Parameterized Optimal Trajectory Generation for Target Localization

This paper presents an approach to near-optimal target localization for small and mi-cro uninhabited aerial vehicles using a family of pre-computed parameterized trajectories. These trajectories are pre-computed for a set of nominal target locations uniformly dis-tributed over the sensor field of view and stored off-line. Upon target detection the vehicle chooses the trajectory corresponding to the closest nominal target location. Adaptation is enabled with the ability to select new trajectories as the target state estimate is updated. Simulation results show the validity of this approach for both single target and sequential target localization missions. Further, results show that very coarse trajectory tables give the same or better target localization performance as finely discretized tables. I

Inverted Landing in a Small Aerial Robot via Deep Reinforcement Learning for Triggering and Control of Rotational Maneuvers

Inverted landing in a rapid and robust manner is a challenging feat for aerial robots, especially while depending entirely on onboard sensing and computation. In spite of this, this feat is routinely performed by biological fliers such as bats, flies, and bees. Our previous work has identified a direct causal connection between a series of onboard visual cues and kinematic actions that allow for reliable execution of this challenging aerobatic maneuver in small aerial robots. In this work, we first utilized Deep Reinforcement Learning and a physics-based simulation to obtain a general, optimal control policy for robust inverted landing starting from any arbitrary approach condition. This optimized control policy provides a computationally-efficient mapping from the system's observational space to its motor command action space, including both triggering and control of rotational maneuvers. This was done by training the system over a large range of approach flight velocities that varied with magnitude and direction. Next, we performed a sim-to-real transfer and experimental validation of the learned policy via domain randomization, by varying the robot's inertial parameters in the simulation. Through experimental trials, we identified several dominant factors which greatly improved landing robustness and the primary mechanisms that determined inverted landing success. We expect the learning framework developed in this study can be generalized to solve more challenging tasks, such as utilizing noisy onboard sensory data, landing on surfaces of various orientations, or landing on dynamically-moving surfaces.Comment: 8 pages, 6 Figures, Submitted for ICRA 2023 Conference (Pending Review

Optimal Waypoint Guidance for Collision Scenarios

<p>As unmanned aerial vehicles (UAVs) grow in popularity for a variety of industrial, military, and civilian applications, the need for such aircraft to possess safe and reliable collision avoidance system becomes more pressing. This thesis proposes a collision avoidance system for UAVs cruising in a flight environment to avoid unexpected collision with either static and dynamic obstacles.</p><p>The proposed system can adhere to airspace regulations specifying safety zones around other aircraft, is simple enough for deployment in a "plug-and-play" fashion onboard a UAV, and is efficient in terms of control effort needed to achieve the avoidance. The system contains logic to determine if a given obstacle is threatening, as well as to generate an "aiming point" to which the vehicle should travel to avoid the obstacle; it accomplishes this by examining the geometry of the encounter and transposing the problem of collision avoidance to one of waypoint guidance. Furthermore, the system contains a simple feedback controller that can guide the ownship to such an aiming point or a generic goal in the flight environment, while minimizing the overall control effort (e.g., fuel) needed to do so.</p><p>High-fidelity simulation was used to validate the performance of the avoidance framework. The potential of the framework to enable UAVs to avoid head-on collision, even at high speeds, was also successfully demonstrated through flight tests at a university research site at an airport. In the in situ flight tests, the framework was deployed on a helicopter UAV that avoided a simulated dynamic intruder UAV at relative speeds in excess of 100 feet per second, being initially separated by 1 mile at the outset of the encounter.</p&gt

Estimating the Vertical Structure of Weather-Induced Mission Costs for Small UAS

The performance of small uninhabited aerial systems (UAS) is very sensitive to the atmospheric state. Improving awareness of the environment and its impact on mission performance is important to enabling greater autonomy for small UAS. A modeling system is proposed that allows a small UAS to build a model of the atmospheric state using computational resources available onboard the aircraft and relate the atmospheric state to the cost of completing a mission. In this case, mission cost refers to the energy required per distance traveled. The system can use in situ observations made by the aircraft, but can also incorporate observations from other aircraft and sensors. The modeling system is demonstrated in a flight test aboard a small UAS and validated against radiosondes and numerical weather model analyses. The test demonstrates that the modeling system can represent the atmospheric state and identifies times where significant error exists between the state expected by the numerical weather model and that observed. Transformation of the atmospheric state into a mission performance cost identifies cases where the mission performance cost predicted by a numerical weather model differs from that observed by more than 30%