Bioinspired approaches for coordination and behaviour adaptation of aerial robot swarms
Abstract
Behavioural adaptation is a pervasive component in a myriad of animal societies.
A well-known strategy, known as Levy Walk, has been commonly linked to such
adaptation in foraging animals, where the motion of individuals couples periods of
localized search and long straight forward motions. Despite the vast number of
studies on Levy Walks in computational ecology, it was only in the past decade
that the first studies applied this concept to robotics tasks. Therefore, this Thesis
draws inspiration from the Levy Walk behaviour, and its recent applications to
robotics, to design biologically inspired models for two swarm robotics tasks, aiming
at increasing the performance with respect to the state of the art.
The first task is cooperative surveillance, where the aim is to deploy a swarm so
that at any point in time regions of the domain are observed by multiple robots simultaneously. One of the contributions of this Thesis, is the Levy Swarm Algorithm
that augments the concept of Levy Walk to include the Reynolds’ flocking rules and
achieve both exploration and coordination in a swarm of unmanned aerial vehicles.
The second task is adaptive foraging in environments of clustered rewards. In
such environments behavioural adaptation is of paramount importance to modulate
the transition between exploitation and exploration. Nature enables these adaptive
changes by coupling the behaviour to the fluctuation of hormones that are mostly
regulated by the endocrine system. This Thesis draws further inspiration from Nature and proposes a second model, the Endocrine Levy Walk, that employs an Artificial Endocrine System as a modulating mechanism of Levy Walk behaviour. The
Endocrine Levy Walk is compared with the Yuragi model (Nurzaman et al., 2010),
in both simulated and physical experiments where it shows its increased performance in terms of search efficiency, energy efficiency and number of rewards found.
The Endocrine Levy Walk is then augmented to consider social interactions between
members of the swarm by mimicking the behaviour of fireflies, where individuals attract others when finding suitable environmental conditions. This extended model,
the Endocrine Levy Firefly, is compared to the Levy+ model (Sutantyo et al., 2013)
and the Adaptive Collective Levy Walk Nauta et al. (2020). This comparison is also
made both in simulated and physical experiments and assessed in terms of search
efficiency, number of rewards found and cluster search efficiency, strengthening the
argument in favour of the Endocrine Levy Firefly as a promising approach to tackle
collaborative foraging