Autonomous mobile robots are usually faced with challenging situations when driving in complex environments. Namely, they have to recognize the static and dynamic obstacles, plan the driving path and execute their motion. For addressing the issue of perception and path planning, in this paper, we introduce OctoPath , which is an encoder-decoder deep neural network, trained in a self-supervised manner to predict the local optimal trajectory for the ego-vehicle. Using the discretization provided by a 3D octree environment model, our approach reformulates trajectory prediction as a classification problem with a configurable resolution. During training, OctoPath minimizes the error between the predicted and the manually driven trajectories in a given training dataset. This allows us to avoid the pitfall of regression-based trajectory estimation, in which there is an infinite state space for the output trajectory points. Environment sensing is performed using a 40-channel mechanical LiDAR sensor, fused with an inertial measurement unit and wheels odometry for state estimation. The experiments are performed both in simulation and real-life, using our own developed GridSim simulator and RovisLab's Autonomous Mobile Test Unit platform. We evaluate the predictions of OctoPath in different driving scenarios, both indoor and outdoor, while benchmarking our system against a baseline hybrid A-Star algorithm and a regression-based supervised learning method, as well as against a CNN learning-based optimal path planning method.

翻译：自动移动机器人通常在复杂环境下驾驶时面临挑战性的情况。 也就是说, 他们必须认识到静态和动态障碍, 规划驾驶路径并执行运动。 为了解决感知和路径规划问题, 在本文件中, 我们引入了“ 屋大帕特 ”, 这是一种基于回归的轨迹估计的陷阱, 其中有一个无限的状态空间用于输出轨迹点。 环境遥感使用一个由40个机械机械式LIDAR传感器进行, 与基于惯性测量单位和轮式轨道测量进行整合, 以进行国家估计。 实验在模拟和真实生活中进行, 使用我们自己开发的GridSim 和手动驱动的轨迹在特定培训数据集中的误差。 这使我们能够避免基于回归的轨迹估计的陷阱, 从而在输出轨迹点有无限的状态空间。 环境测用一个40个机械式LDAR传感器进行, 与基于惯性测量单位和轮式的轨迹测测算。 实验是在模拟和真实生活中进行, 使用我们自己开发的GridSimal- listal assimal ass assimal adbal adbal assilla adview Ad adview adview laview laview laview 和我们自己的系统, AS