Coordinated multi-robot formation control

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201

On Provably Safe and Live Multirobot Coordination With Online Goal Posting

A standing challenge in multirobot systems is to realize safe and efficient motion planning and coordination methods that are capable of accounting for uncertainties and contingencies. The challenge is rendered harder by the fact that robots may be heterogeneous and that their plans may be posted asynchronously. Most existing approaches require constraints on the infrastructure or unrealistic assumptions on robot models. In this article, we propose a centralized, loosely-coupled supervisory controller that overcomes these limitations. The approach responds to newly posed constraints and uncertainties during trajectory execution, ensuring at all times that planned robot trajectories remain kinodynamically feasible, that the fleet is in a safe state, and that there are no deadlocks or livelocks. This is achieved without the need for hand-coded rules, fixed robot priorities, or environment modification. We formally state all relevant properties of robot behavior in the most general terms possible, without assuming particular robot models or environments, and provide both formal and empirical proof that the proposed fleet control algorithms guarantee safety and liveness

Enclosing a moving target with an optimally rotated and scaled multiagent pattern

We propose a novel control method to enclose a moving target in a two-dimensional setting with a team of agents forming a prescribed geometric pattern. The approach optimises a measure of the overall agent motion costs, via the minimisation of a suitably defined cost function encapsulating the pattern rotation and scaling. We propose two control laws which use global information and make the agents exponentially converge to the prescribed formation with an optimal scale that remains constant, while the team's centroid tracks the target. One control law results in a multiagent pattern that keeps a constant orientation in the workspace; for the other, the pattern rotates with constant speed. These behaviours, whose optimality and steadiness are very relevant for the task addressed, occur independently from the target's velocity. Moreover, the methodology does not require distance measurements, common coordinate references, or communications. We also present formal guarantees of collision avoidance for the proposed approach. Illustrative simulation examples are provided

Multi-bot Easy Control Hierarchy

The goal of our project is to create a software architecture that makes it possible to easily control a multi-robot system, as well as seamlessly change control modes during operation. The different control schemes first include the ability to implement on-board and off-board controllers. Second, the commands can specify either actuator level, vehicle level, or fleet level behavior. Finally, motion can be specified by giving a waypoint and time constraint, a velocity and heading, or a throttle and angle. Our code is abstracted so that any type of robot - ranging from ones that use a differential drive set up, to three-wheeled holonomic platforms, to quadcopters - can be added to the system by simply writing drivers that interface with the hardware used and by implementing math packages that do the required calculations. Our team has successfully demonstrated piloting a single robots while switching between waypoint navigation and a joystick controller. In addition, we have demonstrated the synchronized control of two robots using joystick control. Future work includes implementing a more robust cluster control, including off-board functionality, and incorporating our architecture into different types of robots

Multi-robot Implicit Control of Massive Herds

This paper solves the problem of herding countless evaders by means of a few robots. The objective is to steer all the evaders towards a desired tracking reference while avoiding escapes. The problem is very challenging due to the highly complex repulsive evaders' dynamics and the underdetermined states to control. We propose a solution that is based on Implicit Control and a novel dynamic assignment strategy to select the evaders to be directly controlled. The former is a general technique that explicitly computes control inputs even in highly complex input-nonaffine dynamics. The latter is built upon a convex-hull dynamic clustering inspired by the Voronoi tessellation problem. The combination of both allows to choose the best evaders to directly control, while the others are indirectly controlled by exploiting the repulsive interactions among them. Simulations show that massive herds can be herd throughout complex patterns by means of a few herders.Comment: E. Sebastian, E. Montijano and C. Sagues,"Multi-robot Implicit Control of Massive Herds'', Fifth Iberian Robotics Conference (ROBOT22), 202

PERFORMANCE EVALUATION AND REVIEW FRAMEWORK OF ROBOTIC MISSIONS (PERFORM): AUTONOMOUS PATH PLANNING AND AUTONOMY PERFORMANCE EVALUATION

The scope of this work spans two main areas of autonomy research 1) autonomous path planning and 2) test and evaluation of autonomous systems. Path planning is an integral part of autonomous decision-making, and a deep understanding in this area provides valuable perspective on approaching the problem of how to effectively evaluate vehicle behavior. Autonomous decision-making capabilities must include reliability, robustness, and trustworthiness in a real-world environment. A major component of robot decision-making lies in intelligent path-planning. Serving as the brains of an autonomous system, an efficient and reliable path planner is crucial to mission success and overall safety. A hybrid global and local planner is implemented using a combination of the Potential Field Method (PFM) and A-star (A*) algorithms. Created using a layered vector field strategy, this allows for flexibility along with the ability to add and remove layers to take into account other parameters such as currents, wind, dynamics, and the International Regulations for Preventing Collisions at Sea (COLGREGS). Different weights can be attributed to each layer based on the determined level of importance in a hierarchical manner. Different obstacle scenarios are shown in simulation, and proof-of-concept validation of the path-planning algorithms on an actual ASV is accomplished in an indoor environment. Results show that the combination of PFM and A* complement each other to generate a successfully planned path to goal that alleviates local minima and entrapment issues. Additionally, the planner demonstrates the ability to update for new obstacles in real time using an obstacle detection sensor. Regarding test and evaluation of autonomous vehicles, trust and confidence in autonomous behavior is required to send autonomous vehicles into operational missions. The author introduces the Performance Evaluation and Review Framework Of Robotic Missions (PERFORM), a framework for which to enable a rigorous and replicable autonomy test environment, thereby filling the void between that of merely simulating autonomy and that of completing true field missions. A generic architecture for defining the missions under test is proposed and a unique Interval Type-2 Fuzzy Logic approach is used as the foundation for the mathematically rigorous autonomy evaluation framework. The test environment is designed to aid in (1) new technology development (i.e. providing direct comparisons and quantitative evaluations of varying autonomy algorithms), (2) the validation of the performance of specific autonomous platforms, and (3) the selection of the appropriate robotic platform(s) for a given mission type (e.g. for surveying, surveillance, search and rescue). Several case studies are presented to apply the metric to various test scenarios. Results demonstrate the flexibility of the technique with the ability to tailor tests to the user’s design requirements accounting for different priorities related to acceptable risks and goals of a given mission

Estrategias de control multi-robot aplicadas a problemas de atrapamiento

El Trabajo de Fin de Grado pretende desarrollar estrategias de control en sistemas multi-robot (MRS) aplicadas al problema concreto de atrapamiento. En objetivo del atrapamiento es llevar a un grupo no coordinado de objetivos a un lugar deseado mediante la interacción con un equipo coordinado de robots. La dinámica de movimiento de los objetivos es fuertemente no lineal tanto en la posición de los objetivos como en la de los robots, constituyendo la principal fuente de complejidad del problema. Por otro lado, en ningún momento se asume un numero determinado de objetivos o robots, añadiendo generalidad a las soluciones diseñadas. Para ello, se plantea el estudio teórico y global del problema, aplicando de forma escalonada diferentes técnicas de control. La estrategia inicial se basa en la búsqueda de la formación óptima de los robots con respecto a la posición deseada para los objetivos. A partir aquí se aplica un control óptimo LQR apoyado en la linealización del sistema, que tiene como punto de partida la configuración previamente obtenida. Posteriormente, se aborda el diseño de estrategias de control no lineal basadas en la teoría de estabilidad de Lyapunov para superar los inconvenientes de la estrategia inicial. Por último, se incluye un breve desarrollo de estrategias adaptativas que generalizan el control no lineal a situaciones en las que no se conocen con precisión los parámetros de la dinámica de los objetivos. Además, el trabajo ha supuesto un esfuerzo de diseño de un entorno de simulación para poder evaluar todos los algoritmos, analizando las prestaciones de las diferentes estrategias propuestas. El desarrollo y evaluación de la primera estrategia de control ha supuesto la redacción de un artículo que ha sido aceptado y publicado en la 24a edición IEEE Conference on Emerging Technologies and Factory Automation (ETFA).<br /

Design and Development of an Integrated Mobile Robot System for Use in Simple Formations

In recent years, formation control of autonomous unmanned vehicles has become an active area of research with its many broad applications in areas such as transportation and surveillance. The work presented in this thesis involves the design and implementation of small unmanned ground vehicles to be used in leader-follower formations. This mechatronics project involves breadth in areas of mechanical, electrical, and computer engineering design. A vehicle with a unicycle-type drive mechanism is designed in 3D CAD software and manufactured using 3D printing capabilities. The vehicle is then modeled using the unicycle kinematic equations of motion and simulated in MATLAB/Simulink. Simple motion tasks are then performed onboard the vehicle utilizing the vehicle model via software, and leader-follower formations are implemented with multiple vehicles

Implementación de estrategias de atrapamiento utilizando equipos de robots

La investigación en equipos de robots que cooperan para realizar tareas ha adquirido un importante auge en los últimos años. La utilización de sistema multi-robot permite realizar tareas de forma más eficiente, segura y robusta, como en el caso del atrapamiento o la escolta de objetivos. Sin embargo, al aumentar el número de robots que realizan estas tareas, su ejecución es cada vez más compleja debido a las restricciones de movimiento de los robots. El principal objetivo de este trabajo es la implementación de un algoritmo de atrapamiento en un sistema multi-robot real. Dicho algoritmo debe tener en cuenta posibles errores en la información local y global de la posición del target, así como en la movilidad de los robots involucrados