Collision Free Path Planning for Underwater Vehicles in Rapidly Changing Environments 2024 IEEE International Conference on Advanced Intelligent Mechatronics (AIM)

This paper presents an obstacle avoidance path planning algorithm designed to generate smooth paths for underwater robotic systems that operate in dynamic environments. Using the kinematics of the system, an initial path is generated which is further optimized considering the constraints of the system and the environment. The correlation between path states is embedded into a kernel used throughout the optimization. This produces a more informative optimization process that leads to changes in one state based on all other states. However, the use of this correlation between path states may lead to an exhaustive computational effort for highly dimensional systems. Therefore, the proposed approach, named AmaxGPMP, introduces a strategy capable of reducing the needed information to develop these kernels while accurately describing the correlation among states, hence decreasing the computation time. The proposed path planner was tested in simulation and experimentally on a BlueROV2 Heavy vehicle that was modified to enable autonomous capabilities. The results demonstrate the ability of AmaxGPMP to successfully generate smooth, feasible, and safe behaviors for autonomous underwater vehicles. Grant no. NA21OAR0110196

本论文提出一种面向在动态环境中作业的水下机器人系统的平滑路径生成避障路径规划算法。该算法依托系统运动学生成初始路径，并结合系统与环境约束对初始路径开展进一步优化。研究将路径状态间的相关性嵌入至优化全程所用的核函数（kernel）中，以此构建信息更为丰富的优化过程，使得任一状态的调整可基于其余所有状态完成。然而，针对高维度系统，路径状态相关性的运用可能引发过量计算开销。为此，本文提出名为AmaxGPMP的改进方案，该方案引入一种策略，可在精准描述状态间相关性的前提下，缩减生成此类核函数所需的信息量，进而缩短计算时长。所提路径规划器已通过仿真实验验证，并在经改造以具备自主作业能力的BlueROV2 Heavy水下机器人上开展实体测试。实验结果表明，AmaxGPMP可成功为自主水下机器人生成平滑、可行且安全的路径规划行为。本研究受资助编号NA21OAR0110196资助。