Willms Allan R, Yang Simon X
Department of Mathematics and Statistics, University of Guelph, ON, Canada.
IEEE Trans Syst Man Cybern B Cybern. 2006 Aug;36(4):755-66. doi: 10.1109/tsmcb.2005.862724.
This paper presents a simple yet efficient dynamic-programming (DP) shortest path algorithm for real-time collision-free robot-path planning applicable to situations in which targets and barriers are permitted to move. The algorithm works in real time and requires no prior knowledge of target or barrier movements. In the case that the barriers are stationary, this paper proves that this algorithm always results in the robot catching the target, provided it moves at a greater speed than the target, and the dynamic-system update frequency is sufficiently large. Like most robot-path-planning approaches, the environment is represented by a topologically organized map. Each grid point on the map has only local connections to its neighboring grid points from which it receives information in real time. The information stored at each point is a current estimate of the distance to the nearest target and the neighbor from which this distance was determined. Updating the distance estimate at each grid point is done using only the information gathered from the point's neighbors, that is, each point can be considered an independent processor, and the order in which grid points are updated is not determined based on global knowledge of the current distances at each point or the previous history of each point. The robot path is determined in real time completely from the information at the robot's current grid-point location. The computational effort to update each point is minimal, allowing for rapid propagation of the distance information outward along the grid from the target locations. In the static situation, where both the targets and the barriers do not move, this algorithm is a DP solution to the shortest path problem, but is restricted by lack of global knowledge. In this case, this paper proves that the dynamic system converges in a small number of iterations to a state where the minimal distance to a target is recorded at each grid point and shows that this robot-path-planning algorithm can be made to always choose an optimal path. The effectiveness of this algorithm is demonstrated through a number of simulations.
本文提出了一种简单而高效的动态规划(DP)最短路径算法,用于实时无碰撞机器人路径规划,适用于目标和障碍物允许移动的情况。该算法实时运行,不需要目标或障碍物运动的先验知识。在障碍物静止的情况下,本文证明,只要机器人移动速度比目标快,且动态系统更新频率足够高,该算法总能使机器人追上目标。与大多数机器人路径规划方法一样,环境由拓扑组织的地图表示。地图上的每个网格点仅与其相邻网格点有局部连接,并实时从相邻点接收信息。存储在每个点的信息是到最近目标的当前距离估计以及确定该距离的相邻点。仅使用从该点的相邻点收集的信息来更新每个网格点的距离估计,也就是说,每个点都可被视为一个独立处理器,网格点更新的顺序不是基于每个点当前距离的全局知识或每个点的先前历史来确定的。机器人路径完全根据机器人当前网格点位置的信息实时确定。更新每个点的计算量最小,允许距离信息从目标位置沿网格向外快速传播。在目标和障碍物都不移动的静态情况下,该算法是最短路径问题的动态规划解决方案,但受到缺乏全局知识的限制。在这种情况下,本文证明动态系统在少数几次迭代中收敛到一种状态,即每个网格点记录到目标的最小距离,并表明这种机器人路径规划算法可以始终选择最优路径。通过大量模拟验证了该算法的有效性。