This paper introduces a Nonlinear Model Predictive Control (N-MPC) framework exploiting a Deep Neural Network for processing onboard-captured depth images for collision avoidance in trajectory-tracking tasks with UAVs. The network is trained on simulated depth images to output a collision score for queried 3D points within the sensor field of view. Then, this network is translated into an algebraic symbolic equation and included in the N-MPC, explicitly constraining predicted positions to be collision-free throughout the receding horizon. The N-MPC achieves real time control of a UAV with a control frequency of 100Hz. The proposed framework is validated through statistical analysis of the collision classifier network, as well as Gazebo simulations and real experiments to assess the resulting capabilities of the N-MPC to effectively avoid collisions in cluttered environments. The associated code is released open-source along with the training images.

翻译：本文提出了一种非线性模型预测控制（N-MPC）框架，利用深度神经网络处理机载深度图像，实现无人机轨迹跟踪任务中的碰撞规避。该网络通过模拟深度图像训练，能够为传感器视场内查询的三维点输出碰撞得分。随后，该网络被转化为代数符号方程并集成至N-MPC中，显式约束预测位置在整个滚动时域内无碰撞。该N-MPC实现了100Hz控制频率下的无人机实时控制。通过碰撞分类网络的统计分析、Gazebo仿真实验以及实际环境测试，验证了所提框架在杂乱环境中有效规避碰撞的能力。相关代码及训练图像已开源发布。