Deep Reinforcement Learning based Continuous Control for Multicopter Systems

In this paper we apply deep reinforcement learning techniques on a multicopter for learning a stable hovering task in a continuous action state environment. We present a framework based on OpenAI GYM, Gazebo and RotorS MAV simulator, utilized for successfully training different agents to perform various tasks. The deep reinforcement learning method used for the training is model-free, on-policy, actor-critic based algorithm called Trust Region Policy Optimization (TRPO). Two neural networks have been used as a nonlinear function approximators. Our experiments showed that such learning approach achieves successful results, and facilitates the process of controller design

Real-time graph-based SLAM in unknown environments using a small UAV

Autonomous navigation of small Unmanned Aerial Vehicles (UAVs) in cluttered environments is still a challenging problem. In this work, we present an approach based on graph slam and loop closure detection for online mapping of unknown outdoor environments using a small UAV. Here, we used an onboard front facing stereo camera as the primary sensor. The data extracted by the cameras are used by the graph-based slam algorithm to estimate the position and create the graph-nodes and construct the map. To avoid multiple detections of one object as different objects and to identify re-visited locations, a loop closure detection is applied with optimization algorithm using the g2o toolbox to minimize the error. Furthermore, 3D occupancy map is used to represent the environment. This technique is used to save memory and computational time for the online processing. Real experiments are conducted in outdoor cluttered and open field environments.The experiment results show that our presented approach works under real time constraints, with an average time to process the nodes of the 3D map is 17.79ms

Modeling and Control of Aerial Manipulation Vehicle with Visual sensor

Modeling and control of a Quadrotor with robotic arm which uses vision sensor is discussed. A quadrotor model coupled with a two link manipulator is first developed and then the integrated control mechanism is investigated. An Image Based Visual Servo system is introduced and then used with the aerial manipulator to successfully perform specific tasks of positioning and stabilization during manipulation

Model predictive cooperative localization control of multiple UAVs using potential function sensor constraints: a workflow to create sensor constraint based potential functions for the control of cooperative localization scenarios with mobile robots.

The global localization of multiple mobile robots can be achieved cost efficiently by localizing one robot globally and the others in relation to it using local sensor data. However, the drawback of this cooperative localization is the requirement of continuous sensor information. Due to a limited sensor perception space, the tracking task to continuously maintain this sensor information is challenging. To address this problem, this contribution is presenting a model predictive control (MPC) approach for such cooperative localization scenarios. In particular, the present work shows a novel workflow to describe sensor limitations with the help of potential functions. In addition, a compact motion model for multi-rotor drones is introduced to achieve MPC real-time capability. The effectiveness of the presented approach is demonstrated in a numerical simulation, an experimental indoor scenario with two quadrotors as well as multiple indoor scenarios of a quadrotor obstacle evasion maneuver

Evasive Maneuvering for UAVs: An MPC Approach

Flying autonomously in a workspace populated by obstacles is one of the main goals when working with Unmanned Aerial Vehicles (UAV). To address this challenge, this paper presents a model predictive flight controller that drives the UAV through collision-free trajectories to reach a given pose or follow a way-point path. The major advantage of this approach lies on the inclusion of three-dimensional obstacle avoidance in the control layer by adding ellipsoidal constraints to the optimal control problem. The obstacles can be added, moved and resized online, providing a way to perform waypoint navigation without the need of motion planning. In addition, the delays of the system are considered in the prediction by an experimental first order with delay model of the system. Successful experiments in 3D path tracking and obstacle avoidance validates its effectiveness for sense-and-avoid and surveillance applications presenting the proper structure to extent its autonomy and applications

Real time degradation identification of UAV using machine learning techniques

The usages and functionalities of Unmanned Aerial Vehicles (UAV) have grown rapidly during the last years. They are being engaged in many types of missions, ranging from military to agriculture passing by entertainment and rescue or even delivery. Nonetheless, for being able to perform such tasks, UAVs have to navigate safely in an often dynamic and partly unknown environment. This brings many challenges to overcome, some of which can lead to damages or degradations of different body parts. Thus, new tools and methods are required to allow the successful analysis and identification of the different threats that UAVs have to manage during their missions or flights. Various approaches, addressing this domain, have been proposed. However, most of them typically identify the changes in the UAVs behavior rather than the issue. This work presents an approach, which focuses not only on identifying degradations of UAVs during flights, but estimate the source of the failure as well

Advances in Control Techniques for Floating Platform Stabilization in the Zero-G Lab

peer reviewedThe study presents a novel control approach for managing floating platforms in the unique environment of a zero-gravity laboratory (Zero-G Lab) of University of Luxembourg. These platforms are pivotal for diverse experiments and technologies in space. Our solution combines Model Predictive Control (MPC) and Proportional-Derivative (PD) control techniques to ensure precise positioning and stability. The MPC algorithm generates optimal trajectories based on predictive platform models, adjusting paths for minimal effort. Augmented by a PD controller using feedback from the Optitrack motion system, real-time adjustments maintain stability by considering platform state, position, and orientation data. Extensive simulations and experiments within the Zero-G Lab demonstrate the effectiveness of our approach. The MPC-PD strategy accurately controls platforms, making them resilient against external disturbances and human interactions. This strategy holds promise for space exploration, microgravity experiments, and beyond, offering adaptable control in zero-gravity conditions

A real-time model predictive position control with collision avoidance for commercial low-cost quadrotors

Unmanned aerial vehicles (UAVs) are the future technology for autonomous fast transportation of individual goods. They have the advantage of being small, fast and not to be limited to the local infrastructure. This is not only interesting for delivery of private consumption goods up to the doorstep, but also particularly for smart factories. One drawback of autonomous drone technology is the high development costs, that limit research and development to a small audience. This work is introducing a position control with collision avoidance as a first step to make low-cost drones more accessible to the execution of autonomous tasks. The paper introduces a semilinear state-space model for a commercial quadrotor and its adaptation to the commercially available AR.Drone 2 system. The position control introduced in this paper is a model predictive control (MPC) based on a condensed multiple-shooting continuation generalized minimal residual method (CMSCGMRES). The collision avoidance is implemented in the MPC based on a sigmoid function. The real-time applicability of the proposed methods is demonstrated in two experiments with a real AR.Drone quadrotor, adressing position tracking and collision avoidance. The experiments show the computational efficiency of the proposed control design with a measured maximum computation time of less than 2ms

Lightweight robotic arm actuated by Shape Memory Alloy (SMA) Wires

The current paper discusses the design, modeling and control of a Light weight robotic arm actuated by Shape Memory Alloy (SMA) actuators, usable for applications such as Aerial Manipulator. Compared to servo motor based robotic arm the proposed design has an added advantage of light weight and high force to mass ratio, but further introduces the problem of nonlinearities such as Hysteresis into the system. A nonlinear dynamic model of the hysteretic robotic arm is systematically developed to perform closed loop simulations. A Joint Space control is performed using Variable Structure Control and the closed loop performance is successfully verified by simulation studies

A tracking error control approach for model predictive position control of a quadrotor with time varying reference

In mobile robotic applications, a common problem is the following of a given trajectory with a constant velocity. Using standard model predictive control (MPC) for tracking of time varying trajectories leads to a constant tracking error. This problem is modelled in this paper as quadrotor position tracking problem. The presented solution is a computationally light-weight target position control (T PC), that controls the tracking error of MPCs for constantly moving targets. The proposed technique is assessed mathematically in the Laplace domain, in simulation, as well as experimentally on a real quadrotor system