scholarly journals Teamwork for Multi-Robot Systems in Dynamic Environments

2019 ◽  
Author(s):  
Kurt Geihs
Keyword(s):  
State Of The Art ◽  
Conceptual Modeling ◽  
Dynamic Environments ◽  
Open Research ◽  
Robot Systems ◽  
Multi Agent ◽  
Research Questions ◽  
Technical Solutions ◽  
Future Work ◽  
Multi Robot

The increasing number of robots around us will soon create 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 has been studied extensively during the last two decades, related engineering concerns have not received the same degree of attention. Therefore, this paper makes two contributions. The analysis part discusses general design challenges that apply to robot teamwork in dynamic application domains. The constructive part presents a review of existing engineering approaches for challenges that arise with dynamically changing runtime conditions. An exhaustive survey of robot teamwork aspects would be beyond the scope of this paper. Instead, we aim at creating awareness for the manifold dimensions of the design space and highlight state-of-the-art technical solutions for dynamically adaptive teamwork, thus pointing at open research questions that need to be tackled in future work.

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 Users roles identification on online crowdsourced Q&A platforms and encyclopedias: a survey

2021 ◽  
Author(s):  
Akrati Saxena ◽  
Harita Reddy
Keyword(s):  
Network Analysis ◽  
Knowledge Sharing ◽  
Informal Learning ◽  
Social Roles ◽  
Online Discussion ◽  
State Of The Art ◽  
Temporal Analysis ◽  
The State ◽  
Open Research ◽  
Research Questions

AbstractOnline informal learning and knowledge-sharing platforms, such as Stack Exchange, Reddit, and Wikipedia have been a great source of learning. Millions of people access these websites to ask questions, answer the questions, view answers, or check facts. However, one interesting question that has always attracted the researchers is if all the users share equally on these portals, and if not then how the contribution varies across users, and how it is distributed? Do different users focus on different kinds of activities and play specific roles? In this work, we present a survey of users’ social roles that have been identified on online discussion and Q&A platforms including Usenet newsgroups, Reddit, Stack Exchange, and MOOC forums, as well as on crowdsourced encyclopedias, such as Wikipedia, and Baidu Baike, where users interact with each other through talk pages. We discuss the state of the art on capturing the variety of users roles through different methods including the construction of user network, analysis of content posted by users, temporal analysis of user activity, posting frequency, and so on. We also discuss the available datasets and APIs to collect the data from these platforms for further research. The survey is concluded with open research questions.

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.

A Novel Approach With Bayesian Networks to Multi-Robot Task Allocation in Dynamic Environments

2021 ◽  
Author(s):  
Ching-Wei Chuang ◽  
Harry H. Cheng
Keyword(s):  
Bayesian Networks ◽  
Task Allocation ◽  
Low Cost ◽  
Dynamic Environments ◽  
Modern World ◽  
Robot System ◽  
Novel Approach ◽  
Robot Systems ◽  
Robot Task ◽  
Multi Robot

Abstract In the modern world, building an autonomous multi-robot system is essential to coordinate and control robots to help humans because using several low-cost robots becomes more robust and efficient than using one expensive, powerful robot to execute tasks to achieve the overall goal of a mission. One research area, multi-robot task allocation (MRTA), becomes substantial in a multi-robot system. Assigning suitable tasks to suitable robots is crucial in coordination, which may directly influence the result of a mission. In the past few decades, although numerous researchers have addressed various algorithms or approaches to solve MRTA problems in different multi-robot systems, it is still difficult to overcome certain challenges, such as dynamic environments, changeable task information, miscellaneous robot abilities, the dynamic condition of a robot, or uncertainties from sensors or actuators. In this paper, we propose a novel approach to handle MRTA problems with Bayesian Networks (BNs) under these challenging circumstances. Our experiments exhibit that the proposed approach may effectively solve real problems in a search-and-rescue mission in centralized, decentralized, and distributed multi-robot systems with real, low-cost robots in dynamic environments. In the future, we will demonstrate that our approach is trainable and can be utilized in a large-scale, complicated environment. Researchers might be able to apply our approach to other applications to explore its extensibility.

Review on state-of-the-art dynamic task allocation strategies for multiple-robot systems

2020 ◽  
Vol 47 (6) ◽  
pp. 929-942
Author(s):  
Seenu N. ◽  
Kuppan Chetty R.M. ◽  
Ramya M.M. ◽  
Mukund Nilakantan Janardhanan
Keyword(s):  
Task Allocation ◽  
State Of The Art ◽  
Objective Functions ◽  
Research Directions ◽  
Content Type ◽  
Dynamic Task ◽  
Dynamic Task Allocation ◽  
Robot Systems ◽  
Allocation Strategies ◽  
Multi Robot

Purpose This paper aims to present a concise review on the variant state-of-the-art dynamic task allocation strategies. It presents a thorough discussion about the existing dynamic task allocation strategies mainly with respect to the problem application, constraints, objective functions and uncertainty handling methods. Design/methodology/approach This paper briefs the introduction of multi-robot dynamic task allocation problem and discloses the challenges that exist in real-world dynamic task allocation problems. Numerous task allocation strategies are discussed in this paper, and it establishes the characteristics features between them in a qualitative manner. This paper also exhibits the existing research gaps and conducive future research directions in dynamic task allocation for multiple mobile robot systems. Findings This paper concerns the objective functions, robustness, task allocation time, completion time, and task reallocation feature for performance analysis of different task allocation strategies. It prescribes suitable real-world applications for variant task allocation strategies and identifies the challenges to be resolved in multi-robot task allocation strategies. Originality/value This paper provides a comprehensive review of dynamic task allocation strategies and incites the salient research directions to the researchers in multi-robot dynamic task allocation problems. This paper aims to summarize the latest approaches in the application of exploration problems.

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.

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