A Distributed Epigenetic Shape Formation and Regeneration Algorithm for a Swarm of Robots

Living cells exhibit both growth and regeneration of body tissues. Epigenetic Tracking (ET), models this growth and regenerative qualities of living cells and has been used to generate complex 2D and 3D shapes. In this paper, we present an ET based algorithm that aids a swarm of identically-programmed robots to form arbitrary shapes and regenerate them when cut. The algorithm works in a distributed manner using only local interactions and computations without any central control and aids the robots to form the shape in a triangular lattice structure. In case of damage or splitting of the shape, it helps each set of the remaining robots to regenerate and position themselves to build scaled down versions of the original shape. The paper presents the shapes formed and regenerated by the algorithm using the Kilombo simulator.Comment: 8 pages, 9 figures, GECCO-18 conferenc

TRANSPORTE COOPERATIVO DE OBJETOS CON UNA PLATAFORMA MÓVIL EN UN ENTORNO ESTRUCTURADO

La robótica cooperativa busca diseñar sistemas compuestos de varios robots capaces de resolver problemas conjuntamente, de tal manera que dicha implementación puede llevar consigo distintos tipos de control (centralizado o distribuido), de esta manera una de las aplicaciones en esta línea de investigación es el transporte de objetos mediante distintas estrategias que van desde el empuje, la sujeción y arrastre de los objetos; tales trabajos se han presentado desde la década de los 90 hasta el día de hoy. El enfoque del presente artículo busca mostrar el desarrollo e implementación de una plataforma robótica cooperativa aplicada al transporte de objetos largos a través de un entorno estructurado utilizando las herramientas que provee el kit Lego Mindstorms RCX 2.0 y el grupo de investigación en robótica móvil autónoma ROMA

Negotiation of goal direction for cooperative transport

In this paper, we study the cooperative transport of a heavy object by a group of robots towards a goal. We investigate the case in which robots have partial and noisy knowledge of the goal direction and can not perceive the goal itself. The robots have to coordinate their motion to apply enough force on the object to move it. Furthermore, the robots should share knowledge in order to collectively improve their estimate of the goal direction and transport the object as fast and as accurately as possible towards the goal. We propose a bio-inspired mechanism of negotiation of direction that is fully distributed. Four different strategies are implemented and their performances are compared on a group of four real robots, varying the goal direction and the level of noise. We identify a strategy that enables effcient coordination of motion of the robots. Moreover, this strategy lets the robots improve their knowledge of the goal direction. Despite significant noise in the robots' communication, we achieve effective cooperative transport towards the goal and observe that the negotiation of direction entails interesting properties of robustness

Occlusion-based cooperative transport with a swarm of miniature mobile robots

This paper proposes a strategy for transporting a large object to a goal using a large number of mobile robots that are significantly smaller than the object. The robots only push the object at positions where the direct line of sight to the goal is occluded by the object. This strategy is fully decentralized and requires neither explicit communication nor specific manipulation mechanisms. We prove that it can transport any convex object in a planar environment. We implement this strategy on the e-puck robotic platform and present systematic experiments with a group of 20 e-pucks transporting three objects of different shapes. The objects were successfully transported to the goal in 43 out of 45 trials. When using a mobile goal, teleoperated by a human, the object could be navigated through an environment with obstacles. We also tested the strategy in a 3-D environment using physics-based computer simulation. Due to its simplicity, the transport strategy is particularly suited for implementation on microscale robotic systems

Transport of an object by six pre-attached robots interacting via physical links

SCOPUS: cp.pinfo:eu-repo/semantics/publishe

Swarm-Based Techniques for Adaptive Navigation Primitives

Adaptive Navigation (AN) has, in the past, been successfully accomplished by using mobile multi-robot systems (MMS) in highly structured formations known as clusters. Such multi-robot adaptive navigation (MAN) allows for real-time reaction to sensor readings and navigation to a goal location not known a priori. This thesis successfully reproduces MAN cluster techniques via swarm control techniques, a less computationally expensive but less formalized control technique for MMS, which achieves robot control through a combination of primitive robot behaviors. While powerful for large numbers of robots, swarm robotics often relies on “emergent” swarm behaviors resulting from robot-level behaviors, rather than top-down specification of swarm behaviors. For adaptive navigation purposes, it was desired to be able to specify swarm-level behavior from a top down perspective rather than experimenting with emergent behaviors. To this end, a simulation environment was developed to allow rapid development and vetting of swarm behaviors while easily interfacing with an existing testbed for validation on hardware. An initial suite of robot primitive and composite behaviors was developed and vetted using this simulator, and the behaviors were validated using the existing testbed in Santa Clara University’s Robotics System Laboratory (RSL). Of particular importance were the adaptive navigation primitives of extrema finding and contour finding and following. These AN primitives were tested over a variety of experimental parameters, yielding design guidelines for top-down specification of swarm robotic adaptive navigation. These design guidelines are presented, and their usefulness is demonstrated for a Contour Finding and Following application using the RSL’s testbed. Finally, possible future work to expand the capability of swarm-based adaptive navigation techniques is discussed

Transport of an Object by Six Pre-attached Robots Interacting via Physical Links Roderich Gro,

We study the cooperative transport of a heavy object by a group of mobile robots. The system is fully decentralized and the information flow between the robots is limited to physical interactions. The robots have no explicit or implicit knowledge about their relative positions

물체 수송을 위한 협업 로봇의 행동 연구

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 2. 이범희.This dissertation presents two cooperative object transportation techniques according to the characteristics of objects: passive and active. The passive object is a typical object, which cannot communicate with and detect other robots. The active object, however, has abilities to communicate with robots and can measure the distance from other robots using proximity sensors. Typical areas of research in cooperative object transportation include grasping, pushing, and caging techniques, but these require precise grasping behaviors, iterative motion correction according to the object pose, and the real-time acquisition of the object shape, respectively. For solving these problems, we propose two new object transportation techniques by considering the properties of objects. First, this dissertation presents a multi-agent behavior to cooperatively transport an active object using a sound signal and interactive communication. We first developed a sound localization method, which estimates the sound source from an active object by using three microphone sensors. Next, since the active object cannot be recalled by only a single robot, the robots organized a heterogeneous team by themselves with a pusher, a puller, and a supervisor. This self-organized team succeeded in moving the active object to a goal using the cooperation of its neighboring robots and interactive communication between the object and robots. Second, this dissertation presents a new cooperative passive object transportation technique using cyclic shift motion. The proposed technique does not need to consider the shape or the pose of objects, and equipped tools are also unnecessary for object transportation. Multiple robots create a parallel row formation using a virtual electric dipole field and then push multiple objects into the formation. This parallel row is extended to the goal using cyclic motion by the robots. The above processes are decentralized and activated based on the finite state machine of each robot. Simulations and practical experiments are presented to verify the proposed techniques.Chapter 1 Introduction 1 1.1 Background and Motivation 1 1.2 Related Work 4 1.2.1 The Categories of Object Transportation Techniques 4 1.2.2 Sound Localization Techniques for Active Object Transportation 7 1.3 Contributions 8 1.4 Organization 10 Chapter 2 Object Transportation Problem 11 2.1 Passive Object versus Active Object 11 2.2 Problem Formulation 13 2.3 Assumptions 13 Chapter 3 Active Object Transportation using a Sound Signal and Interactive Communication 15 3.1 Overview of Active Object Transportation 16 3.2 Sound Vector Generation using Triple Microphones 17 3.2.1 Sound Isocontour Generation using ILD 18 3.2.2 Sound Circle Generation using Inverse-square Law 21 3.2.3 Sound Vector Generation 22 3.3 Cooperative Control Method using Interactive Communication 25 3.3.1 Role Assignment of Multi-robot Team 25 3.3.2 Position Assignment of Multi-robot Team 26 3.3.3 Transportation Process of an Active Object 29 Chapter 4 Passive Object Transportation using Cyclic Shift Motion 33 4.1 Overview of Passive Object Transportation 34 4.2 Multi-robot Team Organization 35 4.3 Row Formation Generation using Multiple Robots 37 4.3.1 Cyclic Shift Motion 37 4.3.2 Path Generation using Virtual Electric Dipole Field 39 4.3.3 Path Following using Bang-bang Controller 42 4.4 Multi-object Transportation by a Decentralized Multi-robot Team 45 4.4.1 Information Acquisition Methods for Finite State Machine 45 4.4.2 Finite State Machines (FSMs) 48 4.4.2.1 The FSM of Guider Robots 49 4.4.2.2 The FSM of a Pusher Robot 52 4.4.2.3 The FSM of a Leader Robot 54 4.4.3 Object Transportation Process 55 4.4.4 Formation Constraints for Curved Transportation Path 57 Chapter 5 Simulation Results 61 5.1 Simulation Environment 61 5.2 Simulation Result of Passive Object Transportation 63 5.3 Comparison Results with Other Passive Object Transportation Techniques 69 5.3.1 Simulation Result of Leader-Follower Technique 70 5.3.2 Simulation Result of Caging Technique 72 Chapter 6 Practical Experiments 77 6.1 Experimental Environment 77 6.2 Experimental Results of Active Object Transportation 81 6.2.1 Experimental Result of the SV Estimation 81 6.2.2 Experimental Result of Active Object Transportation 82 6.3 Experimental Results of Passive Object Transportation 86 6.3.1 Small-object Transportation with Straight Path 86 6.3.2 Small-object Transportation with Curved Path 91 6.3.3 Large-object Transportation 93 6.4 Comparison Result with Caging Technique 95 Chapter 7 Discussion 96 Chapter 8 Conclusions 99 Appendix A: The Approaching Phase of Passive Object Transportation 101 A.1 Approaching Phase 101 A.2 Experimental Result of Approaching Phase 107 Appendix B: Object Transportation in a Static Environment 109 B.1 Overview 109 B.2 Object Transportation Problem in a Static Environment 111 B.3 Multi-object Transportation using Hybrid System 112 B.4 New Finite State Machines 113 B.4.1 The States of Guider Robots 114 B.4.2 The States of a Pusher Robot 115 B.4.3 The States of a Leader Robot 116 B.5 Simulation Results 118 B.5.1 Simulation Result: An Obstacle 118 B.5.2 Simulation Result: Two Obstacles 120 B.6 Practical Experiment 122 Bibliography 124Docto

Entwicklungsumgebung für Roboterschwärme

In der vorliegenden Arbeit werden der systematische Entwurf und die Entwicklung einer Entwicklungsumgebung für Roboterschwärme beschrieben, die auf die spezifischen Eigenarten solcher Multi-Roboter-Systeme (MRS) eingeht. Kernstück der Entwicklungsumgebung sind eine interpretierte Steuersprache sowie eine dynamische interaktive Arena für Experimente. Die Entwicklungsumgebung vereinfacht den Entwurf von MRS, was in mehreren Experimenten mit verschiedenen Robotern anschaulich dargelegt wird