Range-Based Reactive Deployment of a Flying Robot for Target Coverage

Flying robots, also known as drones and unmanned aerial vehicles (UAVs), have found numerous applications in civilian domains thanks to their excellent mobility and reduced cost. In this paper, we focus on a scenario of a flying robot monitoring a set of targets, which are assumed to be moving as a group, to which the sparse distribution of the targets is not applicable. In particular, the problem of finding the optimal position for the flying robot such that all the targets can be monitored by the on-board ground facing camera is considered. The studied problem can be formulated as the conventional smallest circle problem if all the targets' locations are given. Because it may be difficult to obtain the locations in practice, such as in Global Navigation Satellite Systems (GNSS) dined environments, a range-based navigation algorithm based on the sliding mode control method is proposed. This algorithm navigates the flying robot toward the farthest target dynamically, using the estimated robot-target distances from the received signal strength, until the maximum robot-target distance cannot be further reduced. It is light computation and easily implementable, and both features help to improve the energy efficiency of the flying robot because no heavy computation is required and no special sensing device needs to be installed on the flying robot. The presented solution does not directly solve the smallest circle problem. Instead, our proposed method dynamically navigates the flying robot to the center of the group of targets using the extracted distance information only. Simulations in Matlab and Gazebo have been conducted for both stationery and mobile targets to verify the effectiveness of the proposed approach.

Automated summary

This paper focuses on the scenario of a flying robot monitoring a group of moving targets and presents a range-based navigation algorithm to solve the problem of finding the optimal position. The algorithm dynamically navigates the robot based on the estimated distances using signal strength. Simulations in Matlab and Gazebo were conducted to verify the effectiveness of the proposed approach.