Bootstrapped Motion of an Agent on an Adaptive Resource Landscape

Trung V Phan,Gao Wang,Liyu Liu,Robert H Austin

doi:10.3390/sym13020225

Abstract

We theoretically show that isolated agents that locally and symmetrically consume resources and sense positive resource gradients can generate constant motion via bootstrapped resource gradients in the absence of any externally imposed gradients, and we show a realization of this motion using robots. This self-generated agent motion can be coupled with neighboring agents to act as a spontaneously broken symmetry seed for emergent collective dynamics. We also show that in a sufficiently weak externally imposed gradient, it is possible for an agent to move against an external resource gradient due to the local resource depression on the landscape created by an agent. This counter-intuitive boot-strapped motion against an external gradient is demonstrated with a simple robot system on an light-emitting diode (LED) light-board.

Highlights

In biology, it is often assumed that agents do not actively influence their environment, but rather passively respond to the environment
The robots moved only in response to the local light-intensity gradient at their positions on the light-emitting diode (LED) light-board with rotational noise, as shown in the movie contained in the Supplementary movie smovie01.mp4
We have found that, due to spontaneous symmetry breaking, an agent can bootstrap itself to a constant motion via a self-generated gradient in a recovering environment and in a diffusive environment

Summary

Introduction

It is often assumed that agents do not actively influence their environment, but rather passively respond to the environment. While the agents’ movements are changed by chemotaxis, in the absence of any gradients, it is assumed that the agents still have a zero-gradient inherent speed Chemotactic agents, such as Escherichia coli and Dictyostelium, can explore complex topologies, such as mazes and fractals, much more rapidly and efficiently than a response to an imposed gradient alone would predict [5,6]. This occurs if agents do not just follow an imposed gradient passively, and influence the surrounding environment by consuming and producing chemicals [7,8,9]. The step will be to let the agents interact with each other and look for emergent large-scale symmetry breaking

Approach

Passively Diffusive Environment

Retrograde Movement against an Applied Gradient

Actively Recovering Environment

Results

Conclusions