Augmenting Ultra-Wideband Localization with Computer Vision for Accurate Flight

Ultra-wideband radio networks enable low-cost, low-computation robot localization in semi-structured environments; however, previous results have shown that these localization systems suffer from spatially-varying measurement biases, leading to a spatially-varying offset between the physical and the estimated position. In tasks where absolute positioning or high tracking accuracy is required, this offset can lead to failure of the task. This paper proposes augmenting ultra-wideband-based localization with visual localization to improve estimation accuracy for critical tasks. It also presents a control strategy that takes the camera measurement process into account, and allows the ultra-wideband system’s measurement biases to be learned and compensated over multiple executions of the task. This bias compensation can be used to improve the accuracy of the task in the case of visual impairment. The effectiveness of the proposed framework is demonstrated by accurately flying a quadrocopter to a landing platform using on-board estimation and control.


System Identification of the Crazyflie 2.0 Nano Quadrocopter

Accurate control and state estimation require an accurate mathematical model of a system. This Bachelor thesis takes a first principles approach to the modelling and identification of the major parameters that govern the flight of a Crazyflie 2.0 nano-quadrocopter. These parameters have since been incorporated into the Crazyflie 2.0 as part of it’s extended Kalman filter, non-linear controller and model-based power-distribution framework.


A Robot Self-Localization System using One-Way Ultra-Wideband Communication

A robot localization system is presented that enables a robot to estimate its position within some space by passively receiving ultra-wideband radio signals from fixed-position modules. Communication from the fixed-position modules is one-way, allowing the system to scale to multiple robots. Furthermore, the system’s high position update rate makes it suitable to be used in a feedback control system, and enables the robot to track and perform high-speed, dynamic motions.

This paper describes the algorithmic underpinnings of the system, discusses design decisions and their impact on the performance of the resulting localization, and highlights challenges faced during implementation. Performance of the localization system is experimentally verified through comparison with data from a motion-capture system. Finally, the system’s application to robot self-localization is demonstrated through integration with a quadrocopter.


Fusing ultra-wideband range measurements with accelerometers and rate gyroscopes for quadrocopter state estimation

A state estimator for a quadrocopter is presented, using measurements from an accelerometer, angular rate gyroscope, and a set of ultra-wideband ranging radios. The estimator uses an extended aerodynamic model for the quadrocopter, where the full 3D airspeed is observable through accelerometer measurements. The remaining quadrocopter states, including the yaw orientation, are rendered observable by fusing ultra-wideband range measurements, under the assumption of no wind. The estimator is implemented on a standard microcontroller using readily-available, low-cost sensors. Performance is experimentally investigated in a variety of scenarios, where the quadrocopter is flown under feedback control using the estimator output.

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The Flight Assembled Architecture Installation: Cooperative construction with flying machines

The art installation “Flight Assembled Architecture” is one of the first structures built by flying vehicles. Culminating in a 6m-tall tower composed of 1500 foam modules, the installation was assembled by four quadrocopters in 18 hours during a four-day-long live exhibition at the Regional Contemporary Art Fund Center in Orléans, France. This article documents the design and development of specific elements of the autonomous system behind this one-of-a-kind installation and describes the process and challenges of bringing such a complex system out of the laboratory and into the public realm, where live demonstration and human-in-the-loop interaction demand high levels of robustness, dependability, and safety. The installation is a 1:100 scale model of what was originally conceived as a 600 m-high vertical village and is an exploration of aerial construction in architecture.


Knowledge Transfer for High-Performance Quadrocopter Maneuvers

Iterative Learning Control algorithms are based on the premise that “practice makes perfect”. By iteratively performing an action, repetitive errors can be learned and accounted for in subsequent iterations, in a non-causal and feed-forward manner. This method has been previously implemented for a quadrocopter system, enabling the quadrocopter to learn to accurately track high-performance slalom trajectories. However, one major limitation of this system is that knowledge from previously learned trajectories is not generalized or transferred to new trajectories; these must be learned from a state of zero experience.

This paper experimentally shows that the major dynamics of the Iterative Learning Control process can be captured by a linear map, trained on previously learned slalom trajectories. This map enables this prior knowledge to be used to improve the initialization of an unseen trajectory. Experimental results show that prediction based on a single prior is enough to reduce the initial tracking error for an unseen trajectory by an order of magnitude.