Abstract
In this paper a microscopic, non-discrete, mathematical model based on stigmergy for predicting the nodal aggregation dynamics of decentralized, autonomous robotic swarms is proposed. The model departs from conventional applications of stigmergy in bioinspired path-finding optimization, serving as a dynamic aggregation algorithm for nodes with limited or no ability to perform discrete logical operations, aiding in agent miniaturization. Time-continuous simulations were developed and carried out where nodal aggregation efficiency was evaluated using the following metrics: time to aggregation equilibrium, agent spatial distribution within aggregate (including average inter-nodal distance, center of mass of aggregate deviation from target), and deviation from target agent number. The system was optimized using cost minimization of the above factors through generating a random set of cost datapoints with varying initial conditions (number of aggregates, agents, field dimensions, and other specific agent parameters) where the best-fit scalar field was obtained using a random forest ensemble learning strategy and polynomial regression. The scalar cost field global minimum was obtained through basin-hopping with L-BFGS-B local minimization on the scalar fields obtained through both methods. The proposed optimized model describes the physical properties that non-digital agents must possess so that the proposed aggregation behavior emerges, in order to avoid discrete state algorithms aiming towards developing agents independent of digital components aiding to their miniaturization.
Highlights
Biological behaviors have been recently serving as the basis for multiple different algorithms designed for multi-agent robotic systems coordination
This paper provides an aggregation model based on environment mediation and probabilistic aggregation that achieves the relaxation of the finite state machine implementation of the previously mentioned approaches
Each task is associated with a random color seen in the legend
Summary
Biological behaviors have been recently serving as the basis for multiple different algorithms designed for multi-agent robotic systems coordination. This field is referred to as Swarm Robotics (SR) where numerous constructed physical robots (nodes/agents) are controlled such that certain behaviors emerge [1]. Swarm agents are usually relatively small in scale and low cost, ideally deprived of the ability to perform real time complicated calculations and attain global information about their environment [2,3]. SR systems range from areal heterogeneous surveillance swarms designed for wide coverage tracking and scouting
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