Abstract
A Multirobot Path-Planning Strategy for Autonomous Wilderness Search and Rescue
Mobile robotics has played an increasingly important role in search and rescue efforts. The success of robotics in urban search and rescue environments has encouraged its expansion to wilderness search and rescue. Wilderness search and rescue presents unique challenges, including less predictable terrain, a moving target, and an increasing search area as time progresses. Current wilderness search and rescue groups primarily use human searchers; however, the authors believe that multirobot coordination results in more systematic coverage of the search area. This paper proposes a novel path-planning strategy for autonomous multirobot coordination in wilderness search and rescue scenarios.
The investigators identify 3 important components when using multirobot coordination for wilderness search and rescue: planning initial paths, implementing and evaluating the paths, and replanning the paths if necessary. When creating a path to be followed by the robots, the investigators propose a novel process that creates boundaries using “iso-probability curves.” These curves are constructed based on rays from the last known point of the lost human subject. Multiple autonomously functioning robots that communicate with each other will cover these points on the curves, in the hope of finding the lost subject. Although robots capable of performing this function still need to be fully implemented, human teams can also utilize the iso-probability curves to develop improved and systemic search methods. Even though there are recognized limitations, including difficult terrain, the researchers’ proposal creates an effective model for robot path planning during wilderness search and rescue efforts.
(IEEE Trans Cybern. 2014 Nov 3 [Epub ahead of print]) A Macwan, J Vilela, G Nejat, B Benhabib.