Quadcopter Attitude Control Optimization and Multi-Agent Coordination

This thesis presents a method of automated control gain tuning for a Quadcopter Unmanned Aerial Vehicle and proposes a method of coordination multiple autonomous robotic agents capable for formation aggregation. Sliding Mode Control for Quadcopter altitude and attitude stabilization is presented and tuned using Particle Swarm Optimization. Different configurations for the optimization process are compared to determine an effective and time-efficient setup to complete the control gain tuning. The multi-agent coordination scheme expands upon an existing adjustable swarm framework based on an Artificial Potential Field Sliding Mode Controller. The original leader-follower scheme is modified with the goal of producing a leaderless swarm where agents move towards specific locations to aggregate a desired formation. Analysis of the swarm control scheme pays particular attention to maintaining proper distance between agents. Using Lyapunov methods following that of the original controller analysis, stability under first order and general higher order dynamics is analyzed. Numerical simulations of the swarm controller using agents with nonlinear Quadcopter or second order point mass dynamics are presented to illustrate the capabilities of this algorithm. The automatically tuned Quadcopter controller is used in simulations when applicable. The development of an experimental test platform is discussed with the intention of validating the simulation results on physical Quadcopters

Quadcopter drone formation control via onboard visual perception

Quadcopter drone formation control is an important capability for fields like area surveillance, search and rescue, agriculture, and reconnaissance. Of particular interest is formation control in environments where radio communications and/or GPS may be either denied or not sufficiently accurate for the desired application. To address this, we focus on vision as the sensing modality. We train an Hourglass Convolutional Neural Network (CNN) to discriminate between quadcopter pixels and non-quadcopter pixels in a live video feed and use it to guide a formation of quadcopters. The CNN outputs "heatmaps" - pixel-by-pixel likelihood estimates of the presence of a quadcopter. These heatmaps suffer from short-lived false detections. To mitigate these, we apply a version of the Siamese networks technique on consecutive frames for clutter mitigation and to promote temporal smoothness in the heatmaps. The heatmaps give an estimate of the range and bearing to the other quadcopter(s), which we use to calculate flight control commands and maintain the desired formation. We implement the algorithm on a single-board computer (ODROID XU4) with a standard webcam mounted to a quadcopter drone. Flight tests in a motion capture volume demonstrate successful formation control with two quadcopters in a leader-follower setup

A benchmark mechatronics platform to assess the inspection around pipes with variable pitch quadrotor for industrial sites

Article number 102641This paper investigates the inspection-of-pipe topic in a new framework, by rotation around a pipe, peculiar to industrial sites and refineries. The evolution of the ultimate system requires prototype design and preliminary tests. A new benchmark has been designed and built to mimic the rotation around a pipe, with the main purpose of assessing the different types of rotors and control systems. The benchmark control system presents a mechatronics package including mechanical design and machining, electronics and motor drive, motor-blade installation, computer programming, and control implementation. The benchmark is also modular, working with two modes of one- and two-degree-of-freedom (DoF), easily interchangeable. To cover a full rotation, conventional fixed-pitch drones fail to provide negative thrusts; nonetheless, variable-pitch (VP) rotor quad- copters can produce that in both directions. A closed-loop nonlinear optimal method is chosen as a controller, so- called, “the state-dependent Riccati equation (SDRE)” approach. Optimal control policies are challenging for experimentation though it has been successfully done in this report. The advantage of the VP is also illustrated in a rotation plus radial motion in comparison with fixed-pitch rotors while a wind gust disturbs the inspection task. The proposed VP system compensated the disturbance while the fixed pitch was pushed away by the wind gust. The solution methods to the SDRE were mixed, a closed-form exact solution for the one-DoF system, and a numerical one for the two-DoF. Solving the Riccati online in each time step is a critical issue that was effectively solved by the implementation approach, through online communication with MATLAB software. Both simula- tions and experiments have been performed along with a discussion to prove the application of VP systems in rotary-motion pipe inspectionEuropean Union (UE). H2020 779411Agencia Estatal de Investigación española RTI2018-102224-B-I0