A flow disturbance estimation and rejection strategy for multirotors with round-trip trajectories

This paper presents a round-trip strategy of multirotors subject to unknown flow disturbances. During the outbound flight, the vehicle immediately utilizes the wind disturbance estimations in feedback control, as an attempt to reduce the tracking error. During this phase, the disturbance estimations with respect to the position are also recorded for future use. For the return flight, the disturbances previously collected are then routed through a feedforward controller. The major assumption here is that the disturbances may vary over space, but not over time during the same mission. We demonstrate the effectiveness of this feedforward strategy via experiments with two different types of wind flows; a simple jet flow and a more complex flow. To use as a baseline case, a cascaded PD controller with an additional feedback loop for disturbance estimation was employed for outbound flights. To display our contributions regarding the additional feedforward approach, an additional feedforward correction term obtained via prerecorded data was integrated for the return flight. Compared to the baseline controller, the feedforward controller was observed to produce 43% less RMSE position error at a vehicle ground velocity of 1 m/s with 6 m/s of environmental wind velocity. This feedforward approach also produced 14% less RMSE position error for the complex flows as well

작업용 거대 비행 스켈레톤 시스템을 위한 분산 IMU 사용 접촉력 추정 기법 개발

학위논문(석사) -- 서울대학교대학원 : 공과대학 기계공학부, 2021.8. 이동준.In this paper, we propose the novel external wrench estimator for large-size aerial skeleton system with distributed rotor action (LASDRA) which exploits multiple distributed IMUs. The proposed wrench estimator reduces the delay of a conventional momentum-base observer, which using only velocity information, by fuse the acceleration information from distributed IMUs with Kalman Filter. Also, angular acceleration estimation utilizes multiple IMU signals are proposed to improve external torque estimation accuracy. The delay reduction of the proposed wrench estimator was verified in simulation and also experiment verification at 3-link LASDRA is proceed.본 논문에서는 분산배치된 다중 IMU를 활용한 공중작업용 분산로터기반 거대 스켈레톤 시스템(large-size aerial skeleton system with distributed rotor actuation (LASDRA))의 외력 추정 기법을 제시하였다. 속도 정보만을 사용하는 기존의 모멘텀기반 외력 관측기를 다중/분산 IMU에서 측정된 가속도 정보를 융합하여 외력추정의 정확도와 성능을 향상 시켰으며, 각가속도 또한 다중 IMU 신호를 활용 추정하여 외력 토크 추정 정확도 또한 향상 시켰다. 제시된 외력 추정기는 시뮬레이션 상에서 모멘텀기반 외력 관측기와 함께 적용되어 가속도 정보를 사용하여 외력 추정의 딜레이가 줄어드는것을 확인하였으며, 3개 링크로 구성된 LASDRA의 외력을 추정하는 실험을 진행하여 실제 환경에서도 사용 가능함을 확인하였다.1 Introduction 1 1.1 Motivation 1 1.2 Related Works 4 1.3 Contribution 6 2 Preliminary 7 2.1 LASDRA System 7 2.1.1 System Description 7 2.1.2 Estimation of LASDRA 9 2.2 Wrench estimator 10 2.2.1 Dynamics 10 2.2.2 Inverse dynamics 11 2.2.3 Momentum based observer 11 3 Wrench estimator algorithm 13 3.1 Wrench estimation of each link 14 3.1.1 Sensor measurements 14 3.1.2 Wrench estimator pipeline 15 3.1.3 Pixhawk built-in EKF 16 3.1.4 Angular acceleration estimation 16 3.1.5 Wrench estimator 18 3.2 Wrench propagation 20 4 Simulation Results 22 4.1 Simulation setup 22 4.2 Angular acceleration estimation 24 4.3 External wrench estimation 26 5 Experimental Results 28 5.1 Experiment setup 28 5.1.1 LASDRA setup 30 5.1.2 FT sensors setup 32 5.1.3 Experiments scenario 35 5.2 Wrench estimation results 37 6 Conclusion and Future Work 43 6.1 Conclusion 43 6.2 Future work 45 Acknowledgements 50석

Fast, Autonomous Flight in GPS-Denied and Cluttered Environments

One of the most challenging tasks for a flying robot is to autonomously navigate between target locations quickly and reliably while avoiding obstacles in its path, and with little to no a-priori knowledge of the operating environment. This challenge is addressed in the present paper. We describe the system design and software architecture of our proposed solution, and showcase how all the distinct components can be integrated to enable smooth robot operation. We provide critical insight on hardware and software component selection and development, and present results from extensive experimental testing in real-world warehouse environments. Experimental testing reveals that our proposed solution can deliver fast and robust aerial robot autonomous navigation in cluttered, GPS-denied environments.Comment: Pre-peer reviewed version of the article accepted in Journal of Field Robotic

Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Control

Model-based control requires an accurate model of the system dynamics for precisely and safely controlling the robot in complex and dynamic environments. Moreover, in the presence of variations in the operating conditions, the model should be continuously refined to compensate for dynamics changes. In this paper, we present a self-supervised learning approach that actively models the dynamics of nonlinear robotic systems. We combine offline learning from past experience and online learning from current robot interaction with the unknown environment. These two ingredients enable a highly sample-efficient and adaptive learning process, capable of accurately inferring model dynamics in real-time even in operating regimes that greatly differ from the training distribution. Moreover, we design an uncertainty-aware model predictive controller that is heuristically conditioned to the aleatoric (data) uncertainty of the learned dynamics. This controller actively chooses the optimal control actions that (i) optimize the control performance and (ii) improve the efficiency of online learning sample collection. We demonstrate the effectiveness of our method through a series of challenging real-world experiments using a quadrotor system. Our approach showcases high resilience and generalization capabilities by consistently adapting to unseen flight conditions, while it significantly outperforms classical and adaptive control baselines

A survey on fractional order control techniques for unmanned aerial and ground vehicles

In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade

Navigation and Autonomous Control of a Hexacopter in Indoor Environments

This thesis presents methods for estimation and autonomous control of a hexacopter which is an unmanned aerial vehicle with six rotors. The hexacopter used is a ArduCopter 3DR Hexa B and the work follows a model-based approach using Matlab Simulink, running the model on a PandaBoard ES after automatic code generation. The main challenge will be to investigate how data from an Internal Measurement Unit can be used to aid an already implemented computer vision algorithm in a GPS-denied environment. First a physical representation is created by Newton-Euler formalism to be used as a base when developing algorithms for estimation and control. To estimate the position and velocity of the hexacopter, an unscented Kalman filter is implemented for sensor fusion. Sensor fusion is the combining of data from different sensors to receive better results than if the sensors would have been used individually. Control strategies for vertical and horizontal movement are developed using cascaded PID control. These high level controllers feed the ArduCopter with setpoints for low level control of angular orientation and throttle. To test the algorithms in a safe way a simulation model is used where the real system is replaced by blocks containing a mix of differential equations and transfer functions from system identification. When a satisfying behavior in simulation is achieved, tests on the real system are performed. The result of the improvements made on estimation and control is a more stable flight performance with less drift in both simulation and on the real system. The hexacopter can now hold position for over a minute with low drift. Air turbulence, sensor and computer vision imperfections as well as the absence of a hard realtime system degrades the position estimation and causes drift if movement speed is anything but very slow

Low Speed Flap-bounding in Ornithopters and its Inspiration on the Energy Efficient Flight of Quadrotors

Flap-bounding, a form of intermittent flight, is often exhibited by small birds over their entire range of flight speeds. The purpose of flap-bounding is unclear during low to medium speed (2 - 8 m/s) flight from a mechanical-power perspective: aerodynamic models suggest continuous flapping would require less power output and lower cost of transport. This thesis works towards the understanding of the advantages of flap-bounding and tries to employ the underlining principle to design quadrotor maneuver to improve power efficiency. To explore the functional significance of flap-bounding at low speeds, I measured body trajectory and kinematics of wings and tail of zebra finch (Taeniopygia guttata, N=2) during flights in a laboratory between two perches. The flights consist of three phases: initial, descending and ascending. Zebra finch first accelerated using continuous flapping, then descended, featuring intermittent bounds. The flight was completed by ascending using nearly-continuous flapping. When exiting bounds in descending phase, they achieved higher than pre-bound forward velocity by swinging body forward similar to pendulum motion with conserved mechanical energy. Takeoffs of black-capped chickadees (Poecile atricapillus, N=3) in the wild was recorded and I found similar kinematics. Our modeling of power output indicates finch achieves higher velocity (13%) with lower cost of transport (9%) when descending, compared with continuous flapping in previously-studied pigeons. To apply the findings to the design of quadrotor motion, a mimicking maneuver was developed that consisted of five phases: projectile drop, drop transition, pendulum swing, rise transition and projectile rise. The quadrotor outputs small amount (4 N) of thrust during projectile drop phase and ramps up the thrust while increasing body pitch angle during the drop transition phase until the thrust enables the quadrotor to advance in pendulum-like motion in the pendulum swing phase. As the quadrotor reaches the symmetric point with respect to the vertical axis of the pendulum motion, it engages in reducing the thrust and pitch angle during the rise transition phase until the thrust is lowered to the same level as the beginning of the maneuver and the body angle of attack minimized (0.2 deg) in the projectile rise phase. The trajectory of the maneuver was optimized to yield minimum cost of transport. The quadrotor moves forward by tracking the cycle of the optimized trajectory repeatedly. Due to the aggressive nature of the maneuver, we developed new algorithms using onboard sensors to determine the estimated position and attitude. By employing nonlinear controller, we showed that cost of transport of the flap-bounding inspired maneuver is lower (28%) than conventional constant forward flight, which makes it the preferable strategy in high speed flight (≥15 m/s)

LEARNEST: LEARNing Enhanced Model-based State ESTimation for Robots using Knowledge-based Neural Ordinary Differential Equations

State estimation is an important aspect in many robotics applications. In this work, we consider the task of obtaining accurate state estimates for robotic systems by enhancing the dynamics model used in state estimation algorithms. Existing frameworks such as moving horizon estimation (MHE) and the unscented Kalman filter (UKF) provide the flexibility to incorporate nonlinear dynamics and measurement models. However, this implies that the dynamics model within these algorithms has to be sufficiently accurate in order to warrant the accuracy of the state estimates. To enhance the dynamics models and improve the estimation accuracy, we utilize a deep learning framework known as knowledge-based neural ordinary differential equations (KNODEs). The KNODE framework embeds prior knowledge into the training procedure and synthesizes an accurate hybrid model by fusing a prior first-principles model with a neural ordinary differential equation (NODE) model. In our proposed LEARNEST framework, we integrate the data-driven model into two novel model-based state estimation algorithms, which are denoted as KNODE-MHE and KNODE-UKF. These two algorithms are compared against their conventional counterparts across a number of robotic applications; state estimation for a cartpole system using partial measurements, localization for a ground robot, as well as state estimation for a quadrotor. Through simulations and tests using real-world experimental data, we demonstrate the versatility and efficacy of the proposed learning-enhanced state estimation framework.Comment: 7 pages, 3 figures, 1 tabl