In this work, we present an Integrated Guidance and Controller (IGC) scheme to drive quadcopters in path-following tasks with obstacle avoidance and constant uncertainty rejection. This scheme is based on the combination of a time-varying artificial vector field and Backstepping with integral action control. The vector field switches between two behaviors: (i) path-following; and (ii) obstacle circumnavigation to allow collision avoidance. This vector field is then integrated into a nonlinear controller designed via Backstepping with Integral Action to deal with the quadcopter vehicle dynamics and reject constant uncertainties. The considered vehicle model is based on quaternion algebra. The control inputs are considered to be the total thrust and torques. Stability is proved by using Lyapunov's Theory and Matrosov's Theorem.

The present work focuses on the development of an efficient path controller to guide a fixed-wing UAV (Unmanned Aerial Vehicle) to follow a closed curve and avoid unknown dynamic obstacles. Our strategy is composed of two layers: a top level layer responsible for guidance and a lower level layer responsible for tracking the references given by the top level. To solve the guidance problem, we propose a vector field strategy that switches between two forms: a vector field to converge and circulate the target curve and a vector field to avoid obstacles by circulating the closest one. To make the fixed-wing UAV follow the velocity provided by the guidance vector field we consider a Model Predictive Control scheme. The feedback linearization allows efficient computation of control commands as a linear MPC controller can be employed. Our results are validated in simulations that take into account the 6DOF (Degrees of Freedom) model with constraints of the aircraft, wind disturbance and uncertainties on the measurements.

This paper presents a guidance vector field strategy to control a fixed-wing UAV (unmanned aerial vehicle)subject to uncertainty in order to converge to and circulate a closed curve in ℝ3. The control system is designed based on a reference model of the airplane with constrained input controls. The law is independent of the vector field's structure, however, some analysis considers a consolidated vector field approach. Asymptotic stability is proven with Lyapunov Theory and ultimate bounds are found when bounded uncertainties are taken into account. The control law is continuous except in the surroundings of the unavoidable field's singularities. A theorem ensures asymptotic convergence when a switch is made. Simulations with a 6 DOF, 12 states realistic aircraft model demonstrate the efficiency of the strategy and its advantages.

This paper describes RBESP, a dual stack protocol designed as part of the communication infrastructure of a short-range Unmanned Aerial Vehicle (UAV). It aims at providing a reliable communication between the UAV and its base station and a best effort communication between the UAV and a WSN. RBESP targets two distinct communication requirements using the same communication transceiver. The other end of the communication link can be heterogeneous devices transmitting with the IEEE 802.15.4 standard, which is also used in the UAV transceiver. The paper details the software layers that implement both the reliable communication with the base station and the best-effort communication with the WSN nodes. Two different scenarios were implemented in order to allow RBESP evaluation. The first scenario consists of the UAV node communicating with spatially distributed sensors on the ground. In the second scenario, the UAV node delivers the collected data to the base station. Both communication scenarios are very different in terms of connection time and volume of transmitted data. Obtained results are presented and discussed along the paper.

The IEEE RAS Ontologies for Robotics and Automation Working Group is dedicated to developing a methodology for knowledge representation and reasoning in robotics and automation. As part of this working group, the Autonomous Robots sub-group is tasked with developing ontology modules for autonomous robots. This paper describes the work in progress on the development of ontologies for autonomous systems. For autonomous systems, the focus is on the cooperation, coordination, and communication of multiple unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), and autonomous underwater vehicles (AUVs). The ontologies serve as a framework for working out concepts of employment with multiple vehicles for a variety of operational scenarios with emphasis on collaborative and cooperative missions.

This paper presents an application of the nonlinear ℋ∞controller to two-wheeled self-balanced vehicles. An underactuated mechanical control system representation under input coupling is used to design the control law. To achieve null steady-state error when persistent disturbances are acting on the system, the integral of the position error is considered in the state vector. Practical results obtained in experiments using a two-wheeled vehicle equipped with an embedded microcontroller system are presented. These results corroborate the good features of the proposed controller in presence of external disturbances, extreme initial conditions and unmodeled dynamics.

This paper presents a model predictive controller (MPC) structure for solving the path-tracking problem of terrestrial autonomous vehicles. To achieve the desired performance during high-speed driving, the controller architecture considers both the kinematic and the dynamic control in a cascade structure. Our study contains a comparative study between two kinematic linear predictive control strategies: The first strategy is based on the successive linearization concept, and the other strategy combines a local reference frame with an approaching path strategy. Our goal is to search for the strategy that best comprises the performance and hardware-cost criteria. For the dynamic controller, a decentralized predictive controller based on a linearized model of the vehicle is used. Practical experiments obtained using an autonomous ldquoMini-Bajardquo vehicle equipped with an embedded computing system are presented. These results confirm that the proposed MPC structure is the solution that better matches the target criteria.