Learning Enabled Fast Planning and Control in Dynamic Environments with Intermittent Information

TL;DR

This paper presents a neural network-based approach for safe, fast planning and control of mobile robots in dynamic environments with intermittent, noisy external information, demonstrated through simulations and real experiments.

Contribution

It introduces a compositional neural network technique combining reachability analysis and potential fields for planning with stale data in dynamic environments.

Findings

Successful simulation of underwater vehicle crossing a shipping channel.

Real-world experiments with ground vehicles in limited communication environments.

Neural network effectively generates safe control actions using intermittent data.

Abstract

This paper addresses a safe planning and control problem for mobile robots operating in communication- and sensor-limited dynamic environments. In this case the robots cannot sense the objects around them and must instead rely on intermittent, external information about the environment, as e.g., in underwater applications. The challenge in this case is that the robots must plan using only this stale data, while accounting for any noise in the data or uncertainty in the environment. To address this challenge we propose a compositional technique which leverages neural networks to quickly plan and control a robot through crowded and dynamic environments using only intermittent information. Specifically, our tool uses reachability analysis and potential fields to train a neural network that is capable of generating safe control actions. We demonstrate our technique both in simulation with an underwater vehicle crossing a crowded shipping channel and with real experiments with ground vehicles in communication- and sensor-limited environments.