Unmanned Systems

In this paper, we explore a novel multi-mode hybrid Unmanned Aerial Vehicle (UAV). We combine a tailless fixed-wing with a dual-wing monocopter such that the craft’s propulsion systems and aerodynamic surfaces are fully utilized in both a horizontal cruising mode and a vertical hovering mode. This maximizes the structural efficiency across the flight envelope, thereby reducing drag and unused mass while airborne in either flight mode. This UAV is also designed such that the transition between the two flight modes can be executed in mid-air, using only its existing flight actuators and sensors — there are no transition specific actuators. Using two prototypes, the foundational design and control of the system is established; the first explores the hovering mode characteristics of the unique dual-wing monocopter configuration, while the second explores the full multi-mode capabilities of the combined platform. In addition to analytical simulations, the prototypes are experimentally evaluated and assessed to demonstrate the feasibility, viability and potential of this multi-mode aerial robot design.

Autonomous unmanned vehicles are well suited for long-endurance, persistent intelligence, surveillance and reconnaissance (PISR) missions. In order to conduct missions, vehicles must implement a method of task selection. We propose the Maximal Distance Discounted & Weighted Revisit Period ($M{D}^{2}WRP$) utility function as a solution. We derive $M{D}^{2}WRP$ as a zeroth-order approximation to an infinite horizon solution of PISR when formulated as a dynamic programming (DP) problem. We then use the DP solution to develop a heuristic utility function for autonomous task selections, with the goal of minimizing the prioritized revisit time to each task. Our function adapts to different task maps and task priorities, is scalable in the number of tasks, and is robust to the ad-hoc addition or removal of tasks. We demonstrate how the $M{D}^{2}WRP$ parameters influence vehicle behavior. We also prove that the policy results in steady-state task selections that are periodic and that such periodicity occurs regardless of initial conditions. We then demonstrate periodicity via numerical simulations on a set of test scenarios. We present a two-step heuristic methodology for selecting utility function parameters that deliver empirically good performance, which we demonstrate through a simulation-based comparison to a single-vehicle Traveling Salesman Problem (TSP) solution. The comparisons are based on four sample task maps designed to resemble operational scenarios.

Vision-based tracking of a moving target is achieved in three steps: image processing, coordinate recovery and trajectory following. A testing platform containing two spawned quadrotors as the target and the tracker is developed on ROS, from which sequences of image frames are generated by an on-board camera while the target moves along a given path on the ground. A simple image processing algorithm is developed that achieves visual tracking and is robust to image blur and other background noises. Coordinate recovery recovers the original target trajectory as the desired path of the tracker in the real-world. A target following control law moves the tracker along the desired trajectory point-by-point. An analysis of the robustness of the tracking approach with respect to image blur and measurement noise of the tracker position is carried out to assure the applicability of the approach to real-world scenarios.

This paper investigates the interference that arises from overlapping Unmanned Aerial Vehicle (UAV) propellers during hovering flight. The tests have been conducted on $28^{\prime \prime }\times 8.4^{\prime \prime }$ ultralight carbon fiber propellers using a bespoke mount and the RCBenchmark Series 1780 dynamometer at various degrees of overlap $(d∕D)$ and vertical separation $(z∕D)$. A great deal of confusion regarding the losses that are associated with mounting propellers in a co-axial configuration is reported in the literature, with a summary of historical tandem helicopters having been conducted. The results highlight a region of beneficial overlap (0–20%), which has the potential to be advantageous to a wide range of UAVs.

Autonomous Aerial Refueling (AAR) is vulnerable to various failures and involves cooperation among autonomous receivers, tankers and remote pilots. Dangerous flight maneuvers may be executed when unexpected failures or command conflicts happen. To solve this problem, a failsafe mechanism based on State Tree Structures (STS) is proposed. The failsafe mechanism is a control logic that guides what subsequent actions the autonomous receiver should take, by observing real-time information of internal low-level subsystems such as guidance and drogue&probe and external instructions from tankers and pilots. To generate such a controller using STS, the AAR procedure is decomposed into several modes, and safety issues related with seven low-level subsystems are summarized. Then common functional demands and safety requirements are textually described. On this basis, the AAR plants and specifications are modeled by STS, and a supervisor is synthesized to control the AAR model. To prove its feasibility and correctness, a simulation environment incorporating such a logic supervisor is built and tested. The design procedures presented in this paper can be used in decision-making strategies for similar flight tasks. Supporting materials can be downloaded in Github, [https://github.com/KevinDong0810/Failsafe-Design-for-AAR-using-STS] including related software, input documents and output files.