Coordination and navigation of heterogeneous MAV-UGV formations localized by a 'hawk-eye'-like approach under a model predictive control scheme

n approach for coordination and control of 3D heterogeneous formations of unmanned aerial and ground vehicles under hawk-eye-like relative localization is presented in this paper. The core of the method lies in the use of visual top-view feedback from flying robots for the stabilization of the entire group in a leader–follower formation. We formulate a novel model predictive control-based methodology for guiding the formation. The method is employed to solve the trajectory planning and control of a virtual leader into a desired target region. In addition, the method is used for keeping the following vehicles in the desired shape of the group. The approach is designed to ensure direct visibility between aerial and ground vehicles, which is crucial for the formation stabilization using the hawk-eye-like approach. The presented system is verified in numerous experiments inspired by search-and-rescue applications, where the formation acts as a searching phalanx. In addition, stability and convergence analyses are provided to explicitly determine the limitations of the method in real-world applications

A control architecture and human interface for agile, reconfigurable micro aerial vehicle formations

This thesis considers the problem of controlling a group of micro aerial vehicles for agile maneuvering cooperatively, or distributively. We first introduce the background and motivation for micro aerial vehicles, especially for the popular multi-rotor aerial vehicle platform. Then, we discuss the dynamics of quadrotor helicopters. A quadrotor is a specific kind of multi-rotor aerial vehicle with a special property called differential flatness, which simplifies the algorithm of trajectory planning, such that, instead of planning a trajectory in a 12-dimensional state space and 4-dimensional input space, we only need to plan the trajectory in 4-dimensional, so called, flat output space, while the 12-dimensional state and 4-dimensional input can be recovered from a mapping called endogenous transformation. We propose a series of approaches to achieve agile maneuvering of a dynamic quadrotor formation, from controlling a single quadrotor in an artificial vector field, to controlling a group of quadrotors in a Virtual Rigid Body (VRB) framework, to balancing the effect between the human control and autonomy for collision avoidance, and to fast on-line distributed collision avoidance with Buffered Voronoi Cells (BVC). In the vector field method, we generate velocity, acceleration, jerk and snap fields, depending on the tasks, or the positions of obstacles, such that a single quadrotor can easily find its required state and input from the endogenous transformation in order to track the artificial vector field. Next, with a Virtual Rigid Body framework, we let a group of quadrotors follow a single control command while also keeping a required formation, or even reconfigure from one formation to another. The Virtual Rigid Body framework decouples the trajectory planning problem into two sub-problems. Then we consider the problem of collision avoidance of the quadrotor formation when it is meanwhile tele-operated by a single human operator. The autonomy with collision avoidance algorithm, based on the vector field methods for a single quadrotor, is an assistive portion of the quadrotor formation controller, such that the human operator can focus on his/her high-level tasks, leaving the low-level collision avoidance task be handled automatically. We also consider the full autonomy problem of quadrotor formations when reconfiguring from one formation to another by developing a fast, on-line distributed collision avoidance algorithm using Buffered Voronoi Cells (BVCs). Our BVC based collision avoidance algorithm only requires sensed relative position, rather than relative position and velocity, while the computational complexity is comparable to other methods like velocity obstacles. At last, we introduce our experimental quadrotor platform which is built from PixHawk flight controller and Odroid-XU4 single-board computer. The hardware and software architecture of this multiple-quadrotor platform is described in detail so that our platform can easily be adopted and extended with different purposes. Our conclusion remark and discussion of future work are also given in this thesi

Model Predictive Control of Nonholonomic Mobile Robots

In this work, we investigate the possibility of using model predictive control (MPC) for the motion coordination of nonholonomic mobile robots. The contributions of this dissertation can be summarized as follows.A robust formation controller is developed for the leader-following formation of unmanned aerial vehicles (UAVs). With the assumption that an autopilot operating in holding mode at the low-layer, we present a two-layered hierarchical control scheme which allows a team of UAVs to perform complex navigation tasks under limited inter-vehicle communication. Specifically, the robust control law eliminates the requirement of leader's velocity and acceleration information, which reduces the communication overhead.A dual-mode MPC algorithm that allows a team of mobile robots to navigate in formations is developed. The stability of the formation is guaranteed by constraining the terminal state to a terminal region and switching to a stabilizing terminal controller at the boundary of the terminal region. With this dual-mode MPC implementation, stability is achieved while feasibility is relaxed.A first-state contractive model predictive control (FSC-MPC) algorithm is developed for the trajectory tracking and point stabilization problems of nonholonomic mobile robots. The stability of the proposed MPC scheme is guaranteed by adding a first-state contractive constraint and the controller is exponentially stable. The convergence is faster and no terminal region calculation is required. Tracking a trajectory moving backward is no longer a problem under this MPC controller. Moreover, the proposed MPC controller has simultaneous tracking and point stabilization capability.Simulation results are presented to verify the validity of the proposed control algorithms and demonstrate the performance of the proposed controllers.School of Electrical & Computer Engineerin

Safe Autonomous Aerial Surveys of Historical Building Interiors

Cílem této práce je vývoj systému pro bezpečný autonomní průzkum interiérů historických budov za pomocí vícerotorových autonomních bezpilotních helikoptér. Navržené řešení zahrnuje metodu pro sledování požadované trajektorie založené na přístupu lídr-následovník a prediktivním řízení, detekci potenciálních chyb a systému pro řízení mise, který zprostředkovává spolupráci mezi jednotlivými členy formace a korektní reakci na nastalé chyby jednotlivých podsystémů. Návrh celého systému je ovlivněn jeho plánovaným nasazením v rámci skenování interiérů historických budov. Funkčnost navrženého systému je nejprve otestována v rámci početných simulací a následně během experimentu s reálnými bezpilotními helikoptérami.This thesis is aimed at development of the system for safe autonomous survey of historical building interiors by the cooperative formation of multi-rotor unmanned aerial vehicles (UAVs). The proposed solution involves the method for safe trajectory tracking based on the leader-follower scheme and model predictive control, detection of potential faults and failures, and the mission controller which ensures the control of cooperation of particular UAVs and proper reaction on occurrence of faults and failures. The proposition of the whole system is influenced by the aim at its deployment in real world scenarios motivated by the documentation of historical monuments. The developed system is firstly evaluated in simulations. After that, it is tested in a real world scenario with the real UAVs

Leader-Follower Control with Odometry Error Analysis

In this paper we present a leader-follower control law that enables a mobile robot to track a desired trajectory, and allows us to specify the position in the plane of the follower robot with respect to the leader robot. We first describe the dynamic model of the plant, including input torques, and friction forces. Then the control law is developed using backstepping, and it is proved to asymptotically stabilize the tracking error to the origin. Simulation and experimental results of the closed loop system are presented, highlighting its potential application to formation control. The special case of pure tracking (without bi-dimensional position information use) is analyzed, showing that it can be applied to particular classes of non-feasible trajectories. Finally, motivated by some observations on the experiments, the effects of odometry errors are analyzed, revealing that boundedness of the tracking errors can be guaranteed if absolute position information becomes available periodically

A Real-Time Game Theoretic Planner for Autonomous Two-Player Drone Racing

In this article, we propose an online 3-D planning algorithm for a drone to race competitively against a single adversary drone. The algorithm computes an approximation of the Nash equilibrium in the joint space of trajectories of the two drones at each time step, and proceeds in a receding horizon fashion. The algorithm uses a novel sensitivity term, within an iterative best response computational scheme, to approximate the amount by which the adversary will yield to the ego drone to avoid a collision. This leads to racing trajectories that are more competitive than without the sensitivity term. We prove that the fixed point of this sensitivity enhanced iterative best response satisfies the first-order optimality conditions of a Nash equilibrium. We present results of a simulation study of races with 2-D and 3-D race courses, showing that our game theoretic planner significantly outperforms amodel predictive control (MPC) racing algorithm. We also present results of multiple drone racing experiments on a 3-D track in which drones sense each others'' relative position with onboard vision. The proposed game theoretic planner again outperforms the MPC opponent in these experiments where drones reach speeds up to 1.25m/s