Backflipping With Miniature Quadcopters by Gaussian-Process-Based Control and Planning

This article proposes two control methods for performing a backflip maneuver with miniature quadcopters. First, an existing feedforward control approach is improved by finding the optimal sequence of motion primitives via Bayesian optimization, using a surrogate Gaussian process (GP) model. To evaluate the cost function, the flip maneuver is performed repeatedly in a simulation environment. The second method is based on closed-loop control and it consists of two main steps: first, a novel robust, adaptive controller is designed to provide reliable reference tracking even in case of model uncertainties. The controller is constructed by augmenting the nominal model of the drone with a GP that is trained using measurement data. Second, an efficient trajectory planning algorithm is proposed, which designs feasible trajectories for the flip maneuver using only quadratic programming. The two approaches are analyzed in simulations and in real experiments using Bitcraze Crazyflie 2.1 quadcopters

Backflipping With Miniature Quadcopters by Gaussian-Process-Based Control and Planning

This article proposes two control methods for performing a backflip maneuver with miniature quadcopters. First, an existing feedforward control approach is improved by finding the optimal sequence of motion primitives via Bayesian optimization, using a surrogate Gaussian process (GP) model. To evaluate the cost function, the flip maneuver is performed repeatedly in a simulation environment. The second method is based on closed-loop control and it consists of two main steps: first, a novel robust, adaptive controller is designed to provide reliable reference tracking even in case of model uncertainties. The controller is constructed by augmenting the nominal model of the drone with a GP that is trained using measurement data. Second, an efficient trajectory planning algorithm is proposed, which designs feasible trajectories for the flip maneuver using only quadratic programming. The two approaches are analyzed in simulations and in real experiments using Bitcraze Crazyflie 2.1 quadcopters

International Summerschool Computer Science 2014: Proceedings of Summerschool 7.7. - 13.7.2014

Proceedings of International Summerschool Computer Science 201

Sampling-Based Optimization for Multi-Agent Model Predictive Control

We systematically review the Variational Optimization, Variational Inference and Stochastic Search perspectives on sampling-based dynamic optimization and discuss their connections to state-of-the-art optimizers and Stochastic Optimal Control (SOC) theory. A general convergence and sample complexity analysis on the three perspectives is provided through the unifying Stochastic Search perspective. We then extend these frameworks to their distributed versions for multi-agent control by combining them with consensus Alternating Direction Method of Multipliers (ADMM) to decouple the full problem into local neighborhood-level ones that can be solved in parallel. Model Predictive Control (MPC) algorithms are then developed based on these frameworks, leading to fully decentralized sampling-based dynamic optimizers. The capabilities of the proposed algorithms framework are demonstrated on multiple complex multi-agent tasks for vehicle and quadcopter systems in simulation. The results compare different distributed sampling-based optimizers and their centralized counterparts using unimodal Gaussian, mixture of Gaussians, and stein variational policies. The scalability of the proposed distributed algorithms is demonstrated on a 196-vehicle scenario where a direct application of centralized sampling-based methods is shown to be prohibitive

Homography-Based State Estimation for Autonomous Exploration in Unknown Environments

This thesis presents the development of vision-based state estimation algorithms to enable a quadcopter UAV to navigate and explore a previously unknown GPS denied environment. These state estimation algorithms are based on tracked Speeded-Up Robust Features (SURF) points and the homography relationship that relates the camera motion to the locations of tracked planar feature points in the image plane. An extended Kalman filter implementation is developed to perform sensor fusion using measurements from an onboard inertial measurement unit (accelerometers and rate gyros) with vision-based measurements derived from the homography relationship. Therefore, the measurement update in the filter requires the processing of images from a monocular camera to detect and track planar feature points followed by the computation of homography parameters. The state estimation algorithms are designed to be independent of GPS since GPS can be unreliable or unavailable in many operational environments of interest such as urban environments. The state estimation algorithms are implemented using simulated data from a quadcopter UAV and then tested using post processed video and IMU data from flights of an autonomous quadcopter. The homography-based state estimation algorithm was effective, but accumulates drift errors over time due to the relativistic homography measurement of position

Multirotor Design Optimization: The Mechatronic Approach

Doktorgradsavhandling ved Fakultet for teknologi og realfag, Universitetet i Agder, 2015Multirotors such as the more famous quadcopter have been a favoured research object the last years. It is widely used as a flying platform for the hobby enthusiasts, but recently also used more and more by the industry. The multirotor has complex dynamics and requires sensors and a control system in order to fly. To get the desired flight characteristics batteries, motor and the propeller have to be chosen wisely as different combinations create different properties. The usual design approach is to test different combinations of motors and propellers, and based on experience select components that will be closest to the desired flight properties. This thesis presents an optimization method that calculates what hardware to use in order to get closest to the demanded properties. The method will only select from a given database, hence not returning a diameter and pitch of a propeller that are not available. A wide range of criteria can be optimized, examples are dynamics of the motor/propeller, flight dynamics, flight time, payload etc. The optimization routine will also calculate if the better choice is a quadcopter with four propellers, a hexacopter with six or an octocopter with eight propellers. The optimization is not trivial due to the non-linear characteristics of the propeller. A lot of experimental work was done to test the response of the propeller, both for acceleration and deceleration. Theory and experimental work show that the thrust response of the propeller can be more or less equal if the electronic speed controller controls the motor in a special mode. This mode also makes the response of the motor faster than normal. The new design is tested with a new approach for attitude estimation, and a controller operating directly with the result of the estimator. Most of the multirotors use a microcontroller with limited resources as the control system, hence the attitude and controller were designed specifically without time consuming trigonometric functions such as the sine and cosine. Overall, the methods and results presented in this thesis will aid the engineer when designing a multirotor system consisting of control system, mechanical frame, battery, actuators and propellers