Efficient use of the space in an elevator is very necessary for a service robot, due to the need for reducing the amount of time caused by waiting for the next elevator. To provide a solution for this, we propose a hybrid approach that combines reinforcement learning (RL) with voice interaction for robot navigation in the scene of entering the elevator. RL provides robots with a high exploration ability to find a new clear path to enter the elevator compared to traditional navigation methods such as Optimal Reciprocal Collision Avoidance (ORCA). The proposed method allows the robot to take an active clear path action towards the elevator whilst a crowd of people stands at the entrance of the elevator wherein there are still lots of space. This is done by embedding a clear path action (voice prompt) into the RL framework, and the proposed navigation policy helps the robot to finish tasks efficiently and safely. Our model approach provides a great improvement in the success rate and reward of entering the elevator compared to state-of-the-art navigation policies without active clear path operation.
翻译:高效使用电梯空间对于服务机器人来说是非常必要的,因为需要减少等待下一电梯的时间。为了提供解决办法,我们提议采用混合方法,将强化学习(RL)与进入电梯现场的机器人导航语音互动结合起来。RL为机器人提供了高探索能力,以找到进入电梯的新清晰路径,而与最佳对等协作避免等传统导航方法相比,这是非常必要的。拟议方法允许机器人在仍然有大片空间的电梯入口处人群站在电梯入口处时,采取积极的明确路径行动,通过将清晰路径行动(语音提示)嵌入电梯框架,以及拟议导航政策帮助机器人高效、安全地完成任务。我们的模式方法大大改进了进入电梯的成功率和奖赏,而没有积极的清晰路径操作。