Design and implementation of a bristle bot swarm system

Swarm robotics focuses on the study and development of robot systems containing a large number of agents that interact with each other in a collective behaviour in order to achieve tasks or overcome obstacles. Bristlebots are vibration-driven mobile robots. They are characterized by small size, high speed, simple design and low costs for production and application – qualities which are advantageous for agents of swarm robotic systems. However, most studies have been developed over systems with no control or systems with two or more actuators. The aim of this master thesis is the development of a bristle based robot agent for a swarm robotics microsystem with units for locomotion, sensing, data processing, control, communication and energy storage. New approaches in modelling and development of swarm agents are given, and a robot prototype is presented. The robot is driven by a single DC motor and uses a bristle system to create locomotion. It should be noted, that within the system design, considerations for the size, weight and minimalist architecture are taken. Experiments are presented and the system’s capabilities for displacement, velocity and trajectory generation are analysed. While the parallel velocity maintains a positive magnitude in both motor rotation directions, the rotation speed and transversal velocity of the robot have opposite directions, creating curved trajectories with opposite orientations. In Frequencies up to 210 Hz, the rotation direction of the robot is maintained while the magnitude slightly varies. However, for higher frequencies, the rotation direction of the robot is reversed, maintaining a similar magnitude. The transversal speeds at this frequency range, maintain their direction but are clearly reduced compared to lower frequencies.Tesi

Bristlebots in swarm robotics - new approaches in modeling and agent development

Bristlebots are vibration-driven mobile robots. They are characterized by small size, high speed, simple design and low costs of production and application, which are ad- vantageous qualities for agents of swarm robotic systems. In this paper, new ap- proaches in modeling and development of swarm agents are given. It is shown that a simple mass point model can be used to simulate the motion behavior of a bristlebot as complex as necessary for swarm studies. A robot prototype is presented, which has on-board everything needed as a robotic agent. The results of simulations and exper-iments are presented and compared

Swarmodroid 1.0: A Modular Bristle-Bot Platform for Robotic Active Matter Studies

Large swarms of extremely simple robots (i.e., capable just of basic motion activities, like propelling forward or self-rotating) are widely applied to study collective task performance based on self-organization or local algorithms instead of sophisticated programming and global swarm coordination. Moreover, they represent a versatile yet affordable platform for experimental studies in physics, particularly in active matter - non-equilibrium assemblies of particles converting their energy to a directed motion. However, a large set of robotics platforms is being used in different studies, while the universal design is still lacking. Despite such platforms possess advantages in certain application scenarios, their large number sufficiently limits further development of results in the field, as advancing some study requires to buy or manually produce the corresponding robots. To address this issue, we develop an open-source Swarmodroid 1.0 platform based on bristle-bots with reconfigurable 3D-printed bodies, external control of motion velocity, and basic capabilities of velocity profile programming. In addition, we introduce AMPy software package in Python featuring OpenCV-based extraction of robotic swarm kinematics accompanied by the evaluation of key physical quantities describing the collective dynamics. We perform a detailed analysis of individual Swarmodroids' motion characteristics and address their use cases with two examples: a cargo transport performed by self-rotating robots and a velocity-dependent jam formation in a bottleneck by self-propelling robots. Finally, we provide a comparison of existing centimeter-scale robotic platforms, a review of key quantities describing collective dynamics of many-particle systems, and a comprehensive outlook considering potential applications as well as further directions for fundamental studies and Swarmodroid 1.0 platform development.Comment: 18 pages, 7 figures, 1 table + Supplementary Information. Comments are welcom

Adaptive and learning-based formation control of swarm robots

Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation

Collective behavior and task persistification in lazy and minimalist collectives

When individuals in a collective system are constrained in terms of sensing, memory, computation, or power reserves; the design of algorithms to control them becomes challenging. These individual limitations can be due to multiple reasons like the shrinking size of each agent for bulk manufacturing efficiency or enforced simplicity to attain cost efficiency. Whereas, in some areas like nano-medicine, the nature of the task itself warrants such simplicity. This thesis presents algorithms inspired by biological and statistical physics models to achieve useful collective behavior through simple local physical interactions and, minimalist approaches to persistify tasks for long durations in collectives with limited capabilities and energy reserves. The first part of the thesis presents a system of vibration-driven robots that embodies the features of simplicity described above. A combination of theory, experiment, and simulation is used to study dynamic aggregation behavior in these robots facilitated via short-range physical attraction potentials between agents. Collectives in a dynamically aggregated state are shown to be capable of transporting objects over relatively long distances in a finite arena. In the rest of the thesis, two different, yet complementary systems are studied and elaborated to highlight the usefulness of distributed inactivity and activity modulation in aiding persistification of tasks in collectives incapable of implementing complicated algorithms to incorporate regular energy replenishing cycles. To summarize, an approach to achieving dynamic aggregation and related tasks like object transport in a constrained brushbot system is described. Two different artificial and biological collective systems are explored to reveal strategies through which tasks can be persistified without requiring complicated computations, sensing, and memory.Ph.D

Climbing and Walking Robots

With the advancement of technology, new exciting approaches enable us to render mobile robotic systems more versatile, robust and cost-efficient. Some researchers combine climbing and walking techniques with a modular approach, a reconfigurable approach, or a swarm approach to realize novel prototypes as flexible mobile robotic platforms featuring all necessary locomotion capabilities. The purpose of this book is to provide an overview of the latest wide-range achievements in climbing and walking robotic technology to researchers, scientists, and engineers throughout the world. Different aspects including control simulation, locomotion realization, methodology, and system integration are presented from the scientific and from the technical point of view. This book consists of two main parts, one dealing with walking robots, the second with climbing robots. The content is also grouped by theoretical research and applicative realization. Every chapter offers a considerable amount of interesting and useful information

Long-duration robot autonomy: From control algorithms to robot design

The transition that robots are experiencing from controlled and often static working environments to unstructured and dynamic settings is unveiling the potential fragility of the design and control techniques employed to build and program them, respectively. A paramount of example of a discipline that, by construction, deals with robots operating under unknown and ever-changing conditions is long-duration robot autonomy. In fact, during long-term deployments, robots will find themselves in environmental scenarios which were not planned and accounted for during the design phase. These operating conditions offer a variety of challenges which are not encountered in any other discipline of robotics. This thesis presents control-theoretic techniques and mechanical design principles to be employed while conceiving, building, and programming robotic systems meant to remain operational over sustained amounts of time. Long-duration autonomy is studied and analyzed from two different, yet complementary, perspectives: control algorithms and robot design. In the context of the former, the persistification of robotic tasks is presented. This consists of an optimization-based control framework which allows robots to remain operational over time horizons that are much longer than the ones which would be allowed by the limited resources of energy with which they can ever be equipped. As regards the mechanical design aspect of long-duration robot autonomy, in the second part of this thesis, the SlothBot, a slow-paced solar-powered wire-traversing robot, is presented. This robot embodies the design principles required by an autonomous robotic system 1in order to remain functional for truly long periods of time, including energy efficiency, design simplicity, and fail-safeness. To conclude, the development of a robotic platform which stands at the intersection of design and control for long-duration autonomy is described. A class of vibration-driven robots, the brushbots, are analyzed both from a mechanical design perspective, and in terms of interaction control capabilities with the environment in which they are deployed.Ph.D

Development, evolution and genetic analysis of C. elegans-inspired foraging algorithms under different environmental conditions

In this work 3 minimalist bio-inspired foraging algorithms based on C. elegans’ chemotaxis and foraging behaviour were developed and investigated. The main goal of the work is to apply the algorithms to robots with limited sensing capabilities. The refined versions of these algorithms were developed and optimised in 22 different environments. The results were processed using a novel set of techniques presented here, named Genotype Clustering. The results lead to two distinct conclusions, one practical and one more academic. From a practical perspective, the results suggest that, when suitably tuned, minimalist C. elegans-inspired foraging algorithms can lead to effective navigation to unknown targets even in the presence of repellents and under the influence of a significant sensor noise. From an academic perspective, the work demonstrates that even simple models can serve as an interesting and informative testbed for exploring fundamental evolutionary principles. The simulated robots were grounded in real hardware parameters, aiming at future application of the foraging algorithms in real robots. Another achievement of the project was the development of the simulation framework that provides a simple yet flexible program for the development and optimisation of behavioural algorithms

Adaptive behaviour through morphological plasticity in natural and artificial systems.

Our concept of intelligence is changing. Embodiment has led to the rise of morphologies in Artificial Intelligence (AI) research. This thesis focuses on two research questions: 1) How can system morphologies, well-adapted to changing environments, be designed? 2) How can adaptive behaviour be generated through morphology? It is the fundamental argument of this thesis that morphological plasticity (MP), the environmentally induced variation in growth or development, can provide a solution to both questions. Specifically, this thesis is based around a detailed study of diatom valve morphogenesis. Diatoms, a unicellular organism, construct intricate siliceous structures (valves) around themselves which exhibit high plasticity to the environment. Diatom valve morphogenesis is a good example of how morphologies can be well-adapted to changing environments, an open problem in AI, and how adaptive behaviour can be generated through morphological processes alone. Through a constructivist approach this thesis contributes to both understanding of MP in natural systems and the design of MP algorithms for artificial adaptive systems. Several original models and frameworks are defined within this thesis: the Nature's Batik Model of basic diatom valve morphogenesis the Cellanimat, a 'Dynamic Morphology' based on the unicell, capable of MP driven adaptive behaviour through its unique 'Artificial Cytoskeleton' model of cytoskeletal dynamics the Environment-Phenotype Map framework and the Cellanimat Colony Model, which combines all previous models for the investigation of MP mechanisms during diatom colony formation. Cellanimat dynamics and optimization are thoroughly investigated and the model is shown to be multi functional, evolvable, scalable and reasonably robust

Active Materials

What is an active material? This book aims to redefine perceptions of the materials that respond to their environment. Through the theory of the structure and functionality of materials found in nature a scientific approach to active materials is first identified. Further interviews with experts from the natural sciences and humanities then seeks to question and redefine this view of materials to create a new definition of active materials