I-Planner: Intention-Aware Motion Planning Using Learning Based Human Motion Prediction

TL;DR

This paper introduces I-Planner, a motion planning algorithm that predicts human actions using learned models and computes safe, smooth robot trajectories in shared environments, accounting for uncertainties and noise.

Contribution

It presents a novel intention-aware planning method that integrates learned human motion predictions with collision probability bounds for safe robot navigation.

Findings

Effective in complex simulated scenarios

Demonstrates safe trajectories in real-world benchmarks

Handles noise in human motion prediction

Abstract

We present a motion planning algorithm to compute collision-free and smooth trajectories for high-DOF robots interacting with humans in a shared workspace. Our approach uses offline learning of human actions along with temporal coherence to predict the human actions. Our intention-aware online planning algorithm uses the learned database to compute a reliable trajectory based on the predicted actions. We represent the predicted human motion using a Gaussian distribution and compute tight upper bounds on collision probabilities for safe motion planning. We also describe novel techniques to account for noise in human motion prediction. We highlight the performance of our planning algorithm in complex simulated scenarios and real world benchmarks with 7-DOF robot arms operating in a workspace with a human performing complex tasks. We demonstrate the benefits of our intention-aware planner in terms of computing safe trajectories in such uncertain environments.