A Deep Reinforcement Learning Motion Control Strategy of a Multi-rotor UAV for Payload Transportation with Minimum Swing

This paper addresses the problem of controlling a multirotor UAV with a cable-suspended load. In order to ensure the safe transportation of the load, the swinging motion, induced by the strongly coupled dynamics, has to be minimized. Specifically, using the Twin Delayed Deep Deterministic Policy Gradient (TD3) Reinforcement Learning algorithm, a policy Neural Network is trained in a model-free manner which navigates the vehicle to the desired waypoints while, simultaneously, compensating for the load oscillations. The learned policy network is incorporated into the cascaded control architecture of the autopilot by replacing the common PID position controller and, thus, communicating directly with the inner attitude one. The performance of the proposed policy is demonstrated through a comparative simulation and experimental study while using an octorotor UAV. © 2022 IEEE

A Visual Servoing Strategy for Coastline Tracking using an Unmanned Aerial Vehicle

In this paper, an Image-based Visual Servo (IBVS) Control strategy for the autonomous surveillance of coastlines using an octocopter aerial vehicle is proposed. The implemented strategy is focused on the vision-based detection and tracking of dynamic coastlines and in the presence of waves while flying in low altitudes. For this purpose, a Deep Neural Network (DNN) for the detection of the coastline is employed. The DNN is ac-companied by an analytical formulation of an Extended Kalman Filter (EKF), which considers an approximate periodical wave motion model to provide an online estimate of the coastline motion directly in image space. The estimated feedback is provided to an appropriately formulated IBVS tracking controller for the autonomous guidance of the octocopter along the coastline, ensuring the latter is always kept inside the camera's field of view. The efficacy of the proposed scheme is demonstrated via a set of comparative outdoor experiments using an octocopter flying along the coastline on various weather and beach settings. © 2022 IEEE

A Hybrid Model and Data-Driven Vision-Based Framework for the Detection, Tracking and Surveillance of Dynamic Coastlines Using a Multirotor UAV

A hybrid model-based and data-driven framework is proposed in this paper for autonomous coastline surveillance using an unmanned aerial vehicle. The proposed approach comprises three individual neural network-assisted modules that work together to estimate the state of the target (i.e., shoreline) to contribute to its identification and tracking. The shoreline is first detected through image segmentation using a Convolutional Neural Network. The part of the segmented image that includes the detected shoreline is then fed into a CNN real-time optical flow estimator. The position of pixels belonging to the detected shoreline, as well as the initial approximation of the shoreline motion, are incorporated into a neural network-aided Extended Kalman Filter that learns from data and can provide on-line motion estimation of the shoreline (i.e., position and velocity in the presence of waves) using the system and measurement models with partial knowledge. Finally, the estimated feedback is provided to a Partitioned Visual Servo tracking controller for autonomous multirotor navigation along the coast, ensuring that the latter will always remain inside the onboard camera field of view. A series of outdoor comparative studies using an octocopter flying along the shoreline in various weather and beach settings demonstrate the effectiveness of the suggested architecture. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

A Fault-Tolerant Control Scheme for Fixed-Wing UAVs with Flight Envelope Integration

Fault-tolerant control is currently the most important step towards increased autonomy for Unmanned Aerial Vehicles (UAVs). In this work, we consider actuator and airframe faults in fixed-wing UAVs and show that an online Flight Envelope (FE) calculation can interpret their effect on the achievable trim trajectories. Subsequently, we design a fully integrated control scheme with a RRT-based planner and 3 Nonlinear Model Predictive Control (MPC) layers. The FE is provided at each control layer with the beneficial result that non-trimmable, unstable trajectories are avoided. Results from high-fidelity simulations are provided. © 2020 IEEE

A Fault-Tolerant Control Scheme for Fixed-Wing UAVs with Flight Envelope Awareness

In this work a vertically integrated fault-tolerant control scheme for fixed-wing Unmanned Aerial Vehicles (UAVs) is presented. At its core, an online approximate Trim Flight Envelope generator yields the motion constraints of the UAV. Given fault information, it remains always up-to-date in view of emerging faults. The controller stack comprises of Nonlinear Model Predictive Controllers for angular velocity, linear velocity and position. Path Planning is achieved by Simple Sparse Rapidly-exploring Random Trees (SST). Both the controllers and the planner are aware of the flight constraints and are hence tolerant to faults. A large set of sensor and actuator faults, common to UAVs are considered and the controllability of the UAV is examined. Detailed simulations using real-time implementations of the controllers are carried out. © 2021, The Author(s), under exclusive licence to Springer Nature B.V

A Predictive Control Approach for Cooperative Transportation by Multiple Underwater Vehicle Manipulator Systems

This article addresses the problem of cooperative object transportation for multiple underwater vehicle manipulator systems (UVMSs) in a constrained workspace involving static obstacles. We propose a nonlinear model predictive control (NMPC) approach for a team of UVMSs in order to transport an object while avoiding significant constraints and limitations, such as kinematic and representation singularities, obstacles within the workspace, joint limits, and control input saturation. More precisely, by exploiting the coupled dynamics between the robots and the object and using certain load sharing coefficients, we design a predictive controller for each UVMS in order to cooperatively transport the object within the workspace's feasible region. Moreover, the control scheme adopts load sharing among the UVMSs according to their specific payload capabilities. In addition, the feedback relies on each UVMS's onboard measurements and no explicit data are exchanged online among the robots, thus reducing the required communication bandwidth. Finally, realistic simulation results conducted in the UwSim dynamic simulator running in robot operating system (ROS) environment as well as real-time experiments employing two small UVMSs and demonstrated the effectiveness of the proposed control strategy. © 2022 IEEE

Indoor Visual Exploration with Multi-Rotor Aerial Robotic Vehicles

In this work, we develop a reactive algorithm for autonomous exploration of indoor, unknown environments for multiple autonomous multi-rotor robots. The novelty of our approach rests on a two-level control architecture comprised of an Artificial-Harmonic Potential Field (AHPF) for navigation and a low-level tracking controller. Owing to the AHPF properties, the field is provably safe while guaranteeing workspace exploration. At the same time, the low-level controller ensures safe tracking of the field through velocity commands to the drone’s attitude controller, which handles the challenging non-linear dynamics. This architecture leads to a robust framework for autonomous exploration, which is extended to a multi-agent approach for collaborative navigation. The integration of approximate techniques for AHPF acquisition further improves the computational complexity of the proposed solution. The control scheme and the technical results are validated through high-fidelity simulations, where all aspects, from sensing and dynamics to control, are incorporated, demonstrating the capacity of our method in successfully tackling the multi-agent exploration task. © 2022 by the authors

Formation Control and Target Interception for Multiple Multi-rotor Aerial Vehicles

In this paper, we present a distance-based formation control strategy for multiple multi-rotor aerial vehicles participating in a target interception mission. The target moves arbitrarily, following a smooth 3D trajectory, while the intercepting vehicles aim at establishing a predefined enclosing formation around it, by attaining specific distances among them and simultaneously avoiding inter-agent collisions and connectivity breaks. More specifically, we propose a decentralized motion control protocol based on the prescribed performance control notion, which is of low computational complexity and is able to achieve robust and accurate formation establishment. Each agent requires local and relative state feedback, which can be acquired by a common onboard sensor suite without necessitating for explicit network communication. A realistic simulation study with one target and four multi-rotor interceptors was conducted to prove the efficacy of the proposed strategy. © 2020 IEEE