Towards Time-Optimal Tunnel-Following for Quadrotors
Technical University of Munich（慕尼黑工业大学）
Jon Arrizabalaga, Markus Ryll
备注：
This paper has been accepted for publication at the IEEE International Conference on Robotics and Automation (ICRA), Philadelphia, USA, May 2022. Copyright @ IEEE
Minimum-time navigation within constrained and dynamic environments is of special relevance in robotics. Seeking time-optimality, while guaranteeing the integrity of time-varying spatial bounds, is an appealing trade-off for agile vehicles, such as quadrotors. State of the art approaches, either assume bounds to be static and generate time-optimal trajectories offline, or compromise time-optimality for constraint satisfaction. Leveraging nonlinear model predictive control and a path parametric reformulation of the quadrotor model, we present a real-time control that approximates time-optimal behavior and remains within dynamic corridors. The efficacy of the approach is evaluated according to simulated results, showing itself capable of performing extremely aggressive maneuvers as well as stop-and-go and backward motions.
ETH Zurich（苏黎世联邦理工学院）；Delft University of Technology（代尔夫特理工大学）
In this paper we propose an algorithm for the training of neural network control policies for quadrotors. The learned control policy computes control commands directly from sensor inputs and is hence computationally efficient. An imitation learning algorithm produces a policy that reproduces the behavior of a path following control algorithm with collision avoidance. Due to the generalization ability of neural networks, the resulting policy performs local collision avoidance of unseen obstacles while following a global reference path. The algorithm uses a time-free model predictive path-follow…
International Institute of Information Technology Hyderabad（国际信息技术学院海得拉巴分校）
With the advent of intelligent transport, quadrotors are becoming an attractive solution while lifting or dropping payloads during emergency evacuations, construction works, etc. During such operations, dynamic variations in (possibly unknown) payload cause considerable changes in the system dynamics. However, a systematic control solution to tackle such varying dynamical behaviour is still missing. In this work, two control solutions are proposed to solve two specific problems in aerial transportation of payload, as mentioned below. In the first work, we explore the tracking control problem f…
Ability to recover from faults and continue mission is desirable for many quadrotor applications. The quadrotor's rotor may fail while performing a mission and it is essential to develop recovery strategies so that the vehicle is not damaged. In this paper, we develop a model-free deep reinforcement learning approach for a quadrotor to recover from a single rotor failure. The approach is based on Soft-actor-critic that enables the vehicle to hover, land, and perform complex maneuvers. Simulation results are presented to validate the proposed approach using a custom simulator. The results show …
Electric Vertical Takeoff and Landing (eVTOL) vehicles will open new opportunities in aviation. This paper describes the design and wind tunnel analysis of an eVTOL uncrewed aircraft system (UAS) prototype with a traditional aircraft wing, tail, and puller motor along with four vertical thrust pusher motors. Vehicle design and construction are summarized. Dynamic thrust from propulsion modules is experimentally determined at different airspeeds over a large sweep of propeller angles of attack. Wind tunnel tests with the vehicle prototype cover a suite of hover, transition and cruise flight con…
Accurate path following is challenging for autonomous robots operating in uncertain environments. Adaptive and predictive control strategies are crucial for a nonlinear robotic system to achieve high-performance path following control. In this paper, we propose a novel learning-based predictive control scheme that couples a high-level model predictive path following controller (MPFC) with a low-level learning-based feedback linearization controller (LB-FBLC) for nonlinear systems under uncertain disturbances. The low-level LB-FBLC utilizes Gaussian Processes to learn the uncertain environmenta…
Hong Kong University of Science and Technology（香港科技大学）
Nowadays, multirotors are playing important roles in abundant types of missions. During these missions, entering confined and narrow tunnels that are barely accessible to humans is desirable yet extremely challenging for multirotors. The restricted space and significant ego airflow disturbances induce control issues at both fast and slow flight speeds, meanwhile bringing about problems in state estimation and perception. Thus, a smooth trajectory at a proper speed is necessary for safe tunnel flights. To address these challenges, in this letter, a complete autonomous aerial system that can fly…
In this paper we first contribute a large scale online study (N=400) to better understand aesthetic perception of aerial video. The results indicate that it is paramount to optimize smoothness of trajectories across all keyframes. However, for experts timing control remains an essential tool. Satisfying this dual goal is technically challenging because it requires giving up desirable properties in the optimization formulation. Second, informed by this study we propose a method that optimizes positional and temporal reference fit jointly. This allows to generate globally smooth trajectories, wh…
In order to further expand the flight envelope of quadrotors under actuator failures, we design a nonlinear sensor-based fault-tolerant controller to stabilize a quadrotor with failure of two opposing rotors in the high-speed flight condition (> 8m/s). The incremental nonlinear dynamic inversion (INDI) approach which excels in handling model uncertainties is adopted to compensate for the significant unknown aerodynamic effects. The internal dynamics of such an underactuated system have been analyzed, and subsequently stabilized by re-defining the control output. The proposed method can be gene…
Department of Electronic and Computer Engineering, the Hong Kong University of Science and Technology（电子与计算机工程系，香港科学与技术大学）；School of Intelligent Systems Engineering, SunYat-sen University（智能系统工程学院，中山大学）；Institute of Unmanned Systems, Beihang University（无人系统研究院，北航大学）
Hong Kong University of Science and Technology（香港科技大学）
While deep reinforcement learning (RL) methods have achieved unprecedented successes in a range of challenging problems, their applicability has been mainly limited to simulation or game domains due to the high sample complexity of the trial-and-error learning process. However, real-world robotic applications often need a data-efficient learning process with safety-critical constraints. In this paper, we consider the challenging problem of learning unmanned aerial vehicle (UAV) control for tracking a moving target. To acquire a strategy that combines perception and control, we represent the po…
Nanyang Technological University（南洋理工大学）；Imperial College London（帝国理工学院）；International Institute of Information Technology, Hyderabad（海得拉巴国际信息技术学院）
The expectation of unmanned air vehicles (UAVs) pushes the operation environment to narrow spaces, where the systems may fly very close to an object and perform an interaction. This phase brings the variation in UAV dynamics: thrust and drag coefficient of the propellers might change under different proximity. At the same time, UAVs may need to operate under external disturbances to follow time-based trajectories. Under these challenging conditions, a standard controller approach may not handle all missions with a fixed structure, where there may be a need to adjust its parameters for each dif…
Unmanned aerial vehicles (UAVs) have become very popular for many military and civilian applications including in agriculture, construction, mining, environmental monitoring, etc. A desirable feature for UAVs is the ability to navigate and perform tasks autonomously with least human interaction. This is a very challenging problem due to several factors such as the high complexity of UAV applications, operation in harsh environments, limited payload and onboard computing power and highly nonlinear dynamics. The work presented in this report contributes towards the state-of-the-art in UAV contro…
Recently, Model Predictive Contouring Control (MPCC) has arisen as the state-of-the-art approach for model-based agile flight. MPCC benefits from great flexibility in trading-off between progress maximization and path following at runtime without relying on globally optimized trajectories. However, finding the optimal set of tuning parameters for MPCC is challenging because (i) the full quadrotor dynamics are non-linear, (ii) the cost function is highly non-convex, and (iii) of the high dimensionality of the hyperparameter space. This paper leverages a probabilistic Policy Search method - Weig…
Efficient Collision-Avoidance Constraints for Ellipsoidal Obstacles in Optimal Control: Application to Path-Following MPC and UAVs
Institute of Engineering and Computational Mechanics, University of Stuttgart（工程与计算力学研究所，斯图加特大学）；Chair of Systems Modeling and Simulation, Saarland University（系统建模与仿真教授团，萨尔兰大学）；Department of Mechanical Engineering, LUT University（机械工程系，卢霍斯大学）
Chalmers University of Technology（查尔姆斯理工大学）；Lund University（隆德大学）
This paper proposes a motion control scheme for robots operating in a dynamic environment with concave obstacles. A Model Predictive Controller (MPC) is constructed to drive the robot towards a goal position while ensuring collision avoidance without direct use of obstacle information in the optimization problem. This is achieved by guaranteeing tracking performance of an appropriately designed receding horizon path. The path is computed using a guiding vector field defined in a subspace of the free workspace where each point in the subspace satisfies a criteria for minimum distance to all obs…
Optimizing Control-Friendly Trajectories with Self-Supervised Residual Learning
School of Aeronautic Science and Engineering, Beihang University（北京航空航天大学航空科学与工程学院）；Hangzhou Innovation Institute, Beihang University（北京航空航天大学杭州创新研究院）；School of Automation Science and Electrical Engineering, Beihang University（北京航空航天大学自动化科学与电气工程学院）
