Distributed, Physics-Based Control of Swarms of Vehicles

Abstract. In this paper, we present a novel method for performing collective transport in the presence of obstacles. Three robots are physically connected to an object to be transported from a start to a goal location. The task is particularly challenging because the robots have a heterogeneous perception of the environment. In fact, the goal and the obstacles can be perceived only by some of the robots. Hence, the task requires appropriate negotiation of the direction among the robots. We developed a novel negotiation strategy in order to tackle this challenge. We perform experiments in simulation.In the experiments, we analyze efficiency in an environment with only one obstacle, and robustness in an environment with several obstacles.

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“…The most studied decentralized method in this area are social potentials [3] and artificial physics [4].…”

Section: Introductionmentioning

confidence: 99%

Socially-Mediated Negotiation for Obstacle Avoidance in Collective Transport

Ferrante

Brambilla

Birattari

et al. 2013

Springer Tracts in Advanced Robotics

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“…The advantages of this are enormous -one can transition directly from theory to a successful robot demo, without all the usual parameter tweaking. For an example of such a success (using AP solid), see [5]. To demonstrate the feasibility of applying physics-based analysis techniques t o physics-based systems, we make predictions that support some of our claims regarding the suitability of gas models for our surveillance task.…”

Section: Resultsmentioning

confidence: 85%

Two Formal Gas Models for Multi-agent Sweeping and Obstacle Avoidance

Kerr¹,

Spears²,

Spears³

et al. 2004

Formal Approaches to Agent-Based Systems

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Abstract. The task addressed here is a dynamic search through a bounded region, while avoiding multiple large obstacles, such as buildings. In the case of limited sensors and communication, maintaining spatial coverage -especially after passing the obstacles -is a challenging problem. Here, we investigate two physics-based approaches to solving this task with multiple simulated mobile robots, one based on artificial forces and the other based on the kinetic theory of gases. The desired behavior is achieved with both methods, and a comparison is made between them. Because both approaches are physics-based, formal assurances about the multi-robot behavior are straightforward, and are included in the paper.

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“…The new position r of the sensor is in principle a 3 dimensional vector from the continuum R 3 specifying the (x, y, z) coordinates of the next platform position. In this situation, we use ideas from earlier works that employ "virtual force" or "potential field" methods [7]. In the field approach, one computes a force that compels a sensor to move rather than explicitly calculating the value of all possible next positions and choosing the best.…”

Section: E Computational Methodsmentioning

confidence: 99%

“…In our method, we simultaneously approximate both the information coupling term involving the expectation of h and the collision prevention term f by introducing a function which reduces the value of action sets that involve sensors moving close together. We have chosen to use a physicomimetic force [7] to provide this approximation, although other similar approximations are also valid. Evaluating this force has a very small computational burden, and requires only that a node know the positions of its neighbors.…”

Section: Methodsmentioning

confidence: 99%

Network sensor management for tracking and localization

Hero

Kreucher

2007

2007 10th International Conference on Information Fusion

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Abstract-This paper addresses the problem of sensor management for a large network of agile sensors. Sensor management refers to the process of dynamically retasking agile sensors in response to an evolving environment. Sensors may be agile in a variety of ways, e.g., the ability to reposition, point an antenna, choose sensing mode, or waveform. The goal of sensor management in a large network is to choose actions for individual sensors dynamically so as to maximize overall network utility.Sensor management in the multiplatform setting is a challenging problem for several reasons. First, the state space required to characterize an environment is typically of very high dimension and poorly represented by a parametric form. Second, the network must simultaneously address a number of competing goals. Third, the number of potential taskings grows exponentially with the number of sensors. Finally, in low communication environments, decentralized methods are required.The approach we present addresses these challenges through a novel combination of particle filtering for nonparametric density estimation, information theory for comparing actions, and physicomimetics for computational tractability. The efficacy of the method is illustrated in a realistic surveillance application by simulation, where an unknown number of ground targets are to be detected and tracked by a network of mobile sensors.

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Distributed, Physics-Based Control of Swarms of Vehicles

Cited by 331 publications

References 39 publications

Socially-Mediated Negotiation for Obstacle Avoidance in Collective Transport

Socially-Mediated Negotiation for Obstacle Avoidance in Collective Transport

Two Formal Gas Models for Multi-agent Sweeping and Obstacle Avoidance

Network sensor management for tracking and localization

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