Optimal circular flight of multiple UAVs for target tracking in urban areas

This work is an extension of our previous result in which a novel single-target tracking algorithm for fixed-wing UAVs (Unmanned Air Vehicles) was proposed. Our previous algorithm firstly finds the centre of a circular flight path, rc, over the interested ground target which maximises the total chance of keeping the target inside the camera field of view of UAVs, , while the UAVs fly along the circular path. All the UAVs keep their maximum allowed altitude and fly along the same circle centred at rc with the possible minimum turn radius of UAVs. As discussed in [1,4], these circular flights are highly recommended for various target tracking applications especially in urban areas, as for each UAV the maximum altitude flight ensures the maximum visibility and the minimum radius turn keeps the minimum distance to the target at the maximum altitude. Assuming a known probability distribution for the target location, one can quantify , which is incurred by the travel of a single UAV along an arbitrary circle, using line-of-sight vectors. From this observation, (the centre of) an optimal circle among numerous feasible ones can be obtained by a gradient-based search combined with random sampling, as suggested in [1]. This optimal circle is then used by the other UAVs jointly tracking the same target. As the introduction of multiple UAVs may minimise further, the optimal spacing between the UAVs can be naturally considered. In [1], a typical line search method is suggested for this optimal spacing problem. However, as one can easily expect, the computational complexity of this search method may undesirably increase as the number of UAVs increases. The present work suggests a remedy for this seemingly complex optimal spacing problem. Instead of depending on time-consuming search techniques, we develop the following algorithm, which is computationally much more efficient. Firstly, We calculate the distribution (x), where x is an element of , which is the chance of capturing the target by one camera along . Secondly, based on the distribution function, (x), find separation angles between UAVs such that the target can be always tracked by at least one UAV with a guaranteed probabilistic measure. Here, the guaranteed probabilistic measure is chosen by taking into account practical constraints, e.g. required tracking accuracy and UAVs' minimum and maximum speeds. Our proposed spacing scheme and its guaranteed performance are demonstrated via numerical simulations

Feasibility of Onboard Processing of Heuristic Path Planning and Navigation Algorithms within SUAS Autopilot Computational Constraints

This research addresses the flight path optimality of Small Unmanned Aerial Systems (SUAS) conducting overwatch missions for convoys or other moving ground targets. Optimal path planning algorithms have been proposed, but are computationally excessive for real-time execution. Using the Arduino-based ArduPilot Mega Unmanned Aerial Vehicle (UAV) autopilot system, Hardware-in-the-Loop (HIL) analysis is conducted on default mobile target tracking methods. Designed experimentation is used to determine autopilot settings that improve performance with respect to path optimality. Optimality is characterized using a weighted combination of stand-off range and aircraft roll-rate. Finally, a state-based heuristic navigation strategy is designed, developed, and tested that approximates optimal path solutions and can be used for real-time execution. A 66% improvement in mean performance is achieved over default target tracking methods. Finite state machine improvements are found to be statistically significant and it is concluded that heuristic strategies can be a viable approach to realizing near-optimal SUAS flight paths utilizing onboard processing capabilities

Aerial-Ground collaborative sensing: Third-Person view for teleoperation

Rapid deployment and operation are key requirements in time critical application, such as Search and Rescue (SaR). Efficiently teleoperated ground robots can support first-responders in such situations. However, first-person view teleoperation is sub-optimal in difficult terrains, while a third-person perspective can drastically increase teleoperation performance. Here, we propose a Micro Aerial Vehicle (MAV)-based system that can autonomously provide third-person perspective to ground robots. While our approach is based on local visual servoing, it further leverages the global localization of several ground robots to seamlessly transfer between these ground robots in GPS-denied environments. Therewith one MAV can support multiple ground robots on a demand basis. Furthermore, our system enables different visual detection regimes, and enhanced operability, and return-home functionality. We evaluate our system in real-world SaR scenarios.Comment: Accepted for publication in 2018 IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR