Institute of Sensors, Signals and Systems, Heriot-Watt University, Edinburgh EH14 4AS, UK.
Centre for Artificial Intelligence, Robotics and Human-Machine Systems (IROHMS), Cardiff University, Cardiff CF24 3AA, UK.
Sensors (Basel). 2021 Feb 6;21(4):1152. doi: 10.3390/s21041152.
In this paper we present an extension to the hybrid A* (HA*) path planner. This extension allows autonomous underwater vehicle (AUVs) to plan paths in 3-dimensional (3D) environments. The proposed approach enables the robot to operate in a safe manner by accounting for the vehicle's motion constraints, thus avoiding collisions and ensuring that the calculated paths are feasible. Secondly, we propose an improvement for operations in unexplored or partially known environments by endowing the planner with a tree pruning procedure, which maintains a valid and feasible search-tree during operation. When the robot senses new obstacles in the environment that invalidate its current path, the planner prunes the tree of branches which collides with the environment. The path planning algorithm is then initialised with the pruned tree, enabling it to find a solution in a lower time than replanning from scratch. We present results obtained through simulation which show that HA* performs better in known underwater environments than compared algorithms in regards to planning time, path length and success rate. For unknown environments, we show that the tree pruning procedure reduces the total planning time needed in a variety of environments compared to running the full planning algorithm during replanning.
在本文中，我们提出了一种混合 A*（HA*）路径规划器的扩展。该扩展允许自主水下机器人（AUV）在三维（3D）环境中规划路径。所提出的方法通过考虑机器人的运动约束，使机器人能够安全地运行，从而避免碰撞并确保计算出的路径是可行的。其次，我们通过为规划器赋予树修剪过程，在未知或部分已知的环境中进行操作的改进，该过程在操作过程中保持有效的和可行的搜索树。当机器人在环境中检测到新的障碍物，使当前路径无效时，规划器会修剪与环境碰撞的分支树。然后，使用修剪后的树初始化路径规划算法，使其能够在比重新规划从头开始更短的时间内找到解决方案。我们通过仿真结果表明，HA* 在已知水下环境中的规划时间、路径长度和成功率方面优于比较算法。对于未知环境，我们表明与在重新规划时运行完整规划算法相比，树修剪过程在各种环境中减少了所需的总规划时间。
