In this paper, an image based visual servo (IBVS) scheme is developed for a hexacopter, equipped with a robotic soft grasper to perform autonomous object detection and grasping. The structural design of the hexacopter-soft grasper system is analyzed to study the soft grasper’s influence on the multirotor’s aerodynamics. The object detection, tracking and trajectory planning are implemented on a high-level computer which sends position and velocity setpoints to the flight controller. A soft robotic grasper is mounted on the UAV to enable the collection of various contaminants. The use of soft robotics removes excess weight associated with traditional rigid graspers, as well as simplifies the controls of the grasping mechanics. Collected experimental results demonstrate autonomous object detection, tracking and grasping. This pipeline would enable the system to autonomously collect solid and liquid contaminants in water canal based on GPS and multi-camera system. It can also be used for more complex aerial manipulation including in-flight grasping.

This paper presents a new approach for Unmanned Aerial Vehicle (UAV) attitude estimation using a cascade of nonlinear observer and linearized Kalman filter. The nonlinear observer is globally asymptotically stable and is designed using linear matrix inequalities (LMI). The exogenous signal from the nonlinear observer is used to generate a linearized model for the Kalman filter. The method is implemented for attitude estimation of a quadcopter. The nonlinear model is derived from the Newton-Euler equations. The nonlinear model is locally Lipschitz due to the cross and dot products between the angular and linear velocity vectors. The attitude estimation from the dynamical system presented in this paper can be used as a module for fault detection. Simulations in Gazebo on a PX4 using Software In The Loop (SITL) shows the proposed method is able to estimate the attitude of a quadcopter accurately.

This paper proposes an energy-efficient adaptive robust tracking control method for a class of fully actuated, thrust vectoring unmanned aerial vehicles (UAVs) with parametric uncertainties including unknown moment of inertia, mass and center of mass, which would occur in aerial maneuvering and manipulation. We consider a novel vector thrust UAV with all propellers able to tilt about two perpendicular axes, so that the thrust force generated by each propeller is a fully controllable vector in 3D space, based on which an adaptive robust control is designed for accurate trajectory tracking in the presence of inertial parametric uncertainties and uncertain nonlinearities. Theoretically, the resulting controller achieves a guaranteed transient performance and final tracking accuracy in the presence of both parametric uncertainties and uncertain nonlinearities. In addition, in the presence of only parametric uncertainties, the controller achieves asymptotic output tracking. To resolve the redundancy in actuation, a thrust force optimization problem minimizing power consumption while achieving the desired body force wrench is formulated, and is shown to be convex with linear equality constraints. Simulation results are also presented to verify the proposed solution.

In this paper, we address the decentralized collaborative trajectory planning and target surrounding of multiple Unmanned Air Vehicles (UAVs) in three-dimensional space using Partial Differential Equation (PDE) method. The mission objective is simultaneously arrival of UAVs with safe flight trajectory to a certain radius of an a – priori target. Then by reforming the configuration of swarm, UAVs would circle around the target. The assumption in this work is that the arrival time between the UAVs’ final and initial positions are defined a – priori. The constraints in this paper are (i) Three dimensional Dubins path and UAV dynamic constraints, (ii) Minimum separation distance between UAVs, and (iii) Collision-free trajectory throughout the flight. We define a novel concept of Prediction Set (PS) based on our previous study on PDE path planning method and then we apply the PDE PSs to the constraints of the problem (i.e., (i) to (iii)) and solve the optimization problem. Finally, the concept is demonstrated by numerical simulation and an experiment to represent the effectiveness of the solution.

We employ a genetic algorithm approach to solving the persistent visitation problem for UAVs. The objective is to minimize the maximum weighted revisit time over all the sites in a cyclicly repeating walk. In general, the optimal length of the walk is not known, so this method (like the exact methods) assume some fixed length. Exact methods for solving the problem have recently been put forth, however, in the absence of additional heuristics, the exact method scales poorly for problems with more than 10 sites or so. By using a genetic algorithm, performance and computation time can be traded off depending on the application. The main contributions are a novel chromosome encoding scheme and genetic operators for cyclic walks which may visit sites more than once. Examples show that the performance is comparable to exact methods with better scalability.

We present a state estimator for a UAV operating in an environment equipped with ultra-wideband radio beacons. The beacons allow the UAV to measure distances to known positions in the world. The estimator additionally uses the vehicle’s rate gyroscope and accelerometer, and crucially does not rely on any knowledge of the vehicle’s dynamic properties (e.g. mass, mass moment of inertia, aerodynamic properties). This makes the estimator especially useful in situations where the exact system parameters are unknown (e.g. due to unknown payloads), or where the environment is unpredictable (e.g. wind gusts). Experimental results demonstrate the approach’s efficacy, and demonstrate that the estimator can run on low-cost microcontrollers with typical sensors.