Task Modelling in Collective Robotics

Kube, C. Ronald; Zhang, Hong

doi:10.1007/978-1-4757-6451-2_3

Cited by 35 publications

(43 citation statements)

References 23 publications

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“…The decomposition of the mission shall be organized with expert knowledge, which justifies a userdefined decomposition as done in most projects [3], [12]. The tasks can be modeled as dependent tasks, which have a child and/or parent task and are connected via time by them, or a independent tasks, which can be executed on their own.…”

Section: B Task Modeling and Assignmentmentioning

confidence: 99%

“…The majority of research in robot teams considers homogeneous robots, most of them based on wheeled locomotion. The investigated tasks differ in the complexity of structure and cooperation, starting from basic tasks as foraging [1] or exploration of an area without a specific cooperation [2] up to problems with increase in communication and synchronization demands, e.g., cooperative box pushing [3] or cooperative surveillance of an area [4], [5] or soccer playing [6], [7], [8]. A classification of different stages of cooperation is given in [9].…”

Section: Introductionmentioning

confidence: 99%

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Cooperation of heterogeneous, autonomous robots: A case study of humanoid and wheeled robots

Kiener

Stryk

2007

2007 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-In this paper we present a case study of cooperation of a strongly heterogeneous robot team, composed of a highly articulated humanoid robot and a wheeled robot with largely complementing and some competing capabilities. By combining two strongly heterogeneous robots the diversity of accomplishable tasks increases as the variety of sensors and actuators in the robot systems is extended compared with a team consisting of homogeneous robots. The scenario describes a tightly cooperative task, where the humanoid robot and the wheeled robot follow for a long distance a ball, which is kicked finally by the humanoid robot into a goal. The task can be fulfilled successfully by combining the abilities of both robots. For task distribution and allocation, a newly developed objective function is presented which is based on a proper modeling of the sensing, perception, motion and onboard computing capabilities of the cooperating robots. Aspects of reliability and fault tolerance are considered. I. INTROWith the growing importance of autonomous mobile robots in industrial and research applications the need to execute successfully challenging missions and tasks has also grown. To fulfill a large diversity of tasks with a sufficient reliability in the robot system, teams of robots are used instead of single highly specialized robots. The majority of research in robot teams considers homogeneous robots, most of them based on wheeled locomotion. The investigated tasks differ in the complexity of structure and cooperation, starting from basic tasks as foraging [1] or exploration of an area without a specific cooperation [2] up to problems with increase in communication and synchronization demands, e.g., cooperative box pushing [3] or cooperative surveillance of an area [4], [5] or soccer playing [6], [7], [8]. A classification of different stages of cooperation is given in [9]. A homogeneous robot team is usually equipped with identical sensors and actuators which usually differ only slightly, e.g., because of different wear and tear. Therefore, the diversity of tasks which can be accomplished by a homogeneous robot team is still quite limited. This drawback can be overcome by a team of heterogeneous robots, each or several of them equipped with different sensing, perception, motion and onboard computing capabilities. Several application have been investigated with robots, which differ only slightly in their capabilities. Although these robots are not fully identical, commonly they are still considered to form J. Kiener is with the Simulation,

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Section: B Task Modeling and Assignmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cooperation of heterogeneous, autonomous robots: A case study of humanoid and wheeled robots

Kiener

Stryk

2007

2007 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…Additionally, they show that, among the direct communications strategies, a higher complexity does not forcedly result in an advantage. Similarly, Kube and Zhang (1997) describe a collective task in which no direct communication is used among the robots, which are anyway able to coordinate their activities using some form of indirect communication. Stigmergy is the main coordination mechanism employed in many other works relevant for swarm robotics research (Beckers et al, 1994, Holland andMelhuish, 1999).…”

Section: From Insects To Robotsmentioning

confidence: 99%

“…These strategies and protocols need not, however, be particularly complex. In many cases, simple forms of communication-or no explicit communication at all-are enough to obtain the coordination of the activities of the group (Kube and Zhang, 1997). This is the case for swarm robotics, which focuses on local and simple communication paradigms, that can gracefully scale up with the number of agents involved.…”

Section: Introductionmentioning

confidence: 99%

Self-organisation and communication in groups of simulated and physical robots

Trianni

Dorigo

2006

Biol Cybern

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In social insects, both self-organisation and communication play a crucial role for the accomplishment of many tasks at a collective level. Communication is performed with different modalities, which can be roughly classified in three classes: indirect (stigmergic) communication, direct interactions and direct communication. The use of stigmergic communication is predominant in social insects (e.g., the pheromone trails in ants), where however also direct interactions (e.g., antennation in ants) and direct communication (e.g., the waggle dance in honey bees) can be observed. Taking inspiration from insect societies, we present an experimental study of self-organising behaviours for a group of robots, which exploit communication to coordinate their activities. Artificial evolution is responsible for the synthesis in a simulated environment of the robot's neural controllers, which are subsequently tested on physical robots. We study different communication strategies among the robots: no direct communication, handcrafted signalling and a completely evolved approach. We show that the latter is the most efficient, suggesting that artificial evolution can produce behaviours that are more adaptive than those obtained with conventional design methodologies. Moreover, we show that the evolved controllers produce a self-organising systems that is robust enough to be tested on physical robots, notwithstanding the huge gap between simulation and reality.

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“…The cooperative transport of large objects by groups of comparatively small robots is a canonical task studied in collective robotics [5,8,10,4]. Algorithms for cooperative transport need to cope with individual failure and different environmental conditions.…”

Section: Introductionmentioning

confidence: 99%

Using Google Glass in Human–Robot Swarm Interaction

Kapellmann-Zafra

Chen

Groß

2016

Towards Autonomous Robotic Systems

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Abstract. We study how a human operator can guide a swarm of robots when transporting a large object through an environment with obstacles. The operator controls a leader robot that influences the other robots of the swarm. Follower robots push the object only if they have no line of sight of the leader. The leader represents a way point that the object should reach. By changing its position over time, the operator effectively guides the transporting robots towards the final destination. The operator uses the Google Glass device to interact with the swarm. Communication can be achieved via either touch or voice commands and the support of a graphical user interface. Experimental results with 20 physical e-puck robots show that the human-robot interaction allows the swarm to transport the object through a complex environment.

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Task Modelling in Collective Robotics

Cited by 35 publications

References 23 publications

Cooperation of heterogeneous, autonomous robots: A case study of humanoid and wheeled robots

Cooperation of heterogeneous, autonomous robots: A case study of humanoid and wheeled robots

Self-organisation and communication in groups of simulated and physical robots

Using Google Glass in Human–Robot Swarm Interaction

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