空间也减少了机器人对周围机器人突然运动变化作出反应的时间。不过,在某些例子中,更近距离旅行带来的性能效益可能超过潜在的不稳定问题,例如,高速公路上的自主卡车通过汽车“起草”或小型机器人在封闭的环境中优化能源,而这种机器人需要保持近距离、视线通信等。为了实现这种密切协调的机队行为,这项工作引入了一个模型预测最佳控制框架,直接考虑到机队中车辆的非线性动态,同时规划每个机器人的动作。机器人能够以高速度跟踪彼此,根据相邻的机器人提供的国家信息作出预测和被动地偏差反应。这个控制框架自然分散,因此,能够适用于任意的机队飞行速度,我们可以通过任何更高速度的移动式机器人方法,在更短的轨道上展示我们现有的硬性数字。我们通过任何更远的移动式的机械方法,在更短的轨道上可以实现更高的速度。