Optimization-based iterative learning for precise quadrocopter trajectory tracking

Current control systems regulate the behavior of dynamic systems by reacting to noise and unexpected disturbances as they occur. To improve the performance of such control systems, experience from iterative executions can be used to anticipate recurring disturbances and proactively compensate for them. This paper presents an algorithm that exploits data from previous repetitions in order to learn to precisely follow a predefined trajectory. We adapt the feed-forward input signal to the system with the goal of achieving high tracking performance—even under the presence of model errors and other recurring disturbances. The approach is based on a dynamics model that captures the essential features of the system and that explicitly takes system input and state constraints into account. We combine traditional optimal filtering methods with state-of-the-art optimization techniques in order to obtain an effective and computationally efficient learning strategy that updates the feed-forward input signal according to a customizable learning objective. It is possible to define a termination condition that stops an execution early if the deviation from the nominal trajectory exceeds a given bound. This allows for a safe learning that gradually extends the time horizon of the trajectory. We developed a framework for generating arbitrary flight trajectories and for applying the algorithm to highly maneuverable autonomous quadrotor vehicles in the ETH Flying Machine Arena testbed. Experimental results are discussed for selected trajectories and different learning algorithm parameter

On-line reinforcement learning for trajectory following with unknown faults

Reinforcement learning (RL) is a key method for providing robots with appropriate control algorithms. Controller blending is a technique for combining the control output of several controllers. In this article we use on-line RL to learn an optimal blending of controllers for novel faults. Since one cannot anticipate all possible fault states, which are exponential in the number of possible faults, we instead apply learning on the effects the faults have on the system. We use a quadcopter pathfollowing simulation in the presence of unknown rotor actuator faults for which the system has not been tuned. We empirically demonstrate the effectiveness of our novel on-line learning framework on a quadcopter trajectory following task with unknown faults, even after a small number of learning cycles. The authors are not aware of any other use of on-line RL for fault tolerant control under unknown faults

Control of underactuated fluid-body systems with real-time particle image velocimetry

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 141-153).Controlling the interaction of a robot with a fluid, particularly when the desired behavior is intimately related to the dynamics of the fluid, is a difficult and important problem. High-performance aircraft cannot ignore nonlinear stall effects, and robots hoping to fly and swim with performance matching that seen in birds and fish cannot treat fluid flows as quasi-steady. If we wish to match the level of performance seen in nature several major hurdles must be overcome, with one of the most difficult being the poor observability of the fluid state. Fluid dynamicists have long contended with this observability problem, and have used computationally intensive Particle Image Velocimetry (PIV) to gain an understanding of the fluid behavior after the fact. However, improvement in available computational power is now making it possible to perform PIV in real-time. When PIV provides real-time awareness of the fluid state it is no longer just an analysis tool, but rather a valuable sensor that can be integrated into the control loop. In this thesis I present methods for controlling fluid-body systems in which the fluid plays a vital dynamical role, for performing real-time PIV, and for interpreting the output of PIV in a manner useful to control. The utility of these methods is demonstrated on a mechanically simple but dynamically rich experimental platform: the hydrodynamic cartpole. This system is analogous to the well-known cart-pole system in the controls literature, but through its relationship with the surrounding fluid it captures many of the fundamental challenges of general fluid-body control tasks, including: nonlinearity, underactuation, an important and unknown fluid state and a dearth of accurate and tractable models. The first complete demonstration of closed-loop PIV control is performed on this system, and there is a statistically significant improvement in the system's ability to reject fluid disturbances when using real-time PIV for closed-loop control. These results suggest that these new techniques will push the boundaries of what we can expect a robot in a fluid to do.by John W. Roberts.Ph.D

Quaternion-Based Control for Aggressive Trajectory Tracking with a Micro-Quadrotor UAV

With potential missions for quadrotor micro-air vehicles (MAVs) calling for smaller, more agile vehicles, it is important to implement attitude controllers that allow the vehicle to reach any desired attitude without encountering computational singularities, as is the case when using an Euler angle representation. A computationally efficient quaternion-based state estimator is presented that enables the Army Research Laboratory's (ARL) 100-gram micro-quadrotor to determine its attitude during agile maneuvers using only an on-board gyroscope and accelerometer and a low-power processor. Inner and outer loop attitude and position controllers are also discussed that use the quaternion attitude representation to control the vehicle along aggressive trajectories with the assistance of an outside motion capture system. A trajectory generation algorithm is then described that leverages the quadrotor's inherent dynamics to allow it to reach extreme attitudes for applications such as perching on walls or ceilings and flying through small openings

Proceedings of the International Micro Air Vehicles Conference and Flight Competition 2017 (IMAV 2017)

The IMAV 2017 conference has been held at ISAE-SUPAERO, Toulouse, France from Sept. 18 to Sept. 21, 2017. More than 250 participants coming from 30 different countries worldwide have presented their latest research activities in the field of drones. 38 papers have been presented during the conference including various topics such as Aerodynamics, Aeroacoustics, Propulsion, Autopilots, Sensors, Communication systems, Mission planning techniques, Artificial Intelligence, Human-machine cooperation as applied to drones

飛行ロボットにおける人間・ロボットインタラクションの実現に向けて : ユーザー同伴モデルとセンシングインターフェース

学位の種別: 課程博士審査委員会委員 : （主査）東京大学准教授 矢入 健久, 東京大学教授 堀 浩一, 東京大学教授 岩崎 晃, 東京大学教授 土屋 武司, 東京理科大学教授 溝口 博University of Tokyo(東京大学