Scalable Control Strategies and a Customizable Swarm Robotic Platform for Boundary Coverage and Collective Transport Tasks

abstract: Swarms of low-cost, autonomous robots can potentially be used to collectively perform tasks over large domains and long time scales. The design of decentralized, scalable swarm control strategies will enable the development of robotic systems that can execute such tasks with a high degree of parallelism and redundancy, enabling effective operation even in the presence of unknown environmental factors and individual robot failures. Social insect colonies provide a rich source of inspiration for these types of control approaches, since they can perform complex collective tasks under a range of conditions. To validate swarm robotic control strategies, experimental testbeds with large numbers of robots are required; however, existing low-cost robots are specialized and can lack the necessary sensing, navigation, control, and manipulation capabilities. To address these challenges, this thesis presents a formal approach to designing biologically-inspired swarm control strategies for spatially-confined coverage and payload transport tasks, as well as a novel low-cost, customizable robotic platform for testing swarm control approaches. Stochastic control strategies are developed that provably allocate a swarm of robots around the boundaries of multiple regions of interest or payloads to be transported. These strategies account for spatially-dependent effects on the robots' physical distribution and are largely robust to environmental variations. In addition, a control approach based on reinforcement learning is presented for collective payload towing that accommodates robots with heterogeneous maximum speeds. For both types of collective transport tasks, rigorous approaches are developed to identify and translate observed group retrieval behaviors in Novomessor cockerelli ants to swarm robotic control strategies. These strategies can replicate features of ant transport and inherit its properties of robustness to different environments and to varying team compositions. The approaches incorporate dynamical models of the swarm that are amenable to analysis and control techniques, and therefore provide theoretical guarantees on the system's performance. Implementation of these strategies on robotic swarms offers a way for biologists to test hypotheses about the individual-level mechanisms that drive collective behaviors. Finally, this thesis describes Pheeno, a new swarm robotic platform with a three degree-of-freedom manipulator arm, and describes its use in validating a variety of swarm control strategies.Dissertation/ThesisDoctoral Dissertation Mechanical Engineering 201

A two teraflop swarm

© 2018 Jones, Studley, Hauert and Winfield. We introduce the Xpuck swarm, a research platform with an aggregate raw processing power in excess of two teraflops. The swarm uses 16 e-puck robots augmented with custom hardware that uses the substantial CPU and GPU processing power available from modern mobile system-on-chip devices. The augmented robots, called Xpucks, have at least an order of magnitude greater performance than previous swarm robotics platforms. The platform enables new experiments that require high individual robot computation and multiple robots. Uses include online evolution or learning of swarm controllers, simulation for answering what-if questions about possible actions, distributed super-computing for mobile platforms, and real-world applications of swarm robotics that requires image processing, or SLAM. The teraflop swarm could also be used to explore swarming in nature by providing platforms with similar computational power as simple insects. We demonstrate the computational capability of the swarm by implementing a fast physics-based robot simulator and using this within a distributed island model evolutionary system, all hosted on the Xpucks

Computational resources of miniature robots: classification & implications

When it comes to describing robots, many roboticists choose to focus on the size, types of actuators, or other physical capabilities. As most areas of robotics deploy robots with large memory and processing power, the question “how computational resources limit what a robot can do” is often overlooked. However, the capabilities of many miniature robots are limited by significantly less memory and processing power. At present, there is no systematic approach to comparing and quantifying the computational resources as a whole and their implications. This letter proposes computational indices that systematically quantify computational resources—individually and as a whole. Then, by comparing 31 state-of-the-art miniature robots, a computational classification ranging from non-computing to minimally-constrained robots is introduced. Finally, the implications of computational constraints on robotic software are discussed

Chartopolis - A Self Driving Car Test Bed

abstract: This thesis presents an autonomous vehicle test bed which can be used to conduct studies on the interaction between human-driven vehicles and autonomous vehicles on the road. The test bed will make use of a fleet of robots which is a microcosm of an autonomous vehicle performing all the vital tasks like lane following, traffic signal obeying and collision avoidance with other vehicles on the road. The robots use real-time image processing and closed-loop control techniques to achieve automation. The testbed also features a manual control mode where a user can choose to control the car with a joystick by viewing a video relayed to the control station. Stochastic rogue vehicle processes will be introduced into the system which will emulate random behaviors in an autonomous vehicle. The test bed was experimented to perform a comparative study of driving capabilities of the miniature self-driving car and a human driver.Dissertation/ThesisMasters Thesis Electrical Engineering 201

HoverBots: Precise Locomotion Using Robots That Are Designed for Manufacturability

Scaling up robot swarms to collectives of hundreds or even thousands without sacrificing sensing, processing, and locomotion capabilities is a challenging problem. Low-cost robots are potentially scalable, but the majority of existing systems have limited capabilities, and these limitations substantially constrain the type of experiments that could be performed by robotics researchers. As an alternative to increasing the quantity of robots by reducing their functionality, we have developed a new technology that delivers increased functionality at low-cost. In this study, we present a comprehensive literature review on the most commonly used locomotion strategies of swarm robotic systems. We introduce a new type of low-friction locomotion—active low-friction locomotion—and we show its first implementation in the HoverBot system. The HoverBot system consists of an air levitation and magnet table, and a HoverBot agent. HoverBot agents are levitating circuit boards that we have equipped with an array of planar coils and a Hall-effect sensor. The HoverBot agent uses its coils to pull itself toward magnetic anchors that are embedded into a levitation table. These robots use active low-friction locomotion; consist of only surface-mount components; circumvent actuator calibration; are capable of odometry by using a single Hall-effect sensor; and perform precise movement. We conducted three hours of experimental evaluation of the HoverBot system in which we observed the system performing more than 10,000 steps. We also demonstrate formation movement, random collision, and straight collisions with two robots. This study demonstrates that active low-friction locomotion is an alternative to wheeled and slip-stick locomotion in the field of swarm robotics

Study Materials for Robot Platform Robotont

As robotics has gained popularity, the need for robotics engineers has increased. Many robotics companies look for engineers with knowledge of ROS (Robot Operating System). Therefore, ROS should be taught in schools. A ROS capable robot platform called Robotont has been developed at the University of Tartu but it was missing study materials and supporting software. As a result of this effort, eight lab manuals for Robotont were created to introduce topics such as controllers, 2D and 3D mapping, and autonomous navigation. In addition, supporting ROS software was developed. Special focus was set on creating Gazebo simulation packages for Robotont to enable the use of study materials without the real robot. The study materials and robot software were piloted at Univeristy of Tartu Narva College.Robootika on jõudsalt populaarsust kogunud ning seetõttu on suurenenud ka nõudlus robootikainseneride järele. Paljudes robootikafirmades otsitakse ROSi (Robot Operating System) oskusega inimesi, seega tuleks õpetada ROSi kasutamist. Tartu Ülikoolis on arendatud robotplatvorm Robotont, mis on ehitatud ROSile, kuid millel puudusid õppematerjalid ning seda toetav tarkvara. Töö raames valmis Robotondile kaheksa praktikumitööd regulaatorite, 2Dja 3D-kaardistamise ning autonoomse navigeerimise teemadel, seda toetav ROS tarkvara ning Gazebo simulatsioonikeskkond, mis võimaldab praktikumitöid läbida ilma robotita. Valminud praktikumitööd piloteeriti TÜ Narva kolledžis