Unsupervised Multiple Proactive Behavior Learning of Mobile Robots for Smooth and Safe Navigation

While different control approaches have been developed for smooth and safe navigation, they are limited by the needs for model-based assumptions, true training target/reward function, and/or large sample data. To overcome these limitations, this study proposes a model-free neural control architecture with a generic plug-and-play online Multiple Proactive Behavior Learning (MPL) module. The MPL adapts robot neural control policy in an online unsupervised manner with small sample data by correlating its sensory inputs to a local planner command. As a result, it allows a mobile robot to autonomously and quickly learn and balance various proactive behaviors related to smooth motion and collision avoidance. It also compensates for the limited planning update rates and the planning model mismatch of an arbitrary local motion planner. Compared with existing control approaches without the MPL, our control architecture with the MPL leads to (1) a 10% improvement in the smoothness of robot motion and 30% fewer collisions in a narrow static environment, and (2) trading motion smoothness for up to 70% fewer collisions in an unknown dynamic environment. Taken together, this study also demonstrates how to apply model-free neural control with unsupervised learning to existing model-based control (e.g., local motion planner) for efficient proactive behavior learning and control of mobile robots.