A Cooperative Search and Coverage Algorithm with Controllable Revisit and Connectivity Maintenance for Multiple Unmanned Aerial Vehicles

Abstract

In this paper, we mainly study a cooperative search and coverage algorithm for a given bounded rectangle region, which contains several unknown stationary targets, by a team of unmanned aerial vehicles (UAVs) with non-ideal sensors and limited communication ranges. Our goal is to minimize the search time, while gathering more information about the environment and finding more targets. For this purpose, a novel cooperative search and coverage algorithm with controllable revisit mechanism is presented. Firstly, as the representation of the environment, the cognitive maps that included the target probability map (TPM), the uncertain map (UM), and the digital pheromone map (DPM) are constituted. We also design a distributed update and fusion scheme for the cognitive map. This update and fusion scheme can guarantee that each one of the cognitive maps converges to the same one, which reflects the targets’ true existence or absence in each cell of the search region. Secondly, we develop a controllable revisit mechanism based on the DPM. This mechanism can concentrate the UAVs to revisit sub-areas that have a large target probability or high uncertainty. Thirdly, in the frame of distributed receding horizon optimizing, a path planning algorithm for the multi-UAVs cooperative search and coverage is designed. In the path planning algorithm, the movement of the UAVs is restricted by the potential fields to meet the requirements of avoiding collision and maintaining connectivity constraints. Moreover, using the minimum spanning tree (MST) topology optimization strategy, we can obtain a tradeoff between the search coverage enhancement and the connectivity maintenance. The feasibility of the proposed algorithm is demonstrated by comparison simulations by way of analyzing the effects of the controllable revisit mechanism and the connectivity maintenance scheme. The Monte Carlo method is employed to validate the influence of the number of UAVs, the sensing radius, the detection and false alarm probabilities, and the communication range on the proposed algorithm.

Sensing and Connection Systems for Assisted and Autonomous Driving and Unmanned Vehicles

“…The issue of cooperative algorithms for unmanned aerial, underwater or ground vehicles (UAV/UUV/UGV) is addressed in half of the works that are published in the special issues [1,2,3,4,5,6], with the first four from Universities in China and the last two from South Korea and Spain. …”

“…[1] presents a cooperative search and coverage algorithm for a given bounded rectangle region, which contains several unknown stationary targets, by a team of UAVs with non-ideal sensors and limited communication ranges. The goal of the proposed cooperative search and coverage algorithm is minimizing the search time, while gathering more information about the environment and finding more targets.…”

“…The proposed special issue has invited submissions related to sensing and connectivity systems for assisted and autonomous driving and for unmanned aerial, underwater and ground vehicles (namely UAV, UUV and UGV) [1,2,3,4,5,6,7,8,9,10,11,12]. Recent studies on the future of the automotive and robotic industry predict that the borders between the ICT (Information and Communication Technology) industry and the consumer industry will blur, and that vehicles will become consumer-centric [13].…”

Co-Optimization of Communication and Sensing for Multiple Unmanned Aerial Vehicles in Cooperative Target Tracking

“…In the framework of distributed receding horizon optimization, as shown in Figure 3, at time k, each UAV Ai receives the planned control inputs with the other UAVs. Thus, the distribution optimization is performed in a specific order [25]. For example, An plans its trajectory based on the trajectories of the previous (n − 1) aircrafts (A1~An−1) and then sends its trajectory to aircraft An+1.…”

Self-Organized Disaster Management System by Distributed Deployment of Connected UAVs

“…Authors propose an optimization framework for optimally deploy a set of drones to cover ground targets while optimizing both the drones altitude and the total deployment cost. A cooperative search and coverage algorithm is presented in [9] where the drones are deployed to explore the environment in order to gather information about it and concentrate the UAVs around targets to capture them as soon as possible. Global connectivity among drones and a sink is not considered.…”