Multi-Objective Cooperative Search of Spatially Diverse Routes in Uncertain Environments

TL;DR

This paper presents a cooperative multi-vehicle path planning method that explores spatially diverse routes in uncertain environments, optimizing for safety and value by balancing multiple factors and vehicle cooperation.

Contribution

It introduces a novel sequential cooperative path planner that dynamically adjusts route selection criteria and enforces spatial diversity among multiple vehicles.

Findings

Effective generation of spatially diverse routes in uncertain environments

Improved route safety and value through cooperative planning

Adjustable diversity control via a gain parameter

Abstract

This paper focuses on developing new navigation and reconnaissance capabilities for cooperative unmanned systems in uncertain environments. The goal is to design a cooperative multi-vehicle system that can survey an unknown environment and find the most valuable route for personnel to travel. To accomplish the goal, the multi-vehicle system first explores spatially diverse routes and then selects the safest route. In particular, the proposed cooperative path planner sequentially generates a set of spatially diverse routes according to a number of factors, including travel distance, ease of travel, and uncertainty associated with the ease of travel. The planner's dependence on each of these factors is altered by a weighted score, doing so changes the criteria for determining an optimum route. To penalize the selection of same paths by different vehicles, a control gain is used to increase the cost of paths that lie near the route(s) assigned to other vehicles. By varying the control gain, the spatial diversity among routes can be accomplished. By repeatedly searching for different paths cooperatively, an optimal path can be selected that yields the most valuable route.