Autonomous driving paper index
MPC-Based Motion Planning and Tracking Control for Autonomous Underwater Vehicles
One-line summary
This paper proposes a motion planning and tracking control method based on model predictive control (MPC) for autonomous underwater vehicles (AUVs).
Engineering notes
Key topics: autonomous driving, motion planning, planning, control. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
This paper proposes a motion planning and tracking control method based on model predictive control (MPC) for autonomous underwater vehicles (AUVs). Traditional planning algorithms do not smoothly search for paths on grid maps and fail to satisfy AUV’s kinematic constraints. To address this, a new objective function with an obstacle avoidance function is designed to reduce excessive avoidance in traditional algorithms. The motion planning controller is designed to find a kinematically feasible obstacle avoidance trajectory by solving a nonlinear optimization problem with constraints based on AUV state information, obstacle information, and reference target information. The MPC trajectory tracking controller combines the planned trajectory to track it. Simulation results validate the effectiveness of the proposed method.
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