Motion Prediction Based on Multiple Futures for Dynamic Obstacle Avoidance of Mobile Robots

The ability to decide and adjust actions according to motion prediction of dynamic obstacles offers a flexible planning scheme and ampler reaction time to avoid potential impact. Prediction-based collision avoidance implies a two-stage decision-making process from motion prediction to action planning. One of the challenges in motion prediction is the movements of objects are usually non-deterministic and governed by multimodal models. Many studies have been made on motion prediction of dynamic obstacles and action planning for mobile robots separately. The objective of this work is to explore their coherence in terms of multiple future predictions by combining a data-driven motion prediction approach with a model-based control strategy. More specifically, we integrate motion prediction from deep learning models, Mixture Density Networks (MDNs) with a Non-linear Model Predictive Control (NMPC) framework. The deep learning models produce the multimodal probability distribution of future positions of dynamic obstacles, which is utilized by the MPC controller as a constraint. We show via simulation that the selected model provides valid predictions of motion in a dynamic environment. The prediction result endows the controller with the capability to avoid dynamic obstacles in advance