Bounded Rational RRT-QX: Multi-Agent Motion Planning in Dynamic Human-Like Environments Using Cognitive Hierarchy and Q-Learning
Josh Netter, George P. Kontoudis, Kyriakos G. Vamvoudakis · 2021 60th IEEE Conference on Decision and Control (CDC) · 2021
This paper presents a multi-agent motion planning algorithm for human-like navigation in dynamic environments. A cognitive hierarchy approach is used to model the motion of autonomous agents. We discuss potential levels of rationality and introduce a method to predict them in real-time. The rationality level prediction is achieved by observing the kinody-namic distance (KD) of other agents. An offline training phase is required to learn the maximum KD from multiple boundary value problems. Collision avoidance is ensured by introducing artificial obstacles in the environment based on the predicted levels of rationality. The motion planning is then carried out using RRT-QX. The effectiveness of the bounded rational motion planning algorithm is illustrated in simulations.