72,770 research outputs found
Multi-agent Time-based Decision-making for the Search and Action Problem
Many robotic applications, such as search-and-rescue, require multiple agents
to search for and perform actions on targets. However, such missions present
several challenges, including cooperative exploration, task selection and
allocation, time limitations, and computational complexity. To address this, we
propose a decentralized multi-agent decision-making framework for the search
and action problem with time constraints. The main idea is to treat time as an
allocated budget in a setting where each agent action incurs a time cost and
yields a certain reward. Our approach leverages probabilistic reasoning to make
near-optimal decisions leading to maximized reward. We evaluate our method in
the search, pick, and place scenario of the Mohamed Bin Zayed International
Robotics Challenge (MBZIRC), by using a probability density map and reward
prediction function to assess actions. Extensive simulations show that our
algorithm outperforms benchmark strategies, and we demonstrate system
integration in a Gazebo-based environment, validating the framework's readiness
for field application.Comment: 8 pages, 7 figures, submission to 2017 International Conference on
Robotics & Automatio
Exploiting Heterogeneous Robotic Systems in Cooperative Missions
In this paper we consider the problem of coordinating robotic systems with
different kinematics, sensing and vision capabilities to achieve certain
mission goals. An approach that makes use of a heterogeneous team of agents has
several advantages when cost, integration of capabilities, or large search
areas need to be considered. A heterogeneous team allows for the robots to
become "specialized", accomplish sub-goals more effectively, and thus increase
the overall mission efficiency. Two main scenarios are considered in this work.
In the first case study we exploit mobility to implement a power control
algorithm that increases the Signal to Interference plus Noise Ratio (SINR)
among certain members of the network. We create realistic sensing fields and
manipulation by using the geometric properties of the sensor field-of-view and
the manipulability metric, respectively. The control strategy for each agent of
the heterogeneous system is governed by an artificial physics law that
considers the different kinematics of the agents and the environment, in a
decentralized fashion. Through simulation results we show that the network is
able to stay connected at all times and covers the environment well. The second
scenario studied in this paper is the biologically-inspired coordination of
heterogeneous physical robotic systems. A team of ground rovers, designed to
emulate desert seed-harvester ants, explore an experimental area using
behaviors fine-tuned in simulation by a genetic algorithm. Our robots
coordinate with a base station and collect clusters of resources scattered
within the experimental space. We demonstrate experimentally that through
coordination with an aerial vehicle, our ant-like ground robots are able to
collect resources two times faster than without the use of heterogeneous
coordination
A Formal Framework for Mobile Robot Patrolling in Arbitrary Environments with Adversaries
Using mobile robots for autonomous patrolling of environments to prevent
intrusions is a topic of increasing practical relevance. One of the most
challenging scientific issues is the problem of finding effective patrolling
strategies that, at each time point, determine the next moves of the patrollers
in order to maximize some objective function. In the very last years this
problem has been addressed in a game theoretical fashion, explicitly
considering the presence of an adversarial intruder. The general idea is that
of modeling a patrolling situation as a game, played by the patrollers and the
intruder, and of studying the equilibria of this game to derive effective
patrolling strategies. In this paper we present a game theoretical formal
framework for the determination of effective patrolling strategies that extends
the previous proposals appeared in the literature, by considering environments
with arbitrary topology and arbitrary preferences for the agents. The main
original contributions of this paper are the formulation of the patrolling game
for generic graph environments, an algorithm for finding a deterministic
equilibrium strategy, which is a fixed path through the vertices of the graph,
and an algorithm for finding a non-deterministic equilibrium strategy, which is
a set of probabilities for moving between adjacent vertices of the graph. Both
the algorithms are analytically studied and experimentally validated, to assess
their properties and efficiency
Learning of Coordination Policies for Robotic Swarms
Inspired by biological swarms, robotic swarms are envisioned to solve
real-world problems that are difficult for individual agents. Biological swarms
can achieve collective intelligence based on local interactions and simple
rules; however, designing effective distributed policies for large-scale
robotic swarms to achieve a global objective can be challenging. Although it is
often possible to design an optimal centralized strategy for smaller numbers of
agents, those methods can fail as the number of agents increases. Motivated by
the growing success of machine learning, we develop a deep learning approach
that learns distributed coordination policies from centralized policies. In
contrast to traditional distributed control approaches, which are usually based
on human-designed policies for relatively simple tasks, this learning-based
approach can be adapted to more difficult tasks. We demonstrate the efficacy of
our proposed approach on two different tasks, the well-known rendezvous problem
and a more difficult particle assignment problem. For the latter, no known
distributed policy exists. From extensive simulations, it is shown that the
performance of the learned coordination policies is comparable to the
centralized policies, surpassing state-of-the-art distributed policies.
Thereby, our proposed approach provides a promising alternative for real-world
coordination problems that would be otherwise computationally expensive to
solve or intangible to explore.Comment: 8 pages, 11 figures, submitted to 2018 IEEE International Conference
on Robotics and Automatio
Area Protection in Adversarial Path-Finding Scenarios with Multiple Mobile Agents on Graphs: a theoretical and experimental study of target-allocation strategies for defense coordination
We address a problem of area protection in graph-based scenarios with
multiple agents. The problem consists of two adversarial teams of agents that
move in an undirected graph shared by both teams. Agents are placed in vertices
of the graph; at most one agent can occupy a vertex; and they can move into
adjacent vertices in a conflict free way. Teams have asymmetric goals: the aim
of one team - attackers - is to invade into given area while the aim of the
opponent team - defenders - is to protect the area from being entered by
attackers by occupying selected vertices. We study strategies for allocating
vertices to be occupied by the team of defenders to block attacking agents. We
show that the decision version of the problem of area protection is PSPACE-hard
under the assumption that agents can allocate their target vertices multiple
times. Further we develop various on-line vertex-allocation strategies for the
defender team in a simplified variant of the problem with single stage vertex
allocation and evaluated their performance in multiple benchmarks. The success
of a strategy is heavily dependent on the type of the instance, and so one of
the contributions of this work is that we identify suitable vertex-allocation
strategies for diverse instance types. In particular, we introduce a
simulation-based method that identifies and tries to capture bottlenecks in the
graph, that are frequently used by the attackers. Our experimental evaluation
suggests that this method often allows a successful defense even in instances
where the attackers significantly outnumber the defenders
Decentralized Ergodic Control: Distribution-Driven Sensing and Exploration for Multi-Agent Systems
We present a decentralized ergodic control policy for time-varying area
coverage problems for multiple agents with nonlinear dynamics. Ergodic control
allows us to specify distributions as objectives for area coverage problems for
nonlinear robotic systems as a closed-form controller. We derive a variation to
the ergodic control policy that can be used with consensus to enable a fully
decentralized multi-agent control policy. Examples are presented to illustrate
the applicability of our method for multi-agent terrain mapping as well as
target localization. An analysis on ergodic policies as a Nash equilibrium is
provided for game theoretic applications.Comment: 8 pages, Accepted for publication in IEEE Robotics and Automation
Letter
Some comparisons between the Variational rationality, Habitual domain, and DMCS approaches
The "Habitual domain" (HD) approach and the "Variational rationality" (VR)
approach belong to the same strongly interdisciplinary and very dispersed area
of research: human stability and change dynamics (see Soubeyran, 2009, 2010,
for an extended survey), including physiological, physical, psychological and
strategic aspects, in Psychology, Economics, Management Sciences, Decision
theory, Game theory, Sociology, Philosophy, Artificial Intelligence,.... These
two approaches are complementary. They have strong similarities and strong
differences. They focus attention on both similar and different stay and change
problems, using different concepts and different mathematical tools. When they
use similar concepts (a lot), they often have different meaning. We can compare
them with respect to the problems and topics they consider, the behavioral
principles they use, the concepts they modelize, the mathematical tools they
use, and their results.Comment: 31 page
Distributed Cohesive Control for Robot Swarms: Maintaining Good Connectivity in the Presence of Exterior Forces
We present a number of powerful local mechanisms for maintaining a dynamic
swarm of robots with limited capabilities and information, in the presence of
external forces and permanent node failures. We propose a set of local
continuous algorithms that together produce a generalization of a Euclidean
Steiner tree. At any stage, the resulting overall shape achieves a good
compromise between local thickness, global connectivity, and flexibility to
further continuous motion of the terminals. The resulting swarm behavior scales
well, is robust against node failures, and performs close to the best known
approximation bound for a corresponding centralized static optimization
problem
The Automated Mapping of Plans for Plan Recognition
To coordinate with other agents in its environment, an agent needs models of
what the other agents are trying to do. When communication is impossible or
expensive, this information must be acquired indirectly via plan recognition.
Typical approaches to plan recognition start with a specification of the
possible plans the other agents may be following, and develop special
techniques for discriminating among the possibilities. Perhaps more desirable
would be a uniform procedure for mapping plans to general structures supporting
inference based on uncertain and incomplete observations. In this paper, we
describe a set of methods for converting plans represented in a flexible
procedural language to observation models represented as probabilistic belief
networks.Comment: Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994
A Framework for learning multi-agent dynamic formation strategy in real-time applications
Formation strategy is one of the most important parts of many multi-agent
systems with many applications in real world problems. In this paper, a
framework for learning this task in a limited domain (restricted environment)
is proposed. In this framework, agents learn either directly by observing an
expert behavior or indirectly by observing other agents or objects behavior.
First, a group of algorithms for learning formation strategy based on limited
features will be presented. Due to distributed and complex nature of many
multi-agent systems, it is impossible to include all features directly in the
learning process; thus, a modular scheme is proposed in order to reduce the
number of features. In this method, some important features have indirect
influence in learning instead of directly involving them as input features.
This framework has the ability to dynamically assign a group of positions to a
group of agents to improve system performance. In addition, it can change the
formation strategy when the context changes. Finally, this framework is able to
automatically produce many complex and flexible formation strategy algorithms
without directly involving an expert to present and implement such complex
algorithms.Comment: 27 pages, 9 figure
- …