Motion Primitives-based Navigation Planning using Deep Collision Prediction

TL;DR

This paper introduces a novel navigation planning method that uses a learning-based collision prediction network, incorporating uncertainty measures, to enable resilient robot navigation in complex environments.

Contribution

The paper presents a new collision prediction network that accounts for uncertainty and integrates it into a motion primitives-based planner for robot navigation.

Findings

Effective collision prediction in cluttered environments

Improved navigation resilience with uncertainty-aware planning

Successful deployment on a small flying robot in real-world tests

Abstract

This paper contributes a method to design a novel navigation planner exploiting a learning-based collision prediction network. The neural network is tasked to predict the collision cost of each action sequence in a predefined motion primitives library in the robot's velocity-steering angle space, given only the current depth image and the estimated linear and angular velocities of the robot. Furthermore, we account for the uncertainty of the robot's partial state by utilizing the Unscented Transform and the uncertainty of the neural network model by using Monte Carlo dropout. The uncertainty-aware collision cost is then combined with the goal direction given by a global planner in order to determine the best action sequence to execute in a receding horizon manner. To demonstrate the method, we develop a resilient small flying robot integrating lightweight sensing and computing resources. A set of simulation and experimental studies, including a field deployment, in both cluttered and perceptually-challenging environments is conducted to evaluate the quality of the prediction network and the performance of the proposed planner.