Autonomous Situational Awareness for Robotic Swarms in High-Risk Environments

TL;DR

This paper presents an autonomous planning system for robotic swarms operating in high-risk environments, utilizing A* pathfinding and multi-object tracking to adapt to dynamic situations and agent losses.

Contribution

It introduces a novel autonomous mission planning approach that integrates A* pathfinding with multi-object tracking for robotic swarms in hazardous areas.

Findings

Effective in tracking multiple objects in real-time

Adaptable to agent disablement and environmental changes

Improves mission success rates in high-risk scenarios

Abstract

This paper describes a technique for the autonomous mission planning of robotic swarms in high risk environments where agent disablement is likely. Given a swarm operating in a known area, a central command system generates measurements from the swarm. If those measurements indicate changes to the mission situation such as target movement or agent loss, the swarm planning is updated to reflect the new situation and guidance updates are broadcast to the swarm. The primary algorithms featured in this work are A* pathfinding and the Generalized Labeled Multi-Bernoulli multi-object tracking method.