Stefan Bosse - Material-integrated cluster computing in self-adaptive robotic materials using mobile multi-agent systems

Material-integrated cluster computing in self-adaptive robotic

materials using mobile multi-agent systems

Stefan Bosse

1,2

•

Dirk Lehmhus

Received: 16 July 2018 / Revised: 23 November 2018 / Accepted: 19 December 2018

Ó Springer Science+Business Media, LLC, part of Springer Nature 2019

Abstract

Recent trends like internet-of-things (IoT) and internet-of-everything (IoE) require new distributed computing and com-

munication approaches as size of interconnected devices moves from a cm

- to the sub-mm

-scale. Technological advance

behind size reduction will facilitate integration of networked computing on material rather than structural level, requiring

algorithmic and architectural scaling towards distributed computing. Associated challenges are linked to use of low

reliability, large scale computer networks operating on low to very low resources in robotic materials capable of per-

forming cluster computing on micro-scale. Networks of this type need superior robustness to cope with harsh conditions of

operation. These can be provided by self-organization and—adaptivity. On macro scale, robotic materials afford uniﬁed

distributed data processing models to allow their connection to smart environments like IoT/IoE. The present study

addresses these challenges by applying mobile Multi-agent systems (MAS) and an advanced JavaScript agent processing

platform (JAM), realizing self-adaptivity as feature of both data processing and the mechanical system itself. The MAS’

task is to solve a distributed optimization problem using a mechanically adaptive robotic material in which stiffness is

increased via minimization of elastic energy. A practical realization of this example necessitates environmental interaction

and perception, demonstrated here via a reference architecture employing a decentralized approach to control local

property change in service based on identiﬁcation of the loading situation. In robotic materials, such capabilities can

support actuation and/or lightweight design, and thus sustainability.

Keywords Pervasive computing  Ubiquitous computing  Agents  Optimization  Material informatics  Self-organizing

systems  Self-adaptive systems

1 Introduction

Cluster computing is commonly related to large-scale

networks composed of powerful computers and servers

connected by reliable high-bandwidth interconnections

and the Internet. There is an ongoing trend to create big

machines providing high storage, memory, and CPU

capacities towards Cloud computing and data centers.

This is the macro- scale level of computing. But there is

also an emerging micro-scale level of computing, using

low-resource and miniaturized computers towards the

scale deployed in material-integrated computer

networks and the internet-of-things [1]. The term material

is related to the micro-size-scale level (element size about

), and structures to the meso-size-scale level (ele-

ment size about cm

In the last decades there was a shift from passive single

sensors and centralized sensor data processing towards

large networks [2] of smart sensors equipped with Infor-

mation-Communication-Technologies (ICT) forming sen-

sorial materials as one class of material-integrated

intelligent systems (MIIS) [3]. Furthermore, there was a

signiﬁcant increase of the sensor density in sensor networks

integrating sensors and ICT in materials [3], used, e.g., for

structural health monitoring (SHM) [4] or in smart textiles.

& Stefan Bosse

sbosse@uni-bremen.de

Department of Mathematics and Computer Science,

University of Bremen, 28359 Bremen, Germany

Faculty of Computer Science, University of Koblenz-Landau,

56070 Koblenz, Germany

Fraunhofer IFAM, 28359 Bremen, Germany

123

Cluster Computing

https://doi.org/10.1007/s10586-018-02894-x

(0123456789().,-volV)(0123456789().,-volV)

Finally, micro system technologies enable the integration

of actuation in materials creating robotic materials [5], e.g.,

using thermoplastic actuators [6] to modify structures.

Robotic materials are characterized by a tight coupling

of sensing, actuation, computation, and communication.

Data processing in robotic materials can be considered as

cluster computing on micro-scale level. Sensorial and

robotic materials are operating under harsh environmental

conditions requiring resilient behaviour, captured by the

entire design of the ICT architecture and distributed

algorithms.

Load-bearing structures are typically designed towards

relevant load cases known at design-time. New technolo-

gies enabling the design of structures that change material

properties in service in response to load change (i.e., using

robotic materials) could raise additional weight saving

potentials, extending the lifetime, and increasing opera-

tional safety. Thus, self-adaptive robotic materials support

lightweight design and sustainability inspired by bionic

design, e.g., by a top-down approach based on the struc-

ture, load, and function with similarity classiﬁcation [7].

But still bionic design has to consider speciﬁc load cases

and perform classiﬁcation in advance, e.g., performed by

the ELiSE design framework [8]. Examples of cross-sec-

torial and material-independent structural lightweight

constructions propose weight savings about 20–50% [9].

Our approach aims to overcome this limitation.

Structures composed of simple and unreliable actuators

are difﬁcult to control due to non-linearity and a lack of

physical models. One concern regarding active smart cel-

lular structures is the correlated and self-organizing control

of cells’ responses, and the underlying informational

organization providing robustness.

Robustness and real-time capabilities require some kind

of self-organizing and self-adapting computational model

instead of the functional models commonly used (e.g., in

control theory).

The adaptation of mechanical actuated structures to

varying load situations based on external perception or

proprioception that is considered in this work can be

treated as an optimization problem that will be controlled

in this work by multi-agent systems.

The next Sect. 2 outlines the general problem descrip-

tion of distributed computing in material-integrated ICT

systems and the novelty of this work. Section 3 discusses

data processing integrated in materials and structures from

a technical point of view. Robotic materials and the ref-

erence architecture used in this work is described in

Sect. 4. The optimization problem addressed in this work is

introduced in Sect. 5, and the MAS implementation is

described in Sect. 6. Sections 7,8, and 9 address the agent

and simulation platform. Finally, Sects. 10 and 11 show

and evaluate a use-case study.

2 Problem description and contribution

An overview of the concepts addressed in this work is

shown in Fig. 1. Assuming large-scale sensor- and actuator

networks (i.e., sensorial and robotic materials) that are

integrated in materials (or microscopic structures), dis-

tributed computing problems are solved by self-* multi-

agent systems, ﬁnally performing distributed optimization

(adaptation of mechanical properties of a robotic material).

The four upper layers (green boxes) are addressed in this

work.

The lower four layers are the platform and deployment

ﬁelds of the proposed distributed computing approach

using mobile agents, not addressed in this work.

Optimization problems are commonly solved mathe-

matically and numerically [10, 11] or treated as global

problems [6, 12] that can only be solved by processing the

entire sensor data set in each solver iteration. But the

transition to distributed approaches with local processing is

necessary to reach scaling of large systems in the future

consisting of Thousands and Millions of small perceptive

and computational nodes (embedded computers). These

tiny computational units are characterized by their low

computational power and data storage capacity.

This work addresses distributed information processing

integrated in materials and structures posing self-adaptivity

Fig. 1 Overview of the concepts fusioned in this work (lower three

levels are the deployment ﬁelds)

Cluster Computing

123

of the material using a MAS approach with algorithms

based on multi-phase topology optimization (MPTO) [13]

and simulated annealing, which can be considered as a

bionic structural optimization approach.

On the macro-scale level, agents and distributed agent-

based systems are already deployed successfully in

heterogeneous large environments, e.g., production and

manufacturing processes [14, 15], facing adaptive manu-

facturing, maintenance, evolvable assembly systems,

quality control, and energy management aspects, and in

sensing applications, e.g., monitoring of mechanical

structures and devices [16]. Finally, the paradigm of

industrial agents meeting the requirements of modern

industrial applications by integrating sensor networks was

introduced in [17]. In the present study, we show that

agents can be deployed successfully on the micro-scale

level, too.

The central approach in this work focuses on mobile

agents solving an optimization problem by a divide-and-

conquer approach. They pose the ability to support mobile

reconﬁgurable code embedding the agent behaviour, the

agent data, the agent conﬁguration, and the current agent

control state, ﬁnally encapsulated in a portable textual

representation.

In this work JavaScript code (AgentJS) is used and

executed by the JAM platform [18]. The code is capable of

migration between nodes in the network required for

autonomous distributed data processing. This approach

requires only a minimal agent processing platform service

(APPS). The AgentJS code can be directly executed by the

underlying JS VM (e.g., nodejs, jxcore, JVM, webview for

mobile App. development, or spidermonkey used in

browsers).

On the one hand, robotic materials provide internal and

external perception that can be used in a wide range of

sensing applications on the Internet (e.g., product life-cycle

management). Robotic Materials will be part of larger

networks, i.e., the IoT. On the other hand, robotic materials

can proﬁt from environmental information, e.g., collected

by mobile devices and crowd sensing. Therefore, these

material-integrated computational networks should be

connected to a local Intranet or to the global Internet (see

Fig. 2, left side).

One of the major challenges in distributed sensing and

control systems is the derivation of meaningful information

from sensor data. Often the sensors of mobile consumer

devices (such as accelerometer, humidity, light, battery,

temperature, and location) suffer from a poor quality.

Distributed sensor fusion can be applied to improve the

statistical signiﬁcance of such sensor signals by collecting

sensor data in a region of interest from multiple devices.

Fusion can proﬁt from machine learning (ML), which

usually bases on classiﬁcation algorithms derived from

supervised machine learning or pattern recognition using,

e.g., self-organizing [2] and distributed multi-agent sys-

tems with less or no a-priori knowledge of the

environment.

The IoT, material-integrated ICT networks, mobile, and

Cloud environments differ signiﬁcantly in terms of

resources (computational power and data storage). The IoT

and mobile networks consist of a large number of low-

resource devices interacting with the real world and having

strictly limited storage capacities, energy, and computing

power, and the Cloud consists of large-scale computers

with arbitrary and extensible computing power and storage

capacities in a basically virtual world.

A uniﬁed and common data processing and communi-

cation methodology is required to merge the IoT with

Cloud environments seamlessly, which can be fulﬁlled by

mobile agent-based computing proposed in this work.

The scalability of complex ubiquitous applications using

such large-scale cloud-based and wide area distributed

networks deals with systems deploying thousands up to a

million agents.

Considering such strong heterogeneous environments

with computers ranging from 1 MIPS computational power

and 1 MB RAM to 1 GIPS and 1GB RAM a hybrid dual-

platform approach have to be used for agent processing

(Fig. 2, right side).

Addressing the Internet, mobile networks and the IoT,

the

JAM platform [18] is used offering agents directly

implemented in JavaScript (AgentJS) program code hold-

ing the entire control and data state of an agent

Agent processing on very-low resource platforms is

performed with a stack-based processor approach (exe-

cuting AgentFORTH code) featuring hardware implemen-

tation and advanced token-based agent process scheduling

using the AFVM platform [19].

Both program code models base on the same behaviour

and agent behaviour model (ATG/AAPL) and can be

transformed to each other. The agents themselves conform

to the mobile processes model introduced by Milner [20]

ensuring seamless agent mobility. The code can be modi-

ﬁed by the agent itself using code morphing techniques

required for behaviour adaptation (directly supported by

JavaScript Just-in-time and Bytecode Compiler VM

platforms).

This work adds to earlier work [21] the following

extensions and novelties:

– Distributed and decentralized solving of mechanical

optimization problems using different algorithms;

– Self-* multi-agent system performing distributed opti-

mization in a robotic material providing self-adaptation

based on varying load situations and mechanical

defects;

Cluster Computing

123

– Reference architecture of a robotic material composed

of a mesh-grid network of agent processing platforms;

– Different enhanced distributed mechanical optimization

algorithms and their evaluation applied to robotic

materials that are used to meet mechanical constraints

under varying load and damage situations (e.g. holes);

– Multi-domain simulation with tight coupling of phys-

ical and computational agent models.

The next sections offer an introduction to material-inte-

grated intelligent systems and the robotic material refer-

ence architecture, followed by an introduction of the

proposed distributed optimization algorithms and their

implementation with MAS. Finally, an evaluation of the

distributed MAS optimization is performed by a multi-

domain simulation (coupling physical and computational

models) of a JAM network embedded in a robotic material.

3 Material-integrated intelligent systems

Material-integrated intelligent systems feature materials

with embedded data processing, communication, sensors,

actuators, and energy supply. These materials can be

classiﬁed in:

Fig. 2 Uniﬁed distributed

information processing in

heterogeneous MIIS/IoT/Cloud

environments with mobile

agents using a hybrid platform

framework consisting of the

JavaScript agent machine

platform (JAM) and the low-

resource AgentFORTH machine

(AFVM)

Cluster Computing

123

1. Sensorial materials providing intrinsic or environmen-

tal perception capabilities by coupling sensing, com-

putation, and communication.

2. Robotic materials providing intelligent perception and

mechanical adaptation capabilities by coupling sens-

ing, actuation, computation, and communication.

3. Self-adaptive robotic materials providing a closed

control loop using perception to adapt the material/

structure to changing load situations or defects.

All three classes use large-scale autonomous distributed

networks with embedded systems characterized by low

power consumption, low data storage resources, and low

processing power. Commonly, the network nodes are self-

The main ﬁelds of application of Sensorial Materials are

Load and Structural Health Monitoring, the ﬁeld of appli-

cation of robotic materials are mechanical adaptive

structures.

Intelligence is provided on software level featuring self-*

capabilities. Adaptivity, reconﬁguration, and healing in the

presence of technical failures (data processing, networking,

communication, low energy, sensor faults) are elementary

features of resilient systems in safety-critical environments.

Load-bearing structures are typically designed towards

relevant load cases assuming static shape and ﬁxed sets of

materials properties decided upon during design and

materials selection. New technologies enabling the design

of structures that could change local properties in service in

response to load change could raise additional weight

saving potentials, thus supporting lightweight design and

sustainability. Materials with such capabilities must nec-

essarily be composite in the sense of a heterogeneous

build-up, exhibiting, e.g., an architecture consisting of

networks with numerous active cells providing sensing,

signal and data processing, communication, and actuation/

stimulation capability [4] forming robotic materials and

structures [22]. One example for such a material is a spe-

cial class of polymers being capable to change their elas-

ticity based on the inﬂuence of optical, thermal, or

electrical ﬁelds [5].

Although no speciﬁc technology assumptions were

made in this work, there are already micro-scale computers

suitable for such material-integrated computing. An

example is the Micro MOTE M3 [23] providing about

1MIPS computing power and 4 kB data memory with

0.1 mm

chip area consuming less 100 lW power. The

FreeScale KL03 provides 50 MIPS/2 kB with 4 mm

chip

area and 3 mW power consumption. Both microcontrollers

base on ARM architecture.

4 Robotic material and reference

architecture

The reference architecture in this work consists of a three-

dimensional grid network of controller nodes with physical

and communication connectivity between nodes, shown in

Fig. 3. The physical links are actuators that can change

mechanical properties of the material (e.g., stiffness). No

technology-speciﬁc assumptions were made regarding

communication links and actuators.

In our understanding, a self-adaptive robotic material

provides the following major features:

1. Perception using various kinds of sensors, e.g., measuring

of strain, displacement, temperature, pressure, forces;

2. Capability of changing local material and structure

properties by actuators, e.g., stiffness or damping

variation;

3. Integrated networks of information processing and

communication technologies (ICT);

4. Distributed approach: Local sensor processing, aggre-

gation, and actuator control; Global cooperation and

coordination solving optimization.

The general model of such a robotic material is shown in

Fig. 3. It is assumed that the material (on micro-scale level) or

structure (on meso-scale level) consists of volume elements

(bounded regions of the material) that are connected with

neighbourhood elements via links. The links should provide

some kind of sensing (e.g., measuring the strain or displace-

ment along the link main axis) and some kind of material or

structure control providing a controllable actuator (e.g.,

modifying the stiffness of the link). A link can be a discrete

part or a continuous region of the material. A set of actuators is

connected to each node. Two nodes share an actuator (e.g., a

damped spring of variable stiffness and damping).

Each element contains an embedded computer that is

considered as a node in a mesh-grid communication network.

Each node provides some kind of data processing (processor

or digital logic RTL), data and program memory, commu-

nication, energy supply and energy management. Since the

distributed sensing and control of the material should be

performed by an agent-based approach each node has to

provide an agent processing platform (APP).

The APP is executed typically on a virtual machine,

discussed in Sect. 7. Agents have to be able to access

sensors and actuators by a hardware abstraction layer

(HAL), optimally provided by the APP.

It is assumed that the nodes are organized in an ad-hoc

way. Technical failures of single nodes is considered as the

common and may not affect the operation of the robotic

Cluster Computing

123

material and its capability to satisfy some global objective

function. This implies a self-organizing and self-adaptive

approach with respect to the connectivity structure has to

be used to create some kind of holonic system and is the

ﬁrst level of intelligence in a smart system.

Each node controls a number of actuated springs and

dampers connected to neighbouring nodes as shown in

Fig. 3d. Each spring has a virtual strain (displacement)

sensor attached, e.g., a strain gauge sensor. Together with

the current stiffness parameter value, the spring energy can

be computed. It is assumed that the stiffness of each spring

can be varied between a lower and an upper boundary

]. A technically reasonable range is ± 50% around a

nominal stiffness value. The missing sensor values (spring

displacement and current stiffness) must be retrieved from

neighbour nodes.

The models of the actuator (relation of relative dis-

placement l  l

and force f

i;j

between two nodes) and the

sensor (strain gauge signal S

i;j

) are given in Eqs. 1 and 2,

respectively.

i;j

ðl; s; dÞ¼k

sðl  l

Þdu ð1Þ

i;j

ðf

i;j

Þ¼S

i;j

GF; with GF ¼ DR=ðeR

Þð2Þ

with s: stiffness parameter, k: a spring constant, d: damping

parameter, u: relative velocity between nodes i and j, R:

electrical resistance, GF: gauge factor, :strain.

The adaptivity of the robotic material requires percep-

tion based on sensors, interpretation of current and past

load situation (locally or globally or both) and the control

of the actuators. Besides the energy required to perform

computation and communication, the most prominent part

of the energy consumption during operation is caused by

the actuators. The change (increase or decrease) of the

stiffness of the springs commonly requires or releases

energy (e.g., thermoplastic materials require controlled

heating), or requiring energy in both directions, depending

on the actuator technology (e.g., optically excited bi-

stable materials)!

The entire computation and the entire agent processing

implementing the structure optimization discusse d in the

next Section is performed by the material-integrated ICT

network. There is no off-loading of parts of the computa-

tion to external computers.

5 Distributed self-optimization

The concept of self-adaptive robotic materials draws much

of its appeal from the fact that engineering design has to

decide which are the load cases or service conditions to

base design on, while reality typically knows no such

limitations: conventional techniques like shape, topology

(a)

(b) (c)

(d)

Fig. 3 a General network architecture of a robotic material =

sensorial ? actuated material b hardware ? software architecture

c functional decomposition: sensing, acting, processing $ data ?

instruction streams d node connectivity (physical) of controlled

actuators attached to neighbour nodes (shown are the delta vectors

relative to a node position)

Cluster Computing

123

or multi-material optimization all need to single out few or

even one set of boundary conditions to adapt to. Any real

world load-bearing structure may see different scenarios

including misuse, which might be impossible to capture

and deﬁne in the design stage. Such unforeseen loads may

in the worst case lead to immediate failure—in other cases,

they might just wear out a structure, causing it to fail

prematurely and/or in places determined by unexpected

local load histories. A smart adaptive material or structure

that is capable of adjusting its mechanical characteristics—

like stiffness—to external loads could actively manage this

local load history. It could protect areas already worn out

and distribute loads to others instead.

There are three different optimization algorithms that

are applied to materials of this kind and evaluated in this

work:

1. Global optimization;

2. Segmented optimization;

3. Neighbourhood optimization (originally proposed in

[21]),

summarized in Table 1. They are originally based on the

concept of multi-phase topology optimization (MPTO) and

simulated annealing used to optimize structures at design

time by reorganizing material parameter distributions [13].

The algorithms are reactive and iterative, in contrast to

classical mathematical minimization being commonly

functional, applying small changes step-wise to satisfy the

global goal and to minimize an error function. The global

algorithm is presented here for reference only as it requires

a central instance, although the action is performed locally

but based on a global observable. The neighbourhood

algorithm operates continuously without a deﬁned termi-

nation, while the two other algorithms can terminate if they

satisfy a bounded error condition.

The segment and neighbourhood algorithms are suit-

able for distributed processing performing region explo-

ration and negotiation, characterized by self-* features

(self-adaptivity and robustness in case of sensor or node

failures and changes in the network connectivity). That

means, there are multiple instances operating on spatially

bounded data (local data) satisfying global objectives, e.g.,

minimizing the mechanical strain energy of a structure.

One major issue in distributed systems without a central

instance is the efﬁcient collection and computation of

observation variables (observables) like the accumulated

strain energy of a region of nodes. This is discussed in the

next section. The optimization of the structure is performed

by modifying a target variable (control parameter), e.g., the

stiffness of the actuators, based on a comparison of a local

observable (e.g., the local node strain energy) with a global

or glocal (region) observable, e.g., the strain energy of

nodes in a region around the particular node or just a global

accumulated value.

The total strain energy U of a mechanical structure is the

integral of the strain tensor  over the volume V of the

Table 1 Different optimization algorithms (simpliﬁed) for material/

structure adaptation are shown, with N: Set of nodes, S: Set of

segments S, U: total strain energy; : Strain; r: stress; x

: observation

variable of i-th node (element), x: some observation variable; X:

some accumulated observation variable; r

: optimization ratio param-

eter for i-th node, |X|: cardinality of a set X, R: some radius, D: some

discretization function; s

: stiffness of i-th node/element, [s

, s

limits of stiffness, [r

, r

]: limits of optimization parameter, k

: some

problem speciﬁc mapping and scaling function with weight factor w;

|pos

-pos

j=1: neighbourhood relationship of elements or segments

Cluster Computing

123