We present a novel learning-based collision avoidance algorithm, CrowdSteer, for mobile robots operating in dense and crowded environments. Our approach is end-to-end and uses multiple perception sensors such as a 2-D lidar along with a depth camera to sense surrounding dynamic agents and compute collision-free velocities. Our training approach is based on the sim-to-real paradigm and uses high fidelity 3-D simulations of pedestrians and the environment to train a policy using Proximal Policy Optimization (PPO). We show that our learned navigation model is directly transferable to previously unseen virtual and dense real-world environments. We have integrated our algorithm with differential drive robots and evaluated its performance in narrow scenarios such as dense crowds, narrow corridors, T-junctions, L-junctions, etc. In practice, our approach can perform real-time collision avoidance and generate smooth trajectories in such complex scenarios. We also compare the performance with prior methods based on metrics such as trajectory length, mean time to goal, success rate, and smoothness and observe considerable improvement.

中文翻译：

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使用隐式多传感器融合和深度强化学习为密集人群中的移动机器人实时避免碰撞

我们提出了一种新颖的基于学习的碰撞避免算法 CrowdSteer，用于在密集和拥挤的环境中运行的移动机器人。我们的方法是端到端的，并使用多个感知传感器（例如二维激光雷达和深度相机）来感知周围的动态代理并计算无碰撞速度。我们的训练方法基于模拟到真实范式，并使用行人和环境的高保真 3-D 模拟来训练使用近端策略优化 (PPO) 的策略。我们展示了我们学习的导航模型可以直接转移到以前看不见的虚拟和密集的现实世界环境中。我们将我们的算法与差分驱动机器人相结合，并评估了其在密集人群、狭窄走廊、T 形路口、L 形路口等狭窄场景中的性能。在实践中，我们的方法可以在如此复杂的场景中执行实时碰撞避免并生成平滑的轨迹。我们还根据轨迹长度、平均目标时间、成功率和平滑度等指标将性能与先前方法进行了比较，并观察到显着的改进。