论文标题
一种基于图的方法，用于在多边形环境中生成能量最佳的机器人轨迹
A Graph-Based Approach to Generate Energy-Optimal Robot Trajectories in Polygonal Environments
论文作者
论文摘要
随着机器人系统继续解决物流，流动性，制造业和灾难响应等领域的新兴问题，迅速产生安全和节能的轨迹越来越重要。在本文中，我们提出了一种新的方法，可以通过包含多边形障碍的混乱环境来计划能量最佳的轨迹。特别是，我们开发了一种方法来快速为双集成剂系统生成最佳轨迹，并且我们表明，最佳路径规划减少到整数程序。为了找到有效的解决方案，我们提供了一个距离信息的前缀搜索，以有效地为大型环境生成最佳轨迹。我们证明了我们的方法在与路径长度方面相匹配的RRT*和概率路线图的同时，在能源成本和计算时间方面的表现都超过了一个数量级。我们还证明，我们的方法在使用Crazyflie四巨头的实验中产生了可实施的轨迹。
As robotic systems continue to address emerging issues in areas such as logistics, mobility, manufacturing, and disaster response, it is increasingly important to rapidly generate safe and energy-efficient trajectories. In this article, we present a new approach to plan energy-optimal trajectories through cluttered environments containing polygonal obstacles. In particular, we develop a method to quickly generate optimal trajectories for a double-integrator system, and we show that optimal path planning reduces to an integer program. To find an efficient solution, we present a distance-informed prefix search to efficiently generate optimal trajectories for a large class of environments. We demonstrate that our approach, while matching the performance of RRT* and Probabilistic Road Maps in terms of path length, outperforms both in terms of energy cost and computational time by up to an order of magnitude. We also demonstrate that our approach yields implementable trajectories in an experiment with a Crazyflie quadrotor.
