Prescient Collision-Free Navigation of Mobile Robots With Iterative Multimodal Motion Prediction of Dynamic Obstacles

Abstract

To explore safe interactions between a mobile robot and dynamic obstacles, this letter presents a comprehensive approach to collision-free navigation in dynamic indoor environments. The approach integrates Multimodal Motion Predictions (MMPs) of dynamic obstacles with predictive control for obstacle avoidance. MMP is achieved by a deep-learning method that predicts multiple plausible future positions. By repeating the MMP for each time offset in the future, multi-time-step MMPs are obtained. A nonlinear Model Predictive Control (MPC) solver uses the prediction outcomes to achieve collision-free trajectory tracking for the mobile robot. The proposed integration of multimodal motion prediction and trajectory tracking outperforms other non-deep-learning methods in complex scenarios. The approach enables safe interaction between the mobile robot and stochastic dynamic obstacles.