Cooperative transport by ants and robots

Kube, C. Ronald; Bonabeau, Eric

doi:10.1016/s0921-8890(99)00066-4

Cited by 345 publications

(232 citation statements)

References 32 publications

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“…Such systems are common in insect as well as human societies, for example, transport of objects too heavy or awkward to be lifted by a single ant, flying the space shuttle, playing a soccer match, and so on. Collaboration in a group of robots has been studied by several groups [14,17,22,24,29,30]. We will focus on a specific case study initiated by Martinoli and collaborators [22] and studied in detail by Ijspeert et al [14], that takes a swarm intelligence approach to collaboration.…”

Section: Collaboration In Robotsmentioning

confidence: 99%

“…The conservation of the total number of robots is given by Equation 18. We are interested in the steady-state properties of system (Equations [16][17], that is, dN s /dt = dN av /dt = 0. Steady-state solutions describe the long-term, timeindependent behavior of the system.…”

Section: Interference Effectsmentioning

confidence: 99%

“…In the last few years, the swarm intelligence control principles have been successfully applied to a series of case studies in collective robotics: aggregation [2,20,21] and segregation [13], beacon and odor localization [11,12], collaborative mapping [4], collaborative transportation [15,17], work division and task allocation [1,16], and flocking and foraging [23]. All these works have been performed using groups of simple, autonomous robots or embodied simulated agents, exploiting local communication forms among teammates (implicit, through the environment, or explicit, wireless communication), and fully distributed control.…”

Section: Introductionmentioning

confidence: 99%

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A Macroscopic Analytical Model of Collaboration in Distributed Robotic Systems

et al. 2001

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In this article, we present a macroscopic analytical model of collaboration in a group of reactive robots. The model consists of a series of coupled differential equations that describe the dynamics of group behavior. After presenting the general model, we analyze in detail a case study of collaboration, the stick-pulling experiment, studied experimentally and in simulation by Ijspeert et al. [Autonomous Robots,11,. The robots' task is to pull sticks out of their holes, and it can be successfully achieved only through the collaboration of two robots. There is no explicit communication or coordination between the robots. Unlike microscopic simulations (sensor-based or using a probabilistic numerical model), in which computational time scales with the robot group size, the macroscopic model is computationally efficient, because its solutions are independent of robot group size. Analysis reproduces several qualitative conclusions of Ijspeert et al.: namely, the different dynamical regimes for different values of the ratio of robots to sticks, the existence of optimal control parameters that maximize system performance as a function of group size, and the transition from superlinear to sublinear performance as the number of robots is increased.

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Section: Collaboration In Robotsmentioning

confidence: 99%

Section: Interference Effectsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Macroscopic Analytical Model of Collaboration in Distributed Robotic Systems

et al. 2001

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“…Kube and Zhang (1993a,b) studied a distributed approach to let a group of simple robots find and push a prey towards a light. Kube and Bonabeau (2000), on a follow-up of Kube and Zhang's research, evaluated the sensitivity of their robotic system to the prey's geometry by comparing the performance on prey of different shapes. They report that when the prey was a small cuboid, "as the number of robots increased the task took longer to complete as the robot interference was high since the limited box side space created competition (a) Example of initial placement: the prey (black) has to be transported towards a light-emitting beacon acting as a target (e.g., a robot nest).…”

Section: Introductionmentioning

confidence: 99%

Cooperative Transport of Objects of Different Shapes and Sizes

Groß

Dorigo

2004

Ant Colony Optimization and Swarm Intelligence

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Abstract. This paper addresses the design of control policies for groups of up to 16 simple autonomous mobile robots (called s-bots) for the cooperative transport of heavy objects of different shapes and sizes. The s-bots are capable of establishing physical connections with each other and with the object (called prey). We want the s-bots to self-assemble into structures which pull or push the prey towards a target location. The s-bots are controlled by neural networks that are shaped by artificial evolution. The evolved controllers perform quite well, independently of the shape and size of the prey, and allow the group to transport the prey towards a moving target. Additionally, the controllers evolved for a relatively small group can be applied to larger groups, making possible the transport of heavier prey. Experiments are carried out using a physics simulator, which provides a realistic simulation of real robots, which are currently under construction.

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“…While collaboration may be essential for some tasks, we are interested in studying tasks that can be done by a single robot, but where using multiple robots can potentially increase performance by decreasing the time to complete the task and/or by increasing the reliability. Sample tasks include placing a sensor network [16], cleaning up trash [12], pushing boxes [9], or detecting odors [5].…”

Section: Related Workmentioning

confidence: 99%