Model predictive control (MPC) may provide local motion planning for mobile robotic platforms. The challenging aspect is the analytic representation of collision cost for the case when both the obstacle map and robot footprint are arbitrary. We propose a Neural Potential Field: a neural network model that returns a differentiable collision cost based on robot pose, obstacle map, and robot footprint. The differentiability of our model allows its usage within the MPC solver. It is computationally hard to solve problems with a very high number of parameters. Therefore, our architecture includes neural image encoders, which transform obstacle maps and robot footprints into embeddings, which reduce problem dimensionality by two orders of magnitude. The reference data for network training are generated based on algorithmic calculation of a signed distance function. Comparative experiments showed that the proposed approach is comparable with existing local planners: it provides trajectories with outperforming smoothness, comparable path length, and safe distance from obstacles. Experiment on Husky UGV mobile robot showed that our approach allows real-time and safe local planning. The code for our approach is presented at https://github.com/cog-isa/NPField together with demo video.

翻译：模型预测控制（MPC）可为移动机器人平台提供局部运动规划。其挑战性在于：当障碍物地图和机器人足迹均为任意形状时，碰撞代价的解析表达难以实现。我们提出神经势场：一种基于机器人位姿、障碍物地图和机器人足迹返回可微碰撞代价的神经网络模型。该模型的可微性使其能集成到MPC求解器中。由于高维参数问题计算开销极大，我们的架构包含神经图像编码器，将障碍物地图和机器人足迹转换为嵌入向量，使问题维度降低两个数量级。网络训练参考数据基于符号距离函数的算法计算生成。对比实验表明，该方法性能与现有局部规划器相当：生成的轨迹具有更优的平滑度、相当的路径长度和安全的障碍物距离。在Husky UGV移动机器人上的实验验证了该方法可实现实时安全局部规划。相关代码与演示视频详见https://github.com/cog-isa/NPField。