Multipass Target Search in Natural Environments

Abstract

Consider a disaster scenario where search and rescue workers must search difficult to access buildings during an earthquake or flood. Often, finding survivors a few hours sooner results in a dramatic increase in saved lives, suggesting the use of drones for expedient rescue operations. Entropy can be used to quantify the generation and resolution of uncertainty. When searching for targets, maximizing mutual information of future sensor observations will minimize expected target location uncertainty by minimizing the entropy of the future estimate. Motion planning for multi-target autonomous search requires planning over an area with an imperfect sensor and may require multiple passes, which is hindered by the submodularity property of mutual information. Further, mission duration constraints must be handled accordingly, requiring consideration of the vehicle’s dynamics to generate feasible trajectories and must plan trajectories spanning the entire mission duration, something which most information gathering algorithms are incapable of doing. If unanticipated changes occur in an uncertain environment, new plans must be generated quickly. In addition, planning multipass trajectories requires evaluating path dependent rewards, requiring planning in the space of all previously selected actions, compounding the problem. We present an anytime algorithm for autonomous multipass target search in natural environments. The algorithm is capable of generating long duration dynamically feasible multipass coverage plans that maximize mutual information using a variety of techniques such as ϵ-admissible heuristics to speed up the search. To the authors’ knowledge this is the first attempt at efficiently solving multipass target search problems of such long duration. The proposed algorithm is based on best first branch and bound and is benchmarked against state of the art algorithms adapted to the problem in natural Simplex environments, gathering the most information in the given search time.

Multi-UAS Minimum Time Search in Dynamic and Uncertain Environments

“…In Search and Rescue (SaR) missions that take place after natural disasters like an earthquake, a tsunami or an avalanche time is a critical factor for finding survivors, as well as after an accident such as a shipwreck or airplane crash. For instance, survival rates of earthquake victims continually drop with the delay of help, with a sharp drop off at around 48 hours (Kuhlman et al, 2017). Or, in the case of buried victims by an avalanche the survival rates drop dramatically after 15 minutes (Hoffmann et al, 2006).…”

Distributed Cooperative Search Algorithm With Task Assignment and Receding Horizon Predictive Control for Multiple Unmanned Aerial Vehicles

“…Cooperative search and monitoring is one of the most important applications of multi-UAV systems. The search region is divided into numerous search cells [5]- [7],and the values of the uncertainty and the target probability associated with each cell represent the status of the environment and the distribution of the targets. The objective of the cooperative search is to find all hidden targets and cover the entire region in the shortest time.…”

Sensor-Oriented Path Planning for Multiregion Surveillance with a Single Lightweight UAV SAR

“…Conventionally, the main considerations in UAV path planning are the feasibility, length, and safety of the flight path [9]. Nowadays, the cooperative missions such as target search [10,11,12], tracking [13] and surveillance [14,15,16] have been paid more attention by the UAV path planners.…”