Learning Hybrid Policies for MPC with Application to Drone Flight in Unknown Dynamic Environments
National Key Lab of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology（北京理工大学自主智能无人系统全国重点实验室）；Beijing Institute of Technology Chongqing Innovation Center（北京理工大学重庆创新中心）；Department of Control Science and Engineering, Tongji University（同济大学控制科学与工程系）
Zhaohan Feng, Jie Chen, Wei Xiao, Jian Sun, Bin Xin, Gang Wang
In recent years, drones have found increased applications in a wide array of real-world tasks. Model predictive control (MPC) has emerged as a practical method for drone flight control, owing to its robustness against modeling errors/uncertainties and external disturbances. However, MPC's sensitivity to manually tuned parameters can lead to rapid performance degradation when faced with unknown environmental dynamics. This paper addresses the challenge of controlling a drone as it traverses a swinging gate characterized by unknown dynamics. This paper introduces a parameterized MPC approach named hyMPC that leverages high-level decision variables to adapt to uncertain environmental conditions. To derive these decision variables, a novel policy search framework aimed at training a high-level Gaussian policy is presented. Subsequently, we harness the power of neural network policies, trained on data gathered through the repeated execution of the Gaussian policy, to provide real-time decision variables. The effectiveness of hyMPC is validated through numerical simulations, achieving a 100\% success rate in 20 drone flight tests traversing a swinging gate, demonstrating its capability to achieve safe and precise flight with limited prior knowledge of environmental dynamics.
Flight through Narrow Gaps with Morphing-Wing Drones
Laboratory of Intelligent Systems, École Polytechnique Fédérale de Lausanne (EPFL)（智能系统实验室，瑞士联邦理工学院洛桑分校）；Avian-Insect Robotics in Motion Laboratory, University of Nevada, Reno, USA（鸟类-昆虫运动机器人实验室，内华达大学里诺分校）
Policy Search and Model Predictive Control~(MPC) are two different paradigms for robot control: policy search has the strength of automatically learning complex policies using experienced data, while MPC can offer optimal control performance using models and trajectory optimization. An open research question is how to leverage and combine the advantages of both approaches. In this work, we provide an answer by using policy search for automatically choosing high-level decision variables for MPC, which leads to a novel policy-search-for-model-predictive-control framework. Specifically, we formul…
Safe and precise reference tracking is a crucial characteristic of MAVs that have to operate under the influence of external disturbances in cluttered environments. In this paper, we present a NMPC that exploits the fully physics based non-linear dynamics of the system. We furthermore show how the moment and thrust control inputs can be transformed into feasible actuator commands. In order to guarantee safe operation despite potential loss of a motor under which we show our system keeps operating safely, we developed an EKF based motor failure identification algorithm. We verify the effectiven…
Mapless Collision-Free Flight via MPC using Dual KD-Trees in Cluttered Environments
Shanghai Key Laboratory of Navigation and Location-Based Services, School of Sensing Science and Engineering, Shanghai Jiao Tong University（上海导航与位置服务重点实验室，传感科学与工程学院，上海交通大学）
A Multi-Simulation Approach with Model Predictive Control for Anafi Drones
University of Stuttgart, Faculty of Aerospace Engineering and Geodesy, Institute of Flight Mechanics and Control (iFR), Flight Robotics and Perception Group (FRPG)（斯图加特大学航空航天工程与大地测量学院飞行力学与控制研究所飞行机器人与感知组）；Max Planck Institute for Intelligent Systems（智能系统马克斯·普朗克研究所）
College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院）；Huzhou Institute, Zhejiang University（浙江大学湖州研究院）
In this paper, we describe a robust multi-drone planning framework for high-speed trajectories in large scenes. It uses a free-space-oriented map to free the optimization from cumbersome environment data. A capsule-like safety constraint is designed to avoid reciprocal collisions when vehicles deviate from their nominal flight progress under disturbance. We further show the minimum-singularity differential flatness of our drone dynamics with nonlinear drag effects involved. Leveraging the flatness map, trajectory optimization is efficiently conducted on the flat outputs while still subject to …
Hong Kong University of Science and Technology（香港科技大学）
In this paper, we propose a complete and robust motion planning system for the aggressive flight of autonomous quadrotors. The proposed method is built upon on a classical teach-and-repeat framework, which is widely adopted in infrastructure inspection, aerial transportation, and search-and-rescue. For these applications, human's intention is essential to decide the topological structure of the flight trajectory of the drone. However, poor teaching trajectories and changing environments prevent a simple teach-and-repeat system from being applied flexibly and robustly. In this paper, instead of…
Optimum Semiconductor Technologies Inc.（Optimum Semiconductor Technologies 公司）；University of Science and Technology Beijing（北京科技大学）
Unmanned aerial systems capable of beyond visual line of sight operation can be organized into a top-down hierarchy of layers including flight supervision, command and control, simulation of systems, operating systems, and physical hardware. Flight supervision includes unmanned air traffic management, flight planning, authorization, and remote identification. Command and control ensure drones can be piloted safely. Simulation of systems concerns how drones may react to different environments and how changing conditions and information provide input to a piloting system. Electronic hardware con…
We propose a novel framework for swarm-based drone delivery services with in-flight energy recharging. The framework aims to enhance the delivery time of multiple packages by reducing the number of stops and recharging times at intermediate stations. The proposed framework considers various intrinsic and extrinsic delivery constraints. We propose to use support drones whose sole purpose is to recharge other drones in the swarm during their flight. In this respect, we compute the optimal set of optimal support drones to minimize the probability of delivery services and recharging time at the ne…
Ecole Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）
Most human-robot interfaces, such as joysticks and keyboards, require training and constant cognitive effort and provide a limited degree of awareness of the robots state and its environment. Embodied interactions, instead of interfaces, could bridge the gap between humans and robots, allowing humans to naturally perceive and act through a distal robotic body. Establishing an embodied interaction and mapping human movements and a non-anthropomorphic robot is particularly challenging. In this paper, we describe a natural and immersive embodied interaction that allows users to control and experi…
Hellenic Open University（希腊开放大学）；University of Patras（帕特雷大学）
Commercial weather stations can effectively collect weather data for a specified area. However, their ground sensors limit the amount of data that can be logged, thus failing to collect precise meteorological data in a local area such as a micro-scale region. This happens because weather conditions at a micro-scale region can vary greatly even with small altitude changes. For now, drone operators must check the local weather conditions to ensure a safe and successful flight. This task is often a part of pre-flight preparations. Since flight conditions (and most important flight safety) are gre…
Ecole Polytechnique Federale de Lausanne（洛桑联邦理工学院）；The University of Electro-Communications（电气通信大学）；Wageningen University and Research（瓦赫宁根大学及研究中心）
Drones have shown to be useful aerial vehicles for unmanned transport missions such as food and medical supply delivery. This can be leveraged to deliver life-saving nutrition and medicine for people in emergency situations. However, commercial drones can generally only carry 10 % - 30 % of their own mass as payload, which limits the amount of food delivery in a single flight. One novel solution to noticeably increase the food-carrying ratio of a drone, is recreating some structures of a drone, such as the wings, with edible materials. We thus propose a drone, which is no longer only a food tr…
The weight, space, and power limitations of small aerial vehicles often prevent the application of modern control techniques without significant model simplifications. Moreover, high-speed agile behavior, such as that exhibited in drone racing, make these simplified models too unreliable for safety-critical control. In this work, we introduce the concept of time-varying backup controllers (TBCs): user-specified maneuvers combined with backup controllers that generate reference trajectories which guarantee the safety of nonlinear systems. TBCs reduce conservatism when compared to traditional ba…
This paper details the theory and implementation behind practically ensuring safety of remotely piloted racing drones. We demonstrate robust and practical safety guarantees on a 7" racing drone at speeds exceeding 100 km/h, utilizing only online computations on a 10 gram micro-controller. To achieve this goal, we utilize the framework of control barrier functions (CBFs) which give guaranteed safety encoded as forward set invariance. To make this methodology practically applicable, we present an implicitly defined CBF which leverages backup controllers to enable gradient-free evaluations that e…
College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院）
In this paper, we propose an efficient, receding horizon, local adaptive low-level planner as the middle layer between our original planner and controller. Our method is named as corridor-based model predictive contouring control (CMPCC) since it builds upon on MPCC and utilizes the flight corridor as hard safety constraints. It optimizes the flight aggressiveness and tracking accuracy simultaneously, thus improving our system's robustness by overcoming unmeasured disturbances. Our method features its online flight speed optimization, strict safety and feasibility, and real-time performance, a…
University of Zurich（苏黎世大学）；ETH Zurich（苏黎世联邦理工学院）；Intel Labs（英特尔实验室）
Autonomous agile flight brings up fundamental challenges in robotics, such as coping with unreliable state estimation, reacting optimally to dynamically changing environments, and coupling perception and action in real time under severe resource constraints. In this paper, we consider these challenges in the context of autonomous, vision-based drone racing in dynamic environments. Our approach combines a convolutional neural network (CNN) with a state-of-the-art path-planning and control system. The CNN directly maps raw images into a robust representation in the form of a waypoint and desired…
University of Bologna（博洛尼亚大学）；KU Leuven（荷语鲁汶大学）；Dalle Molle Institute for Artificial Intelligence（达尔·莫勒人工智能研究所）；ETH Zürich（苏黎世联邦理工学院）
Nano-sized drones, with palm-sized form factor, are gaining relevance in the Internet-of-Things ecosystem. Achieving a high degree of autonomy for complex multi-objective missions (e.g., safe flight, exploration, object detection) is extremely challenging for the onboard chip-set due to tight size, payload (<10g), and power envelope constraints, which strictly limit both memory and computation. Our work addresses this complex problem by combining bio-inspired navigation policies, which rely on time-of-flight distance sensor data, with a vision-based convolutional neural network (CNN) for objec…
The off-the-shelf drones are simple to operate and easy to maintain aerial systems. However, due to proprietary flight software, these drones usually do not provide any open-source interface which can enable them for autonomous flight in research or teaching. This work introduces a package for ROS1 and ROS2 for straightforward interfacing with off-the-shelf drones from the Parrot ANAFI family. The developed ROS package is hardware agnostic, allowing connecting seamlessly to all four supported drone models. This framework can connect with the same ease to a single drone or a team of drones from…
Morphing multirotors, such as the Foldable Drone , can increase the versatility of drones employing in-flight-adaptive-morphology. To further increase precision in their tasks, recent works have investigated stable flight in asymmetric morphologies mainly leveraging the low-level controller. However, the aerodynamic effects embedded in multirotors are only analyzed in fixed shape aerial vehicles and are completely ignored in morphing drones. In this paper, we investigate the effects of the partial overlap between the propellers and the main body of a morphing quadrotor. We perform experiments …
This paper presents a new framework to use images as the inputs for the controller to have autonomous flight, considering the noisy indoor environment and uncertainties. A new Proportional-Integral-Derivative-Accelerated (PIDA) control with a derivative filter is proposed to improves drone/quadcopter flight stability within a noisy environment and enables autonomous flight using object and depth detection techniques. The mathematical model is derived from an accurate model with a high level of fidelity by addressing the problems of non-linearity, uncertainties, and coupling. The proposed PIDA …
Autonomous missions of drones require continuous and reliable estimates for the drone's attitude, velocity, and position. Traditionally, these states are estimated by applying Extended Kalman Filter (EKF) to Accelerometer, Gyroscope, Barometer, Magnetometer, and GPS measurements. When the GPS signal is lost, position and velocity estimates deteriorate quickly, especially when using low-cost inertial sensors. This paper proposes an estimation method that uses a Recurrent Neural Network (RNN) to allow reliable estimation of a drone's position and velocity in the absence of GPS signal. The RNN is…
Drone swarms are required for the simultaneous delivery of multiple packages. We demonstrate a multi-stop drone swarm-based delivery in a smart city. We leverage formation flying to conserve energy and increase the flight range of a drone swarm. An adaptive formation is presented in which a swarm adjusts to extrinsic constraints and changes the formation pattern in-flight. We utilize the existing building rooftops in a city and build a line-of-sight skyway network to safely operate the swarms. We use a heuristic-based A* algorithm to route a drone swarm in a skyway network.
Zhejiang University（浙江大学）；Zhejiang University of Technology（浙江工业大学）；Northwestern Polytechnical University（西北工业大学）
Autonomous drone racing is becoming an excellent platform to challenge quadrotors' autonomy techniques including planning, navigation and control technologies. However, most research on this topic mainly focuses on single drone scenarios. In this paper, we describe a novel time-optimal trajectory generation method for generating time-optimal trajectories for a swarm of quadrotors to fly through pre-defined waypoints with their maximum maneuverability without collision. We verify the method in the Gazebo simulations where a swarm of 5 quadrotors can fly through a complex 6-waypoint racing track…
Humans race drones faster than algorithms, despite being limited to a fixed camera angle, body rate control, and response latencies in the order of hundreds of milliseconds. A better understanding of the ability of human pilots of selecting appropriate motor commands from highly dynamic visual information may provide key insights for solving current challenges in vision-based autonomous navigation. This paper investigates the relationship between human eye movements, control behavior, and flight performance in a drone racing task. We collected a multimodal dataset from 21 experienced drone pil…
Quadrotors are among the most agile flying robots. However, planning time-optimal trajectories at the actuation limit through multiple waypoints remains an open problem. This is crucial for applications such as inspection, delivery, search and rescue, and drone racing. Early works used polynomial trajectory formulations, which do not exploit the full actuator potential because of their inherent smoothness. Recent works resorted to numerical optimization but require waypoints to be allocated as costs or constraints at specific discrete times. However, this time allocation is a priori unknown an…
EcoFlight: Finding Low-Energy Paths Through Obstacles for Autonomous Sensing Drones
Computer Science New Jersey Institute of Technology Newark, NJ, USA（计算机科学 新 jersey 理工学院 新ARK, NJ, USA）；Computer Engineering New York Institute of Technology New York, NY, USA（电子与计算机工程 新 york 理工学院 新 YORK, NY, USA）
