Balch and Parker
- Title TBD (Subject: Taxonomy of Multirobot Systems)
- Multiagent Systems: A Survey from a machine learning perspective
Stone and Veloso
Distributed Artificial Intelligence (DAI) has existed as a subfield of
AI for less than two decades. DAI is concerned with systems that
consist of multiple independent entities that interact in a domain.
Traditionally, DAI has been divided into two sub-disciplines:
Distributed Problem Solving (DPS) focuses on the information
management aspects of systems with several components working together
towards a common goal; Multiagent Systems (MAS) deals with behavior
management in collections of several independent entities, or agents.
This survey of MAS is intended to serve as an introduction to the
field and as an organizational framework. A series of general
multiagent scenarios are presented. For each scenario, the issues
that arise are described along with a sampling of the techniques that
exist to deal with them. The presented techniques are not exhaustive,
but they highlight how multiagent systems can be and have been used to
build complex systems. When options exist, the techniques presented
are biased towards machine learning approaches. Additional
opportunities for applying machine learning to MAS are highlighted and
robotic soccer is presented as an appropriate test bed for MAS. This
survey does not focus exclusively on robotic systems. However, we
believe that much of the prior research in non-robotic MAS is relevant
to robotic MAS, and we explicitly discuss several robotic MAS,
including many of those presented in this book.
- Social Entropy: An Information Theoretic Measure of Robot
As research expands in multiagent intelligent systems,
investigators need new tools for evaluating
the artificial societies they study.
It is impossible, for example, to correlate heterogeneity with
performance in multiagent robotics without a
quantitative metric of diversity.
Currently diversity is evaluated on
a bipolar scale with systems classified as either heterogeneous
or homogeneous, depending on whether any of the agents differ.
this labeling doesn't tell us much about the extent of
diversity in heterogeneous teams. How can it be determined
if one system is more or less diverse than another?
Heterogeneity must be evaluated on a continuous scale to enable
substantive comparisons between systems.
To enable these types of comparisons, we introduce:
(1) a continuous measure of robot behavioral difference, and
(2) hierarchic social entropy, an application of Shannon's
information entropy metric to robotic
groups that provides a continuous, quantitative
measure of robot team diversity.
The metric captures
important components of the meaning of diversity, including
the number and size of behavioral groups in a society and
the extent to which agents differ.
The utility of the metrics is
demonstrated in the experimental evaluation of
multirobot soccer and multirobot foraging teams.
- CONRO: Design and Implementation of a Self-Contained Module
for Reconfigurable Robots
Castano, Shen and Will
This chapter discusses a new philosophy of design for reconfigurable
robots and some of its results. In contrast to the focus of similar
projects, our goal is to build robots that can be deployed and that
can support inter-robot metamorphing, i.e.,
robots that have not only the capability of changing their shape
but also can split into smaller robots or merge with other robots
to create a single larger robot (inter-robot metamorphing).
The result is a system that can work either as a single robot
with an arbitrary shape or as a team of possibly heterogeneous robots,
each one with an arbitrary shape.
On its turn, each single robot is composed by a team of modules
that have been assigned heterogeneous tasks, e.g., some play the role
of legs, others that of spine or head.
There are hardware and software problems associated with these goals.
The hardware problem is that of how to build a module that can support
stated goals. The software problem is that of how to write the
can drive the hardware.
In this chapter we discuss our solution to the hardware problem:
the philosophy of design of a totally miniature self-sufficient and
autonomous robotic module that can be used as the building block
of the robots and its implementation in the form of actual
modules that can be assembled together to form deployable snakes,
hexapods and other robots.
- Heterogeneous Teams of Modular Robots for Mapping and Exploration
Grabowski, Navarro-Serment, Paredis and Khosla
In this chapter, we present the design of a team of heterogeneous,
centimeter-scale robots that collaborate to map and explore unknown
environments. The robots, called Millibots, are configured from
modular components that include sonar and IR sensors, camera,
communication, computation, and mobility modules. Robots with
different configurations use their special capabilities collaboratively
to accomplish a given task. For mapping and exploration with multiple
robots, it is critical to know the relative positions of each robot
with respect to the others. We have developed a novel localization
system that uses sonar-based distance measurements to determine the
positions of all the robots in the group. With their positions known,
we use an occupancy grid Bayesian mapping algorithm to combine the
sensor data from multiple robots with different sensing modalities.
Finally, we present the results of several mapping experiments
conducted by a user-guided team of five robots operating in a room containing
- A Probabilistic Approach to Collaborative Multi-Robot Localization
Fox, Burgard, Kruppa and Thrun
This chapter covers statistical algorithms for collaborative
mobile robot localization. Our approach uses a sample-based version
of Markov localization, capable of localizing mobile robots in an
any-time fashion. When teams of robots localize themselves in the
same environment, probabilistic methods are employed to synchronize
each robot's belief whenever one robot detects another. As a result,
the robots localize themselves faster, maintain higher accuracy, and
high-cost sensors are amortized across multiple robot platforms. The
technique has been implemented and tested using two mobile robots
equipped with cameras and laser range-finders for detecting other
robots. The results, obtained with the real robots and in series of
simulation runs, illustrate drastic improvements in localization
speed and accuracy when compared to conventional single-robot
localization. A further experiment demonstrates that under certain
conditions, successful localization is only possible if teams of
heterogeneous robots collaborate during localization.
- Life-long Adaptation in Heterogeneous Multi-robot teams:
Response to Continual Variation in Individual Robot Performance
Generating teams of robots that are able to perform their tasks over
long periods of time requires the robots to be responsive to
continual changes in robot team member capabilities and to changes in
the state of the environment and mission. This chapter describes the
L-ALLIANCE architecture, which enables teams of heterogeneous robots
to dynamically adapt their actions over time. This architecture,
which is an extension of our earlier work on ALLIANCE, is a
distributed, behavior-based architecture aimed for use in
applications consisting of a collection of independent tasks. The
key issue addressed in L-ALLIANCE is the determination of which tasks
robots should select to perform during their mission, even when
multiple robots with heterogeneous, continually changing capabilities
are present on the team. In this approach, robots monitor the
performance of their teammates performing common tasks, and evaluate
their performance based upon the time of task completion. Robots
then use this information throughout the lifetime of their mission to
automatically update their control parameters. After describing the
L-ALLIANCE architecture, we discuss the results of implementing this
approach on a physical team of heterogeneous robots performing
proof-of-concept box pushing experiments. The results illustrate the
ability of L-ALLIANCE to enable lifelong adaptation of heterogeneous
robot teams to continuing changes in the robot team member
capabilities and in the environment.
- Grounded Symbolic Communication between Heterogeneous
Jung and Zelinsky
In this chapter, we describe the implementation of a heterogeneous
cooperative multi-robot system that was designed with a goal of engineering
a grounded symbolic representation in a bottom-up fashion. The system
comprises two autonomous mobile robots that perform cooperative
cleaning. Experiments demonstrate successful purposive navigation, map
building and the symbolic communication of locations in a behavior-based
system. We also examine the perceived shortcomings of the system in detail
and attempt to understand them in terms of contemporary knowledge of human
representation and symbolic communication. From this understanding, we
propose the Adaptive Symbol Grounding Hypothesis as a conception for how
symbolic systems can be envisioned.
- Title TBD (Subject: Marsupial Robots)
- Robust Behavior-Based Control for Distributed
Multi-Robot Collection Tasks
Dani Goldberg and Maja Mataric
We report on the successful application of behavior-based control to
distributed multi-robot collection, a class of tasks that includes
de-mining and toxic waste clean-up. The emphasis in this work is on
designing controllers that are both robust to robot failures and
easily modified to facilitate identification of the most desirable
controller variation. We demonstrate a basic, homogeneous multi-robot
controller for the collection task, then show how to easily derive two
heterogeneous, spatio-temporal variations with markedly different
performance properties. We evaluate the desirability of these
controllers in a spatio-temporal context using inter-robot
interference and time-to-completion as the main diagnostic parameters.
The data for evaluation come from experiments using four physical
mobile robots performing the three variations of the collection task.
- Title TBD (Subject: Cooperative Localization between Aerial
and Ground Vehicles)
- Title TBD (Subject: Heterogeneous Construction Robots)
Reid Simmons and David Hershberger
Important announcements or changes will be posted on
the webpage: http://www.cs.cmu.edu/~trb/heterobook