Robot Swarms in an Uncertain World: Controllable Adaptability

TL;DR

This paper presents a probabilistic model for goal-directed robot swarm behavior that enables controlled adaptability without chaos, using entropy-based state detection and predefined adaptation paths.

Contribution

It introduces a novel probabilistic approach to describe and control robot swarm adaptability, reducing complexity and ensuring predictable behavior in uncertain environments.

Findings

Normalized Entropy Index effectively detects system states.

Adaptability paths are limited and programmable.

Model applicable to robots in hazardous environments.

Abstract

There is a belief that complexity and chaos are essential for adaptability. But life deals with complexity every moment, without the chaos that engineers fear so, by invoking goal-directed behaviour. Goals can be programmed. That is why living organisms give us hope to achieve adaptability in robots. In this paper a method for the description of a goal-directed, or programmed, behaviour, interacting with uncertainty of environment, is described. We suggest reducing the structural (goals, intentions) and stochastic components (probability to realise the goal) of individual behaviour to random variables with nominal values to apply probabilistic approach. This allowed us to use a Normalized Entropy Index to detect the system state by estimating the contribution of each agent to the group behaviour. The number of possible group states is 27. We argue that adaptation has a limited number of possible paths between these 27 states. Paths and states can be programmed so that after adjustment to any particular case of task and conditions, adaptability will never involve chaos. We suggest the application of the model to operation of robots or other devices in remote and/or dangerous places.