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首页> 外文期刊>IEEE Transactions on Control Systems Technology >Energy-Optimal Motion Planning for Multiple Robotic Vehicles With Collision Avoidance
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Energy-Optimal Motion Planning for Multiple Robotic Vehicles With Collision Avoidance

机译:避免碰撞的多机器人车辆的能量最优运动计划

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We propose a numerical algorithm for multiple-vehicle motion planning that explicitly takes into account the vehicle dynamics, temporal and spatial specifications, and energy-related requirements. As a motivating example, we consider the case where a group of vehicles is tasked to reach a number of target points at the same time () without colliding among themselves and with obstacles, subject to the requirement that the overall energy required for vehicle motion be minimized. With the theoretical setup adopted, the at the planning level. This paper formulates the problem of multiple-vehicle motion planning in a rigorous mathematical setting, describes the optimization algorithm used to solve it, and discusses the key implementation details. The efficacy of the method is illustrated through numerical examples for the simultaneous arrival problem. The initial guess to start the optimization procedure is obtained from simple geometrical considerations, e.g., by joining the desired initial and final positions of the vehicles via straight lines. Even though the initial trajectories thus obtained may result in intervehicle and vehicle/obstacle collisions, we show that the optimization procedure that we employ in this paper will generate collision-free trajectories that also minimize the overall energy spent by each vehicle and meet the required temporal and spatial constraints. The method developed applies to a very general class of vehicles; however, for clarity of exposition, we adopt as an illustrative example the case of wheeled robots.
机译:我们提出了一种用于多车辆运动规划的数值算法,该算法明确考虑了车辆动力学,时空规格以及与能源相关的要求。作为一个激励示例,我们考虑以下情况:一组车辆被要求同时()到达多个目标点,而彼此之间没有碰撞并且没有障碍,但前提是车辆运动所需的总能量应为最小化。在采用理论设置的情况下,在计划级别。本文在严格的数学设置中阐述了多车辆运动计划的问题,描述了用于解决该问题的优化算法,并讨论了关键的实现细节。通过数值示例说明了同时到达问题的有效性。从简单的几何考虑出发,例如通过将车辆的期望的初始位置和最终位置通过直线连接起来,即可得出开始优化程序的初始猜测。即使由此获得的初始轨迹可能会导致车辆之间的碰撞以及车辆/障碍物的碰撞,我们仍然表明,本文中采用的优化程序将产生无碰撞的轨迹,这些轨迹还将使每辆车花费的总能量降至最低并满足所需的时间和空间限制。所开发的方法适用于非常普通的车辆类别。但是,为了说明清楚,我们以轮式机器人为例进行说明。

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