Energy-Efficient Strategies for Building Short Chains of Mobile Robots Locally

Brandes, Philipp; Degener, Bastian; Kempkes, Barbara; Heide, Friedhelm Meyer auf der

doi:10.1007/978-3-642-22212-2_13

Cited by 8 publications

(3 citation statements)

References 17 publications

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“…Yet, it has been shown that observer/controllerdriven robots can increase their learning speed imitating each other (Jungmann et al, 2011). Local communication between robots allows for establishing real teams that collaboratively perform tasks such as the exploration of unknown terrain, and that assign each other subtasks in a fair manner -decentralized, without the need for global control (Brandes et al, 2011;Kempkes and Meyer auf der Heide, 2011). In addition, recent work shifted the focus toward task allocation strategies for swarm robotics systems characterized by soft deadlines; these self-organized task allocation schemes aim at minimizing the costs associated with missing the task deadlines, see Khaluf and Rammig (2013) and Khaluf et al (2014).…”

Section: From Individuals To Ensemblesmentioning

confidence: 99%

An Organic Computing Approach to Self-Organizing Robot Ensembles

2016

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Similar to the Autonomous Computing initiative, which has mainly been advancing techniques for self-optimization focusing on computing systems and infrastructures, Organic Computing (OC) has been driving the development of system design concepts and algorithms for self-adaptive systems at large. Examples of application domains include, for instance, traffic management and control, cloud services, communication protocols, and robotic systems. Such an OC system typically consists of a potentially large set of autonomous and self-managed entities, where each entity acts with a local decision horizon. By means of cooperation of the individual entities, the behavior of the entire ensemble system is derived. In this article, we present our work on how autonomous, adaptive robot ensembles can benefit from OC technology. Our elaborations are aligned with the different layers of an observer/controller framework, which provides the foundation for the individuals' adaptivity at system design-level. Relying on an extended Learning Classifier System (XCS) in combination with adequate simulation techniques, this basic system design empowers robot individuals to improve their individual and collaborative performances, e.g., by means of adapting to changing goals and conditions. Not only for the sake of generalizability but also because of its enormous transformative potential, we stage our research in the domain of robot ensembles that are typically comprised of several quad-rotors and that organize themselves to fulfill spatial tasks such as maintenance of building facades or the collaborative search for mobile targets. Our elaborations detail the architectural concept, provide examples of individual self-optimization as well as of the optimization of collaborative efforts, and we show how the user can control the ensembles at multiple levels of abstraction. We conclude with a summary of our approach and an outlook on possible future steps.

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Section: From Individuals To Ensemblesmentioning

confidence: 99%

An Organic Computing Approach to Self-Organizing Robot Ensembles

2016

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“…One of them is the task of straightening a chain of robots in the plane, based on purely local methods; this amounts to our problem for the very special case of a communication graph that is a path, and robots are already sorted by labels. In a considerable sequence of papers, Meyer auf der Heide et al [10]- [22] studied versions of the strategy GO-TO-THE-MIDDLE (GTM), in which each robot moves to the midpoint between its two immediate neighbors. Some of the underlying models are based on discrete rounds, with robots performing (possibly larger) discrete moves; however, Degener et al [14] showed that in a setting with continuous motion and sensing, the variant MOVE-ON-BISECTOR produces a straight, evenly spaced chain in time bounded by O(n); more recently, Brandes et al [22] provided an analysis for continuous GTM and also established an upper bound of O(n) on the distance traveled by a robot, and thus, the overall time.…”

Section: Related Workmentioning

confidence: 99%

“…All robots that are on the central path straighten the path by following the continuous GTM method, for which it is known that the chain of robots converges to a straight line (up to inaccuracy of measurements) [10]- [22]. The rule is simple: every robot moves towards the midpoint of the segment between its two neighbors n l and n r in the doubly linked list; see Figure 5.…”

Section: Straightening a Pathmentioning

confidence: 99%

A parallel distributed strategy for arraying a scattered robot swarm

Krupke

Hemmer

McLurkin

et al. 2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-We consider the problem of organizing a scattered group of n robots in two-dimensional space, with geometric maximum distance D between robots. The communication graph of the swarm is connected, but there is no central authority for organizing it. We want to arrange them into a sorted and equally-spaced array between the robots with lowest and highest label, while maintaining a connected communication network.In this paper, we describe a distributed method to accomplish these goals, without using central control, while also keeping time, travel distance and communication cost at a minimum. We proceed in a number of stages (leader election, initial path construction, subtree contraction, geometric straightening, and distributed sorting), none of which requires a central authority, but still accomplishes best possible parallelization. The overall arraying is performed in O(n) time, O(n 2 ) individual messages, and O(nD) travel distance. Implementation of the sorting and navigation use communication messages of fixed size, and are a practical solution for large populations of low-cost robots.

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