论文标题
机器人导航预期策略深入强化运动计划
Robot Navigation Anticipative Strategies in Deep Reinforcement Motion Planning
论文作者
论文摘要
机器人在动态的城市环境中的导航需要机器人详细的预期策略，以避免与动态物体（如自行车或行人）发生碰撞，并意识到人类。我们已经在运动计划中制定并分析了三种预期的运动策略，这些策略考虑到可以移动对象的未来运动，这些动作可能会移动到18 km/h。首先，我们使用了由深层确定性政策梯度（DDPG）培训和社会力量模型（SFM）产生的混合政策，并且我们在与许多行人的四个复杂地图场景中对其进行了对其进行了测试。其次，我们使用混合运动计划方法和带有动态窗口方法（NS-DWA）的ROS导航堆栈在现实生活实验中使用了这些预期策略。模拟和现实生活实验的结果显示在开放环境中以及在狭窄空间的混合情况下的结果非常好。
The navigation of robots in dynamic urban environments, requires elaborated anticipative strategies for the robot to avoid collisions with dynamic objects, like bicycles or pedestrians, and to be human aware. We have developed and analyzed three anticipative strategies in motion planning taking into account the future motion of the mobile objects that can move up to 18 km/h. First, we have used our hybrid policy resulting from a Deep Deterministic Policy Gradient (DDPG) training and the Social Force Model (SFM), and we have tested it in simulation in four complex map scenarios with many pedestrians. Second, we have used these anticipative strategies in real-life experiments using the hybrid motion planning method and the ROS Navigation Stack with Dynamic Windows Approach (NS-DWA). The results in simulations and real-life experiments show very good results in open environments and also in mixed scenarios with narrow spaces.
