The I-SWARM Project: Intelligent Small World Autonomous Robots for Micro-manipulation

Seyfried, Jörg; Szymanski, Marc; Bender, Natalie; Estaña, Ramon; Thiel, Michael; Wörn, Heinz

doi:10.1007/978-3-540-30552-1_7

Cited by 68 publications

(52 citation statements)

References 10 publications

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“…is is the objective of I-swarm project [67] which aimed at building a swarm of micro robots. e development of technologies such as MEMS (Micro-Electro-Mechanical Systems) will allow to create small and cheap robots.…”

Section: Towards Real World Applicationsmentioning

confidence: 99%

An Introduction to Swarm Robotics

2013

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Swarm robotics is a �eld of multi-robotics in which large number of robots are coordinated in a distributed and decentralised way. It is based on the use of local rules, and simple robots compared to the complexity of the task to achieve, and inspired by social insects. Large number of simple robots can perform complex tasks in a more efficient way than a single robot, giving robustness and �exibility to the group. In this article, an overview of swarm robotics is given, describing its main properties and characteristics and comparing it to general multi-robotic systems. A review of different research works and experimental results, together with a discussion of the future swarm robotics in real world applications completes this work.

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Section: Towards Real World Applicationsmentioning

confidence: 99%

An Introduction to Swarm Robotics

2013

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“…It is now technically feasible to launch and deploy 1000s of small 100-gram satellites, called femtosats, into Earth orbit so that they can reconfigure into desired formations [5]. Similarly, swarms of robots are suitable for applications like environmental monitoring [6], reconnaissance of dangerous or unknown regions while overcoming obstacles [7], and collectively building, sorting or foraging objects of interest [8]. In this paper, we integrate and implement distributed guidance, navigation and control (GNC) algorithms for such large swarms of agents and validate them using our formation flying testbed (FFT).…”

Section: Introductionmentioning

confidence: 99%

Probabilistic guidance of distributed systems using sequential convex programming

Morgan

Subramanian

Bandyopadhyay

et al. 2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-In this paper, we integrate, implement, and validate formation flying algorithms for large number of agents using probabilistic swarm guidance with inhomogeneous Markov chains and model predictive control with sequential convex programming. Using an inhomogeneous Markov chain, each agent determines its target position during each time step in a statistically independent manner while the swarm converges to the desired formation. Moreover, the swarm is robust to external disturbances or damages to the formation. An optimal control problem is formulated to ensure that the agents reach the target positions while avoiding collisions. This problem is solved using sequential convex programming to determine optimal, collisionfree trajectories and model predictive control is implemented to update these trajectories as new state information becomes available. Finally, we validate the probabilistic swarm guidance and model predictive control algorithms using the formation flying testbed.

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“…Similar methods of hop-counts, which form linear gradients in the organism, were also used in [11] to construct dense objects from autonomously moving sub-units. A continuous gradient approach for navigation of modules based on non-linear gradients was investigated in [12] and in [13] within the I-SWARM project [14]. Based on these swarm techniques, we elaborated a hormone-inspired control paradigm for body formation and body control, aimed for multi-modular robots used in the EU-funded projects SYMBRION [15] and REPLICATOR [16].…”

Section: Introductionmentioning

confidence: 99%

Major Feedback Loops Supporting Artificial Evolution in Multi-modular Robotics

Schmickl

Stradner

Hamann

et al. 2011

New Horizons in Evolutionary Robotics

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In multi-modular reconfigurable robotics it is extremely challenging to develop control software that is able to generate robust but still flexible behavior of the 'robotic organism' that is formed by several independent robotic modules. We propose artificial evolution and self-organization as methodologies to develop such control software. In this article, we present our concept to evolve a self-organized multimodular robot. We decompose the network of feedbacks, that affect the evolutionary pathway and show why and how specific sub-components, which are involved in these feedbacks, should be subject of evolutionary adaptation. Self-organization is a major component of our framework and is implemented by a hormone-inspired controller governing the behavior of singular autonomous modules. We show first results, which were obtained by artificial evolution with our framework, and give an outlook of how the framework will be applied in future research.

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The I-SWARM Project: Intelligent Small World Autonomous Robots for Micro-manipulation

Cited by 68 publications

References 10 publications

An Introduction to Swarm Robotics

An Introduction to Swarm Robotics

Probabilistic guidance of distributed systems using sequential convex programming

Major Feedback Loops Supporting Artificial Evolution in Multi-modular Robotics

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