51 research outputs found
Programmable reconfiguration of Physarum machines
Plasmodium of Physarum polycephalum is a large cell capable of solving
graph-theoretic, optimization and computational geometry problems due to its
unique foraging behavior. Also the plasmodium is unique biological substrate
that mimics universal storage modification machines, namely the
Kolmogorov-Uspensky machine. In the plasmodium implementation of the storage
modification machine data are represented by sources of nutrients and memory
structure by protoplasmic tubes connecting the sources. In laboratory
experiments and simulation we demonstrate how the plasmodium-based storage
modification machine can be programmed. We show execution of the following
operations with active zone (where computation occurs): merge two active zones,
multiple active zone, translate active zone from one data site to another,
direct active zone. Results of the paper bear two-fold value: they provide a
basis for programming unconventional devices based on biological substrates and
also shed light on behavioral patterns of the plasmodium
Routing Physarum with electrical flow/current
Plasmodium stage of Physarum polycephalum behaves as a distributed dynamical
pattern formation mechanism who's foraging and migration is influenced by local
stimuli from a wide range of attractants and repellents. Complex protoplasmic
tube network structures are formed as a result, which serve as efficient
`circuits' by which nutrients are distributed to all parts of the organism. We
investigate whether this `bottom-up' circuit routing method may be harnessed in
a controllable manner as a possible alternative to conventional template-based
circuit design. We interfaced the plasmodium of Physarum polycephalum to the
planar surface of the spatially represented computing device, (Mills' Extended
Analog Computer, or EAC), implemented as a sheet of analog computing material
whose behaviour is input and read by a regular 5x5 array of electrodes. We
presented a pattern of current distribution to the array and found that we were
able to select the directional migration of the plasmodium growth front by
exploiting plasmodium electro-taxis towards current sinks. We utilised this
directional guidance phenomenon to route the plasmodium across its habitat and
were able to guide the migration around obstacles represented by repellent
current sources. We replicated these findings in a collective particle model of
Physarum polycephalum which suggests further methods to orient, route, confine
and release the plasmodium using spatial patterns of current sources and sinks.
These findings demonstrate proof of concept in the low-level dynamical routing
for biologically implemented circuit design
A morphological adaptation approach to path planning inspired by slime mould
Path planning is a classic problem in computer science and robotics which has recently been implemented in unconventional computing substrates such as chemical reaction–diffusion computers. These novel computing schemes utilise the parallel spatial propagation of information and often use a two-stage method involving diffusive propagation to discover all paths and a second stage to highlight or visualise the path between two particular points in the arena. The true slime mould Physarum polycephalum is known to construct efficient transport networks between nutrients in its environment. These networks are continuously remodelled as the organism adapts its body plan to changing spatial stimuli. It can be guided towards attractant stimuli (nutrients, warm regions) and it avoids locations containing hazardous stimuli (light irradiation, repellents, or regions occupied by predatory threats). Using a particle model of slime mould we demonstrate scoping experiments which explore how path planning may be performed by morphological adaptation. We initially demonstrate simple path planning by a shrinking blob of virtual plasmodium between two attractant sources within a polygonal arena. We examine the case where multiple paths are required and the subsequent selection of a single path from multiple options. Collision-free paths are implemented via repulsion from the borders of the arena. Finally, obstacle avoidance is implemented by repulsion from obstacles as they are uncovered by the shrinking blob. These examples show proof-of-concept results of path planning by morphological adaptation which complement existing research on path planning in novel computing substrates
Emergence of self-organized amoeboid movement in a multi-agent approximation of Physarum polycephalum
The giant single-celled slime mould Physarum polycephalum exhibits complex morphological adaptation and amoeboid movement as it forages for food and may be seen as a minimal example of complex robotic behaviour. Swarm computation has previously been used to explore how spatio-temporal complexity can emerge from, and be distributed within, simple component parts and their interactions. Using a particle-based swarm approach we explore the question of how to generate collective amoeboid movement from simple non-oscillatory component parts in a model of P. polycephalum. The model collective behaves as a cohesive and deformable virtual material, approximating the local coupling within the plasmodium matrix. The collective generates de-novo and complex oscillatory patterns from simple local interactions. The origin of this motor behaviour distributed within the collective rendering is morphologically adaptive, amenable to external influence and robust to simulated environmental insult. We show how to gain external influence over the collective movement by simulated chemo-attraction (pulling towards nutrient stimuli) and simulated light irradiation hazards (pushing from stimuli). The amorphous and distributed properties of the collective are demonstrated by cleaving it into two independent entities and fusing two separate entities to form a single device, thus enabling it to traverse narrow, separate or tortuous paths. We conclude by summarizing the contribution of the model to swarm-based robotics and soft-bodied modular robotics and discuss the future potential of such material approaches to the field. © 2012 IOP Publishing Ltd
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