论文标题
Adaptiveon：自适应户外本地导航方法，用于稳定和可靠的动作
AdaptiveON: Adaptive Outdoor Local Navigation Method For Stable and Reliable Actions
论文作者
论文摘要
我们提出了一种新颖的户外导航算法，以产生稳定有效的动作，以导航机器人以达到目标。我们使用多阶段的训练管道，并表明我们的方法产生了政策，从而在复杂的地形上导致稳定且可靠的机器人导航。根据近端政策优化（PPO）算法，我们开发了一种新颖的方法来实现多种功能来实现户外导航任务，即减轻机器人的漂移，使机器人在颠簸的地形上保持稳定，避免在山上攀爬，并避免了陡峭的山顶攀登，并避免了碰撞。我们的培训过程通过引入通用的环境和机器人参数来减轻现实（SIM到现实）差距，并在高保真统一模拟器中具有LIDAR感知的丰富特征。我们使用ClearPath Husky和Jackal机器人在模拟和现实世界环境中评估我们的方法。此外，我们将我们的方法与最先进的方法进行了比较，并观察到，在现实世界中，它在不均匀的地形上至少提高了30.7％的稳定性，将漂流的稳定性降低了8.08％，并将海拔变化降低了14.75％。
We present a novel outdoor navigation algorithm to generate stable and efficient actions to navigate a robot to reach a goal. We use a multi-stage training pipeline and show that our approach produces policies that result in stable and reliable robot navigation on complex terrains. Based on the Proximal Policy Optimization (PPO) algorithm, we developed a novel method to achieve multiple capabilities for outdoor navigation tasks, namely alleviating the robot's drifting, keeping the robot stable on bumpy terrains, avoiding climbing on hills with steep elevation changes, and avoiding collisions. Our training process mitigates the reality (sim-to-real) gap by introducing generalized environmental and robotic parameters and training with rich features of Lidar perception in a high-fidelity Unity simulator. We evaluate our method in both simulation and real world environments using Clearpath Husky and Jackal robots. Further, we compare our method against the state-of-the-art approaches and observe that, in the real world it improves stability by at least 30.7% on uneven terrains, reduces drifting by 8.08% and decreases the elevation changes by 14.75%.
