This paper introduces a control architecture for real-time and onboard control of Unmanned Aerial Vehicles (UAVs) in environments with obstacles using the Model Predictive Path Integral (MPPI) methodology. MPPI allows the use of the full nonlinear model of UAV dynamics and a more general cost function at the cost of a high computational demand. To run the controller in real-time, the sampling-based optimization is performed in parallel on a graphics processing unit onboard the UAV. We propose an approach to the simulation of the nonlinear system which respects low-level constraints, while also able to dynamically handle obstacle avoidance, and prove that our methods are able to run in real-time without the need for external computers. The MPPI controller is compared to MPC and SE(3) controllers on the reference tracking task, showing a comparable performance. We demonstrate the viability of the proposed method in multiple simulation and real-world experiments, tracking a reference at up to 44 km/h and acceleration close to 20 m/s^2, while still being able to avoid obstacles. To the best of our knowledge, this is the first method to demonstrate an MPPI-based approach in real flight.

翻译：本文提出了一种利用模型预测路径积分（MPPI）方法，在存在障碍物的环境中对无人机（UAV）进行实时机载控制的架构。MPPI允许使用完整的无人机非线性动力学模型和更通用的代价函数，但其代价是较高的计算需求。为了使控制器能够实时运行，基于采样的优化在无人机机载的图形处理器上并行执行。我们提出了一种模拟非线性系统的方法，该方法既考虑了底层约束，又能动态处理避障问题，并证明了我们的方法无需外部计算机即可实时运行。在参考轨迹跟踪任务中，将MPPI控制器与MPC和SE(3)控制器进行了比较，结果显示其性能相当。我们通过多次仿真和真实世界实验验证了所提方法的可行性，在跟踪速度高达44公里/小时、加速度接近20米/秒²的参考轨迹时，仍能成功避开障碍物。据我们所知，这是首个在真实飞行中演示基于MPPI方法的工作。