Autonomous Role Assignment in Homogeneous Multi-Robot Systems(Multi-agent and Learning,Session: TP2-A)

2004 ◽  
Vol 2004.4 (0) ◽  
pp. 51
Author(s):  
Toshiyuki YASUDA ◽  
Kazuhiro OHKURA ◽  
Toshiharu TAURA
Keyword(s):  
Learning Session ◽  
Role Assignment ◽  
Robot Systems ◽  
Multi Agent ◽  
Multi Robot

scholarly journals Modelling and verification of reconfigurable multi-agent systems

2021 ◽  
Vol 35 (2) ◽  
Author(s):  
Yehia Abd Alrahman ◽  
Nir Piterman
Keyword(s):  
Model Checking ◽  
Communication Protocols ◽  
Local State ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Robot Systems ◽  
Message Exchange ◽  
Multi Agent ◽  
Exchange Data ◽  
Multi Robot

AbstractWe propose a formalism to model and reason about reconfigurable multi-agent systems. In our formalism, agents interact and communicate in different modes so that they can pursue joint tasks; agents may dynamically synchronize, exchange data, adapt their behaviour, and reconfigure their communication interfaces. Inspired by existing multi-robot systems, we represent a system as a set of agents (each with local state), executing independently and only influence each other by means of message exchange. Agents are able to sense their local states and partially their surroundings. We extend ltl to be able to reason explicitly about the intentions of agents in the interaction and their communication protocols. We also study the complexity of satisfiability and model-checking of this extension.

scholarly journals Automated Verification of Social Laws for Continuous Time Multi-Robot Systems

2019 ◽  
Vol 33 ◽  
pp. 7683-7690 ◽  
Author(s):  
Ronen Nir ◽  
Erez Karpas
Keyword(s):  
Continuous Time ◽  
Automated Verification ◽  
Shared Environment ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Temporal Planning ◽  
Social Law ◽  
Robot Systems ◽  
Multi Agent ◽  
Multi Robot

Designing multi-agent systems, where several agents work in a shared environment, requires coordinating between the agents so they do not interfere with each other. One of the canonical approaches to coordinating agents is enacting a social law, which applies restrictions on agents’ available actions. A good social law prevents the agents from interfering with each other, while still allowing all of them to achieve their goals. Recent work took the first step towards reasoning about social laws using automated planning and showed how to verify if a given social law is robust, that is, allows all agents to achieve their goals regardless of what the other agents do. This work relied on a classical planning formalism, which assumed actions are instantaneous and some external scheduler chooses which agent acts next. However, this work is not directly applicable to multi-robot systems, because in the real world actions take time and the agents can act concurrently. In this paper, we show how the robustness of a social law in a continuous time setting can be verified through compilation to temporal planning. We demonstrate our work both theoretically and on real robots.

Optimal distributed interconnectivity of multi-robot systems by spatially-constrained clustering

2017 ◽  
Vol 25 (2) ◽  
pp. 96-113 ◽  
Author(s):  
Matin Macktoobian ◽  
Mahdi Aliyari Sh
Keyword(s):  
Clustering Algorithm ◽  
Task Assignment ◽  
Constrained Clustering ◽  
Loosely Coupled ◽  
Robot Systems ◽  
Multi Agent ◽  
Probabilistic Proof ◽  
Data Passing ◽  
Spatially Constrained Clustering ◽  
Multi Robot

A spatially-constrained clustering algorithm is presented in this paper. This algorithm is a distributed clustering approach to fine-tune the optimal distances between agents of the system to strengthen the data passing among them using a set of spatial constraints. In fact, this method will increase interconnectivity among agents and clusters, leading to improvement of the overall communicative functionality of the multi-robot system. This strategy will lead to the establishment of loosely-coupled connections among the clusters. These implicit interconnections will mobilize the clusters to receive and transmit information within the multi-agent system. In other words, this algorithm classifies each agent into the clusters with the lowest cost of local communication with its peers. This research demonstrates that the presented decentralized method will actually boost the communicative agility of the swarm by probabilistic proof of the acquired optimality. Hence, the common assumption regarding the full-knowledge of the agents’ primary locations has been fully relaxed compared to former methods. Consequently, the algorithm’s reliability and efficiency is confirmed. Furthermore, the method’s efficacy in passing information will improve the functionality of higher-level swarm operations, such as task assignment and swarm flocking. Analytical investigations and simulated accomplishments, corresponding to highly-populated swarms, prove the claimed efficiency and coherence.

scholarly journals Engineering Challenges Ahead for Robot Teamwork in Dynamic Environments

2020 ◽  
Vol 10 (4) ◽  
pp. 1368
Author(s):  
Kurt Geihs
Keyword(s):  
Autonomous Robots ◽  
Conceptual Modeling ◽  
Dynamic Environments ◽  
Systems Perspective ◽  
Robot Systems ◽  
Multi Agent ◽  
Review State ◽  
Future Work ◽  
Theoretical Foundations ◽  
Multi Robot

The increasing number of robots around us creates a demand for connecting these robots in order to achieve goal-driven teamwork in heterogeneous multi-robot systems. In this paper, we focus on robot teamwork specifically in dynamic environments. While the conceptual modeling of multi-agent teamwork was studied extensively during the last two decades and commercial multi-agent applications were built based on the theoretical foundations, the steadily increasing use of autonomous robots in many application domains gave the topic new significance and shifted the focus more toward engineering concerns for multi-robot systems. From a distributed systems perspective, we discuss general engineering challenges that apply to robot teamwork in dynamic application domains and review state-of-the-art solution approaches for these challenges. This leads us to open research questions that need to be tackled in future work.

scholarly journals Decision-Making Under Uncertainty in Multi-Agent and Multi-Robot Systems: Planning and Learning

2018 ◽  
Author(s):  
Christopher Amato
Keyword(s):  
Decision Making Under Uncertainty ◽  
High Quality ◽  
Learning Methods ◽  
Robust Solutions ◽  
Systems Planning ◽  
Robot Systems ◽  
Multi Agent ◽  
Robot Tasks ◽  
Multi Agent Planning ◽  
Multi Robot

Multi-agent planning and learning methods are becoming increasingly important in today's interconnected world. Methods for real-world domains, such as robotics, must consider uncertainty and limited communication in order to generate high-quality, robust solutions. This paper discusses our work on developing principled models to represent these problems and planning and learning methods that can scale to realistic multi-agent and multi-robot tasks.

scholarly journals Multi-Robot Planning Under Uncertain Travel Times and Safety Constraints

2019 ◽  
Author(s):  
Masoumeh Mansouri ◽  
Bruno Lacerda ◽  
Nick Hawes ◽  
Federico Pecora
Keyword(s):  
Team Performance ◽  
Current Practice ◽  
Stochastic Petri Nets ◽  
Travel Times ◽  
Planning Approach ◽  
Robot Systems ◽  
Markov Decision ◽  
Safety Constraints ◽  
Multi Agent ◽  
Multi Robot

We present a novel modelling and planning approach for multi-robot systems under uncertain travel times. The approach uses generalised stochastic Petri nets (GSPNs) to model desired team behaviour, and allows to specify safety constraints and rewards. The GSPN is interpreted as a Markov decision process (MDP) for which we can generate policies that optimise the requirements. This representation is more compact than the equivalent multi-agent MDP, allowing us to scale better. Furthermore, it naturally allows for asynchronous execution of the generated policies across the robots, yielding smoother team behaviour. We also describe how the integration of the GSPN with a lower-level team controller allows for accurate expectations on team performance. We evaluate our approach on an industrial scenario, showing that it outperforms hand-crafted policies used in current practice.

Adaptive Division-of-Labor Control Algorithm for Multi-Robot Systems

2010 ◽  
Vol 22 (4) ◽  
pp. 514-525 ◽  
Author(s):  
Yusuke Ikemoto ◽  
◽  
Toru Miura ◽  
Hajime Asama ◽  
◽  
...  
Keyword(s):  
Division Of Labor ◽  
Performance Improvement ◽  
Group Performance ◽  
Homogeneous State ◽  
Adaptive Selection ◽  
Labor Control ◽  
Robot Systems ◽  
Multi Agent ◽  
Selection Of ◽  
Multi Robot

An advanced function for multi-robot systems is the division of labor. There are some studies proposing a multi-agent reinforcement learning method for a division of labor. However, it often requires much time to converge. Many studies focusing on division-of-labor control inspired biological phenomenon have been reported. In those methods, whether heterogeneous or homogeneous state is determined by self-organization, however, group performance improvement is not guaranteed because decentralized control is typically complicated. In this study, we propose adaptive division-of-labor control, enabling adaptive selection of homogeneous or heterogeneous group state. We demonstrate the adaptability of proposal method versus working conditions and address the performance improvement by mathematical analysis. To evaluate the effectiveness of the proposed method, we treat foraging by multi-robot systems and confirm that the robot group inevitably organizes the division of labor with group performance improvement in computer simulations.

Sign in / Sign up

Export Citation Format

Share Document