Multi-Objective Cooperative Search of Spatially Diverse Routes in Uncertain Environments
This addresses navigation and reconnaissance challenges for unmanned systems in uncertain environments, representing an incremental improvement in cooperative path planning.
The paper tackles the problem of cooperative unmanned systems navigating uncertain environments to find valuable routes for personnel, by developing a multi-vehicle path planner that generates spatially diverse routes based on factors like distance and uncertainty, and selects an optimal route through repeated cooperative searches.
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.