Social Navigation in Crowded Environments with Model Predictive Control and Deep Learning-Based Human Trajectory Prediction

Navigating a robot among a crowd has received increasing attention from researchers over the last few decades, resulting in the emergence of numerous approaches aimed at addressing the problem of social navigation to date. Our proposed approach couples agent motion prediction and plan- ning to avoid the freezing robot problem while simultaneously capturing multi-agent social interactions by utilizing a state-of- the-art trajectory prediction model i.e., social long short-term memory model (Social-LSTM). Leveraging the output of Social- LSTM for the prediction of future trajectories of pedestrians at each time-step given the robot’s possible future actions, our framework computes the optimal control action using Model Predictive Control (MPC) for the robot to navigate among pedestrians. We demonstrate the effectiveness of our proposed approach in multiple scenarios of simulated social navigation and compare it against several state-of-the-art reinforcement learning-based methods.