NDLI: Optimization-based iterative learning for precise quadrocopter trajectory tracking

Content Provider	Springer Nature Link
Author	Schoellig, Angela P. Mueller, Fabian L. D’Andrea, Raffaello
Copyright Year	2012
Abstract	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 parameters.
Starting Page	103
Ending Page	127
Page Count	25
File Format	PDF
ISSN	09295593
Journal	Autonomous Robots
Volume Number	33
Issue Number	1-2
e-ISSN	15737527
Language	English
Publisher	Springer US
Publisher Date	2012-04-21
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Motion planning and control Aerial robotics Quadrocopter control Trajectory tracking Learning Input and state constraints Kalman filtering Convex optimization Artificial Intelligence (incl. Robotics) Control, Robotics, Mechatronics Computer Imaging, Vision, Pattern Recognition and Graphics Robotics and Automation
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Decentralized trajectory optimization using virtual motion camouflage and particle swarm optimization

Autonomous robotic capture of non-cooperative target using visual servoing and motion predictive control

Fast motion planning from experience: trajectory prediction for speeding up movement generation

Decentralized probabilistic density control of autonomous swarms with safety constraints

Motion safety and constraints compatibility for multibody robots

Tracking an omnidirectional evader with a differential drive robot

Efficient vision-based navigation : Learning about the influence of motion blur

Towards a swarm of agile micro quadrotors

Trajectory design and control for aggressive formation flight with quadrotors

Optimization-based iterative learning for precise quadrocopter trajectory tracking

Similar Documents

Decentralized trajectory optimization using virtual motion camouflage and particle swarm optimization

Autonomous robotic capture of non-cooperative target using visual servoing and motion predictive control

Fast motion planning from experience: trajectory prediction for speeding up movement generation

Decentralized probabilistic density control of autonomous swarms with safety constraints

Motion safety and constraints compatibility for multibody robots

Tracking an omnidirectional evader with a differential drive robot

Efficient vision-based navigation : Learning about the influence of motion blur

Towards a swarm of agile micro quadrotors

Trajectory design and control for aggressive formation flight with quadrotors

Optimization-based iterative learning for precise quadrocopter trajectory tracking