YOPO-Rally: A Sim-to-Real Single-Stage Planner for Off-Road Terrain

Off-road navigation remains challenging for autonomous robots due to the harsh terrain and clustered obstacles. In this letter, we extend the YOPO (You Only Plan Once) end-to-end navigation framework to off-road environments, explicitly focusing on forest terrains, consisting of a high-performance, multi-sensor supported off-road simulator YOPO-Sim, a zero-shot transfer sim-to-real planner YOPO-Rally, and an MPC controller. Built on the Unity engine, the simulator can generate randomized forest environments and export depth images and point cloud maps for expert demonstrations, providing competitive performance with mainstream simulators. Terrain Traversability Analysis (TTA) processes cost maps, generating expert trajectories represented as non-uniform cubic Hermite curves. The planner integrates TTA and the pathfinding into a single neural network that inputs the depth image, current velocity, and the goal vector, and outputs multiple trajectory candidates with costs. The planner is trained by behavior cloning in the simulator and deployed directly into the real-world without fine-tuning. Finally, a series of simulated and real-world experiments is conducted to validate the performance of the proposed framework.

YOPOv2-Tracker: An End-to-End Agile Tracking and Navigation Framework from Perception to Action

Traditional target tracking pipelines including detection, mapping, navigation, and control are comprehensive but introduce high latency, limitting the agility of quadrotors. On the contrary, we follow the design principle of "less is more", striving to simplify the process while maintaining effectiveness. In this work, we propose an end-to-end agile tracking and navigation framework for quadrotors that directly maps the sensory observations to control commands. Importantly, leveraging the multimodal nature of navigation and detection tasks, our network maintains interpretability by explicitly integrating the independent modules of the traditional pipeline, rather than a crude action regression. In detail, we adopt a set of motion primitives as anchors to cover the searching space regarding the feasible region and potential target. Then we reformulate the trajectory optimization as regression of primitive offsets and associated costs considering the safety, smoothness, and other metrics. For tracking task, the trajectories are expected to approach the target and additional objectness scores are predicted. Subsequently, the predictions, after compensation for the estimated lumped disturbance, are transformed into thrust and attitude as control commands for swift response. During training, we seamlessly integrate traditional motion planning with deep learning by directly back-propagating the gradients of trajectory costs to the network, eliminating the need for expert demonstration in imitation learning and providing more direct guidance than reinforcement learning. Finally, we deploy the algorithm on a compact quadrotor and conduct real-world validations in both forest and building environments to demonstrate the efficiency of the proposed method.