Hierarchical Control of a Team of Quadrotors for Cooperative Active Target Tracking

International audienceThis paper proposes a novel active target tracking strategy for a team of cooperating quadrotors equipped with 3-D range-finding sensors. The work builds upon previous research of the authors, and adopts a realistic nonlinear dynamic model for the quadrotors. A hierarchical controller is designed for the generation and tracking of the desired optimal trajectories of the aerial vehicles, and a discrete-time Kalman filter is used for fusing their local estimates of the target position. Under suitable conditions, it is shown that the cost function for the D-optimality criterion that the quadrotors aim at collaboratively reduce, possesses a single global minimum and no local minima. Numerical simulations and real-world experiments show the effectiveness of the proposed control strategy

A Survey on Aerial Swarm Robotics

The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas

A review of aerial manipulation of small-scale rotorcraft unmanned robotic systems

Small-scale rotorcraft unmanned robotic systems (SRURSs) are a kind of unmanned rotorcraft with manipulating devices. This review aims to provide an overview on aerial manipulation of SRURSs nowadays and promote relative research in the future. In the past decade, aerial manipulation of SRURSs has attracted the interest of researchers globally. This paper provides a literature review of the last 10 years (2008–2017) on SRURSs, and details achievements and challenges. Firstly, the definition, current state, development, classification, and challenges of SRURSs are introduced. Then, related papers are organized into two topical categories: mechanical structure design, and modeling and control. Following this, research groups involved in SRURS research and their major achievements are summarized and classified in the form of tables. The research groups are introduced in detail from seven parts. Finally, trends and challenges are compiled and presented to serve as a resource for researchers interested in aerial manipulation of SRURSs. The problem, trends, and challenges are described from three aspects. Conclusions of the paper are presented, and the future of SRURSs is discussed to enable further research interests

Where Am I Now? Dynamically Finding Optimal Sensor States to Minimize Localization Uncertainty for a Perception-Denied Rover

We present DyFOS, an active perception method that dynamically finds optimal states to minimize localization uncertainty while avoiding obstacles and occlusions. We consider the scenario where a perception-denied rover relies on position and uncertainty measurements from a viewer robot to localize itself along an obstacle-filled path. The position uncertainty from the viewer's sensor is a function of the states of the sensor itself, the rover, and the surrounding environment. To find an optimal sensor state that minimizes the rover's localization uncertainty, DyFOS uses a localization uncertainty prediction pipeline in an optimization search. Given numerous samples of the states mentioned above, the pipeline predicts the rover's localization uncertainty with the help of a trained, complex state-dependent sensor measurement model (a probabilistic neural network). Our pipeline also predicts occlusion and obstacle collision to remove undesirable viewer states and reduce unnecessary computations. We evaluate the proposed method numerically and in simulation. Our results show that DyFOS is faster than brute force yet performs on par. DyFOS also yielded lower localization uncertainties than faster random and heuristic-based searches.Comment: 7 pages, 7 figures, Accepted to 2023 IEEE International Symposium on Multi-Robot & Multi-Agent Systems (MRS

Design of an UAV swarm

This master thesis tries to give an overview on the general aspects involved in the design of an UAV swarm. UAV swarms are continuoulsy gaining popularity amongst researchers and UAV manufacturers, since they allow greater success rates in task accomplishing with reduced times. Appart from this, multiple UAVs cooperating between them opens a new field of missions that can only be carried in this way. All the topics explained within this master thesis will explain all the agents involved in the design of an UAV swarm, from the communication protocols between them, navigation and trajectory analysis and task allocation

A Tutorial on Distributed Optimization for Cooperative Robotics: from Setups and Algorithms to Toolboxes and Research Directions

Several interesting problems in multi-robot systems can be cast in the framework of distributed optimization. Examples include multi-robot task allocation, vehicle routing, target protection and surveillance. While the theoretical analysis of distributed optimization algorithms has received significant attention, its application to cooperative robotics has not been investigated in detail. In this paper, we show how notable scenarios in cooperative robotics can be addressed by suitable distributed optimization setups. Specifically, after a brief introduction on the widely investigated consensus optimization (most suited for data analytics) and on the partition-based setup (matching the graph structure in the optimization), we focus on two distributed settings modeling several scenarios in cooperative robotics, i.e., the so-called constraint-coupled and aggregative optimization frameworks. For each one, we consider use-case applications, and we discuss tailored distributed algorithms with their convergence properties. Then, we revise state-of-the-art toolboxes allowing for the implementation of distributed schemes on real networks of robots without central coordinators. For each use case, we discuss their implementation in these toolboxes and provide simulations and real experiments on networks of heterogeneous robots

A novel coordination framework for multi-robot systems

Having made great progress tackling the basic problems concerning single-robot systems, many researchers shifted their focus towards the study of multi-robot systems (MRS). MRS were shortly found to be a perfect t for tasks considered to be hard, complex or even impossible for a single robot to perform, e.g. spatially separate tasks. One core research problem of MRS is robots' coordinated motion planning and control. Arti cial potential elds (APFs) and virtual spring-damper bonds are among the most commonly used models to attack the trajectory planning problem of MRS coordination. However, although mathematically sound, these approaches fail to guarantee inter-robot collision-free path generation. This is particularly the case when robots' dynamics, nonholonomic constraints and complex geometry are taken into account. In this thesis, a novel bio-inspired collision avoidance framework via virtual shells is proposed and augmented into the high-level trajectory planner. Safe trajectories can hence be generated for the low-level controllers to track. Motion control is handled by the design of hierarchical controllers which utilize virtual inputs. Several distinct coordinated task scenarios for 2D and 3D environments are presented as a proof of concept. Simulations are conducted with groups of three, four, ve and ten nonholonomic mobile robots as well as groups of three and ve quadrotor UAVs. The performance of the overall improved coordination structure is veri ed with very promising result

Real-time control architecture for a multi UAV test bed

The purpose of this thesis is to develop a control architecture running at real-time for a multi unmanned aerial vehicle test bed formed by three AscTec Hummingbird mini quadrotors. The reliable and reconfigurable architecture presented here has a FPGA-based embedded system as main controller. Under the implemented control system, different practical applications have been performed in the MARHES Lab at the University of New Mexico as part of its research in cooperative control of mobile aerial agents. This thesis also covers the quadrotor modeling, the design of a position controller, the real-time architecture implementation and the experimental flight tests. A hybrid approach combining first-principles with system identification techniques is used for modeling the quadrotor due to the lack of information around the structure of the onboard controller designed by AscTec. The complete quadrotor model structure is formed by a black-box subsystem and a point-mass submodel. Experimental data have been gathered for system identification and black-box submodel validation purposes; while the point-mass submodel is found applying rigid-body dynamics. Using the dynamical model, a position control block based in lead-lag and PI compensators is developed and simulated. Improvements in trajectory tracking performance are achieved estimating the linear velocity of the aerial robot and incorporating velocity lead-lag compensators to the control approach. The velocity of the aerial robot is computed by numerical differentiation of position data. Simulation results to a variety of input signals of the control block in cascade with the complete dynamic model of the quadrotor are included. The control block together with the velocity estimation is fully programmed in the embedded controller. A graphical user interface, GUI, as part of the architecture is designed to display real-time data of position and orientation streamed from the motion tracking system as well as to contain useful user controllers. This GUI facilitates that a single operator conducts and oversees all aspects of the different applications where one or multiple quadrotors are used. Experimental tests have helped to tune the control parameters determined by simulation. The performance of the whole architecture has been validated through a variety of practical applications. Autonomous take off, hovering and landing, target surveillance, trajectory tracking and suspended payload transportation are just some of the applications carried out employing the real-time control architecture proposed in this thesis