用MPC优化无人机在复杂环境中的运动规划，更安全、更快、更省电。
Model Predictive Control for Optimal Motion Planning of Unmanned Aerial Vehicles
- 基于点云构建体素地图，用MPC动态优化局部轨迹。
- 轨迹更短平滑，速度更稳，能耗更低，实测优于先进方法。
- 适合复杂未知环境下的无人机自主飞行，如搜救与巡检。
无人机导航中的路径规划至关重要，需适应障碍物与复杂环境以抵达目标。本文提出一种针对未知复杂环境的最优运动规划方法：接收本地激光雷达点云数据，转换为体素网格表示周围环境；基于该网格生成导向目标的局部轨迹，并利用模型预测控制（MPC）进行优化，提升飞行安全性、速度与平滑性。优化过程定义多个代价函数与约束，考虑无人机动力学与实际需求。在多障碍物复杂环境中进行了大量仿真与对比实验，结果表明该方法生成的轨迹更短、更平滑，速度曲线更快且更稳定，同时具有更高的能效，适用于多种无人机应用场景。
原文摘要 · Abstract (English)
Motion planning is an essential process for the navigation of unmanned aerial vehicles (UAVs) where they need to adapt to obstacles and different structures of their operating environment to reach the goal. This paper presents an optimal motion planner for UAVs operating in unknown complex environments. The motion planner receives point cloud data from a local range sensor and then converts it into a voxel grid representing the surrounding environment. A local trajectory guiding the UAV to the goal is then generated based on the voxel grid. This trajectory is further optimized using model predictive control (MPC) to enhance the safety, speed, and smoothness of UAV operation. The optimization is carried out via the definition of several cost functions and constraints, taking into account the UAV's dynamics and requirements. A number of simulations and comparisons with a state-of-the-art method have been conducted in a complex environment with many obstacles to evaluate the performance of our method. The results show that our method provides not only shorter and smoother trajectories but also faster and more stable speed profiles. It is also energy efficient making it suitable for various UAV applications.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。