基于已知地形信息的海底机器人路径规划

路径规划是实现机器人智能化的重要组成部分,规划路径的优劣在很大程度上决定了机器人执行任务的效果. 传统的路径规划算法,例如基于图搜索的dijkstra算法和其改进后的A^*算法,以及基于采样的RRT （rapidlyexploring random tree） 算法和其改进后的 RRT^*算法,仅仅考虑了避障问题;基于插值曲线的算法可以产生较为光滑的轨迹;基于数值优化的算法可以将机器人速度、加速度等加入损失函数,通过优化求解,产生动力学特性较好的轨迹. 然而,面对当前越来越精确、丰富的先验地形信息,鲜有算法可以充分利用他们. 对此,基于海底数字高程地图（digital elevation map, DEM）,提出扩展A^*算法及FM （fast marching）算法改进算法,能够利用先验地形信息提高路径规划的效果. 通过仿真分析,对比3种算法:扩展A^*算法、TC FM （terrian cared fast marching）和TC FM^*算法,仿真结果表明,扩展A^*

Seabed robot path planning based on priori terrain information

Path planning is an important part of realizing robot autonomous movement. The planned path largely determine the performance of the robot in work. Traditional path planning algorithms, such as the dijkstra algorithm based on graph search and its improved version the $A^*$ algorithm, as well as the sampling-based RRT(rapidly-exploring random tree) algorithm and its improved version the RRT^*$ algorithm, only consider obstacle avoidance problems. Algorithms based on interpolation curves can generate smoother trajectories. Algorithms based on numerical optimization can add robot speed, acceleration, etc. to the loss function, which can generate trajectories with better dynamic properties through optimial solving. In current, topographic information are richer and more accurate, and few algorithms can make full use of them. Therefore, based on the digital elevation map(DEM) of the seabed, this paper proposes an extended $A^*$ algorithm and an improved FM(fast marching) algorithm, using prior geographic information to improve the effect of path planning. Through simulation analysis, three algorithms are compared: the extended $A^*$ algorithm, the TC FM(terrian cared fast marching) and the TCFM^*$ algorithm. Simulation results show that the extended $A^*$ algorithm solves faster, its the local planning ability is stronger, and the TCFM$ and TCFM^*$ algorithms find the path shorter and smoother.