Multi-Agent Robotic Systems Research Papers

A Modified Particle Swarm Optimization on Search Tasking

2025, Research Journal of Applied Sciences, Engineering and Technology

Recently, more and more researches have been conducted on the multi-robot system by applying bioinspired algorithms. Particle Swarm Optimization (PSO) is one of the optimization algorithms that model a set of solutions as a swarm of... more

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Optimizing Parallel Task Execution for Multi-Agent Mission Planning

by Baran Curuklu

2024, IEEE Access

Multi-agent systems have received a tremendous amount of attention in many areas of research and industry, especially in robotics and computer science. With the increased number of agents in missions, the problem of allocation of tasks to... more

Multi-agent systems have received a tremendous amount of attention in many areas of research and industry, especially in robotics and computer science. With the increased number of agents in missions, the problem of allocation of tasks to agents arose, and it is one of the most fundamental classes of problems in robotics, formally known as the Multi-Robot Task Allocation (MRTA) problem. MRTA encapsulates numerous problem dimensions, and it aims at providing formulations and solutions to various problem configurations, i.e., complex multi-agent missions. One dimension of the MRTA problem has not caught much of the research attention. In particular, problem configurations including Multi-Task (MT) robots have been neglected. However, the increase in computational power, in robotic systems, has allowed the utilization of parallel task execution. This in turn had the benefit of allowing the creation of more complex robotic missions; however, it came at the cost of increased problem complexity. Our contribution to the aforementioned domain can be grouped into three categories. First, we model the problem using two different approaches, Integer Linear Programming and Constraint Programming. With these models, we aim at filling the gap in the literature related to the formal definition of MT robot problem configuration. Second, we introduce the distinction between physical and virtual tasks and their mutual relationship in terms of parallel task execution. This distinction allows the modeling of a wider range of missions while exploiting possible parallel task execution. Finally, we provide a comprehensive performance analysis of both models, by implementing and validating them in CPLEX and CP Optimizer on the set of problems. Each problem consists of the same set of test instances gradually increasing in complexity, while the percentage of virtual tasks in each problem is different. The analysis of the results includes exploration of the scalability of both models and solvers, the effect of virtual tasks on the solvers' performance, and overall solution quality. INDEX TERMS Multi-agent mission planning, multi-robot task allocation, parallel task execution, integer linear programming, constraint programming.

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Democratic Inspired Particle Swarm Optimization for Multi-Robot Exploration Task

by Hachaichi Yassine

2024

In this paper, we propose a new method for exploring an unknown environment with a team of homogeneous mobile robots. The goal of our approach is to minimize the exploration time. The challenge in multi-robot exploration is how to develop... more

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Applying Area Extension PSO in Robotic Swarm

by Chin-Kuan Ho

2024, Journal of Intelligent and Robotic Systems

Particle Swarm optimization (PSO) is a search method inspired from the social behaviors of animals. PSO has been found to outperform other methods in various tasks. Area Extended PSO (AEPSO) is an enhanced version of PSO that achieves... more

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Multiagent Task Allocation in Complementary Teams: A Hunter-and-Gatherer Approach

by Harley R Myler

2024, Complexity

Consider a dynamic task allocation problem, where tasks are unknowingly distributed over an environment. is paper considers each task comprising two sequential subtasks: detection and completion, where each subtask can only be carried out... more

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Impact of initialization of a modified particle swarm optimization on cooperative source searching

by International Journal of Electrical and Computer Engineering (IJECE) and

2024, International Journal of Electrical and Computer Engineering (IJECE)

Swarm robotic is well known for its flexibility, scalability and robustness that make it suitable for solving many real-world problems. Source searching which is characterized by complex operation due to the spatial characteristic of the... more

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Drone swarm technology as a competitive alternative to traditional aerial firefighting

by Agoston Restas

2023, Imprensa da Universidade de Coimbra eBooks

Aerial firefighting is effective however very expensive solution to suppress forest fires. Drone application as a most developing branch of the aviation industry can be a complement, or perhaps even a competitive solution with the... more

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From Pillars to AI Technology-Based Forest Fire Protection Systems

by Παύλος Κωνσταντινίδης

2023, Artificial Intelligence - Applications in Agriculture and Bio-system Engineering [Working Title]

The importance of forest environment in the perspective of the biodiversity as well as from the economic resources which forests enclose, is more than evident. Any threat posed to this critical component of the environment should be... more

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A Modified Particle Swarm Optimization on Search Tasking

by Mohammad Ahmadi

2023, Research Journal of Applied Sciences, Engineering and Technology

Recently, more and more researches have been conducted on the multi-robot system by applying bioinspired algorithms. Particle Swarm Optimization (PSO) is one of the optimization algorithms that model a set of solutions as a swarm of... more

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Multi-robot assignment algorithm for tasks with set precedence constraints

by Katia Sycara

2023, 2011 IEEE International Conference on Robotics and Automation

In this paper, we present task allocation (assignment) algorithms for a multi-robot system where the tasks are divided into disjoint groups and there are precedence constraints between the task groups. Existing auction-based algorithms... more

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A new multiple robot path planning algorithm: dynamic distributed particle swarm optimization

by asma ayari

2023, Robotics and Biomimetics

Multiple robot systems have become a major study concern in the field of robotic research. Their control becomes unreliable and even infeasible if the number of robots increases. In this paper, a new dynamic distributed particle swarm... more

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RbRDPSO: Repulsion-Based RDPSO for Robotic Target Searching

by ali hamzeh

2023, Iranian Journal of Science and Technology, Transactions of Electrical Engineering

Search application has been one of the many domains wherein multi-robot systems (MRS) have been applied over the past few years. Such applications include, but are not limited to, environmental monitoring, battlefield surveillance, search... more

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A PSO-based multi-robot cooperation method for target searching in unknown environments

by ali hamzeh

2023, Neurocomputing

In this paper, we study the problem of multi-robot target searching in unknown environments. For target searching, robots need an efficient method with respect to their limitations and characteristics of the workspace. Every robotic... more

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Fire-fighting UAV with shooting mechanism of fire extinguishing ball for smart city

by Nastaran Rezanazarzadeh

2022, Indonesian Journal of Electrical Engineering and Computer Science

With the growth of technology and massive city development, firefighting services have become more challenging to cope with a smart-city concept. One of the challenges that firefighters are facing is reaching the top floors of high-raised... more

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Particle Swarm Optimization—An Adaptation for the Control of Robotic Swarms

by George Rossides

2022, Robotics

Particle Swarm Optimization (PSO) is a numerical optimization technique based on the motion of virtual particles within a multidimensional space. The particles explore the space in an attempt to find minima or maxima to the optimization... more

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Fire-fighting UAV with shooting mechanism of fire extinguishing ball for smart city

by Indonesian Journal of Electrical Engineering and Computer Science

2022, Indonesian Journal of Electrical Engineering and Computer Science

With the growth of technology and massive city development, firefighting services have become more challenging to cope with a smart-city concept. One of the challenges that firefighters are facing is reaching the top floors of high-raised... more

Figure 1. Conceptual framework A GPS sensor is used to stabilize the UAV. The UAV works based on the available GPS satellite deployed to orbit the earth 20,180 km above (called MEO) [18], [19]. The copter's navigation was often hampered in the waypoint navigation due to the GPS location's erroneous information. This concept is called a GPS glitch [20]-[23]. The GPS glitch's primary cause was the unavailability of the GPS signals, which results in insufficient GPS data. If the glitch is very long, then the location value gets rejected subsequently [24]. See Figure 2 for the 3-D design of the UAV’s prototype.

Figure 2. 3-D design of the UAV: (a) side view of the UAV and (b) bottom view 2.2. Shooting and dropping mechanism anf For the shooting and dropping mechanism, the NRF24L01 Transceiver is used to send and receive activation signals [25]. By clicking on the designed push button in the controller, the received data would activate the trigger. The SPI library of Arduino is the most important library for communicating with two or more microcontrollers. The researchers made computations based on research-based formulations regarding the shooting mechanism's force of the pressure to the ball. Getting the soring constant (spring stiffness) of a spring using hooke's law,

Figure 3. The velocity of the ball the obtained result of the ball's velocity is used to measure the ball's distance as it exits the barrel. This data also helps future researchers with a related topic using spring as a reference. Since the ball's velocity is calculated, the projectile range can now be obtained. The shooting mechanism is on a one-meter altitude and assumes that the air pressure is not present and the position directly hits the ball's center. In Figure 3, the velocity of the fire distinguisher ball when the drone is one meter above the target is illustrated where the VY, and V, are representing the horizontal and vertical velocity of the ball with 0° angle of elevation (an angle of elevation is formed between the line of sight and horizontal line). Here we present the line of sight with. See (5) to (8) for details.

Figure 4. Prototype of UAV; (a) during the tests on the ground and (b) flying mode

A firefighting UAV with shooting and dropping mechanism has been designed and developed in this study where it can carry up to two fire-distinguisher balls and act as the surveillance to help firefighters monitor the area. There are several components used for this project. The list of components and their utilization in this project is presented in Table 1.

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From selfish auctioning to incentivized marketing

by nathan Michael

2022, Autonomous Robots

Auction and market-based mechanisms are among the most popular methods for distributed task allocation in multi-robot systems. Most of these mechanisms were designed in an heuristic way and analysis of the quality of the resulting... more

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Multi-robot, multi-target Particle Swarm Optimization search in noisy wireless environments

by Milos Manic

2022, 2009 2nd Conference on Human System Interactions

Multiple small robots (swarms) can work together using Particle Swarm Optimization (PSO) to perform tasks that are difficult or impossible for a single robot to accomplish. The problem considered in this paper is exploration of an unknown... more

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Multi-robot, multi-target Particle Swarm Optimization search in noisy wireless environments

by Milos Manic

2022, 2009 2nd Conference on Human System Interactions

Multiple small robots (swarms) can work together using Particle Swarm Optimization (PSO) to perform tasks that are difficult or impossible for a single robot to accomplish. The problem considered in this paper is exploration of an unknown... more

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Fire-fighting UAV with shooting mechanism of fire extinguishing ball for smart city

by Nico Santos

2022, Indonesian Journal of Electrical Engineering and Computer Science

With the growth of technology and massive city development, firefighting services have become more challenging to cope with a smart-city concept. One of the challenges that firefighters are facing is reaching the top floors of high-raised... more

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Particle Swarm Optimization—An Adaptation for the Control of Robotic Swarms

by George Rossides

2022, Robotics

Particle Swarm Optimization (PSO) is a numerical optimization technique based on the motion of virtual particles within a multidimensional space. The particles explore the space in an attempt to find minima or maxima to the optimization... more

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A Modified Particle Swarm Optimization on Search Tasking

by Mohammad Ahmadi

2022, Research Journal of Applied Sciences, Engineering and Technology

Recently, more and more researches have been conducted on the multi-robot system by applying bioinspired algorithms. Particle Swarm Optimization (PSO) is one of the optimization algorithms that model a set of solutions as a swarm of... more

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Task assignment for robots with limited communication

by Xiaoshan Bai

2022, 2017 36th Chinese Control Conference (CCC)

This paper investigates the task assignment problem for multiple dispersed robots constrained by limited communication range. The robots are initially randomly distributed and need to visit several target locations while trying to... more

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Multi-Agent Task Allocation in Complementary Teams: A Hunter and Gatherer Approach

by Harley R Myler

2022

Consider a dynamic task allocation problem, where tasks are unknowingly distributed over an environment. This paper considers each task comprised of two sequential subtasks: detection and completion, where each subtask can only be carried... more

Fig. 1. CPM and UPM. 7; and T, are in agent’s CPM and UPM, respectively. T, is beyond agent’s uncertainty boundary. B. Reasoning Mechanism In this subsection, reasoning mechanisms for both hunters and gatherers are developed to establish their behavior during a mission that determines the way that they communicate, negotiate, and cooperate. Since fundamentals of reasoning mechanisms are similar for both type of agents, for the sake of brevity, we consider a general agent defined as a’? with Je {h,g}, where

Fig. 2. Possible scenarios: (a) reverse auction scenario, and (b) auction scenario. According to Fig. 3, at the first decision node, a’ makes an offer regarding the explained process, and then at the second step, and a? makes an acceptance or rejection decision, using the proposed reasoning mechanisms.

Fig. 4. An example explaining the necessity of multi-tasking capability of gatherer agents.

Fig. 5. An example of multitask-planning by a gatherer agent Fig. 5 illustrates an example in which a fills out its action plan where q,,,,=5. All sequences happen before accomplishing v,. In sequence |, a* has already v, in its action plan and chooses 7, to negotiate. In sequence 2, a* has reached an agreement for accomplishing 7;., and adds it to its action plan as v,. Moreover, there are two candidates and it chooses T.., to negotiate because T,, is not feasible and fails to satisfy the condition mentioned in step 2. In sequence 3, a’ has reached an agreement for accomplishing T,., and adds it to its action plan as v,. In addition, there are two candidate tasks namely T,, and T,;. T,; cannot be verified in step 3, though it’s feasible itself and passes step 2. Therefore, a’ chooses T.., for negotiation. In sequence 4, a’ has reached an agreement for accomplishing T,,, and adds it to its action plan as v,. Moreover, it chooses T,,, to negotiate because it passes all 3 steps. Although choosing T,., causes change in the path to 7, , but it does not bring a‘ into state 3 for T,. it passes all 3 steps. Although choosing T,., causes change in the path to 7, , but it does not bring a’ into state 3 for T,.

Fig. 6. investigating the fairness of the HGMP for: (a) hunter agents, and (b) gatherer agents. Statistically, for each type of agents, there is no significant differenc in agents’ effectiveness that demonstrates the fairness of the HGMP.

We ran the algorithms for all values of a@, and £, that are multiples of 0.025 such that O<a@,<0.5 and 0<£, <0.5, while @, = 8, =0.35. 7, has been calculated for each set of values for a, and f, as illustrated in Fig. 7. According to the results, 7, is vanishingly small when a, + 6, <0.2 that means agents cannot reach an agreement for completing the detected tasks. Further, 7, reaches its maximum when 0.2<a,+,<0.5. Next, 7, falls gradually when a, +, >0.5 because each gatherer’s CMP and UPM are large so that the agent does not fall into the State 3 and easily reach any agreement. As a result, each gatherer accomplishes a significant number of tasks inefficiently which reduces 7, . Wis ghee) eee eee: ee ee t@QIboes cee eee Be ee ey a I ee “Perec ef ase Me” 2 Py SE ee ey Fe ee Ce oe « ren Ses Se ae oo ae Mr: Me. ere « er | C,, as follows:

Fig. 7. Investigating the effect of gatherers’ CPM and UPM on the HGMP’s effectiveness 77, : (a) the contour plot of the results using a polynomial curve-fitting, and (b) polynomial curve-fitting.

Fig. 8. The effect of hunters’ CPM and UPM on the HGMP’s effectiveness 7, : (a) the contour plot of the results using a polynomial curve-fitting, and (b) polynomial curve-fitting.

Fig. 9. Investigating the effect of g_, on 77, . The results show a significant increase in 7, when we increase g_., from 1 to 10

Fig. 10. fairness of the HGMP for gatherer agents for each value of q,,,, . The results demonstrate that the workload is still distributed equally on both gatherers when g__ increases.

Fig. 11. the convergence of the HGMP’s total effectiveness for Gy =1. Gay =4> aNd Pony =10, during T,,. = 1000 iterations D. Functionality Validation of the HGMP by a Comparison

Fig. 12. Comparing the HGMP with an alternative model where each agent does both exploration and completion together. and maneuvering. Therefore, we first plotted the total effectiveness of the HGMP with respect to 9, / p, Which ranges fron Pr K Py» ie. P| Pe =0,to p, * P,. Le. Pr / Pe =1. Secondly, we ran the explained alternative approach to be able to judge the HGMP’s functionality. Since in this approach there is no hunter and gatherer scheme, we only have one type of agent and the obtained total effectiveness is dependent from this ratio p, / p, - By this comparison we basically wanted to answer this questior that: is the HGMP profitable compared to the alternative method? Fig. 12 shows the results of the implemented simulations fo: that purpose, as explained above. Thus, the answer is it depends to the ratio ,/, and that’s why we ended up in having a criterior

Algorithm 3 decision-making outer-loop algorithm of a! Algorithm 4 decision-making outer-loop algorithm of a gatherer agent

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From Pillars to AI Technology-Based Forest Fire Protection Systems

by Vassilis Moulianitis

2022, Artificial Intelligence - Applications in Agriculture and Bio-system Engineering [Working Title]

The importance of forest environment in the perspective of the biodiversity as well as from the economic resources which forests enclose, is more than evident. Any threat posed to this critical component of the environment should be... more

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Multi-objective grasshopper optimization algorithm for robot path planning in static environments

by zahra elmi

2022, 2018 IEEE International Conference on Industrial Technology (ICIT)

Finding the most appropriate path in robot navigation has been an interesting challenge in recent years. A number of different techniques have been proposed to address this problem. Heuristic methods are one of them that have been... more

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Multi-objective grasshopper optimization algorithm for robot path planning in static environments

by zahra elmi

2022, 2018 IEEE International Conference on Industrial Technology (ICIT)

Finding the most appropriate path in robot navigation has been an interesting challenge in recent years. A number of different techniques have been proposed to address this problem. Heuristic methods are one of them that have been... more

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Artificial Intelligence based Solutions for Cooperative Mobile Robots

by Doru Panescu

2022, Journal of Control Engineering and Applied Informatics

This paper presents the way some Artificial Intelligence techniques can contribute to obtaining an efficient solution for a cooperative robotic problem. Based on certain abstraction and sensing procedures the problem specification, the... more

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Aligning wildfire management resourcing decisions with operational needs

by Christopher Johnson

2022, Proceedings of the Winter Simulation Conference 2014

A hierarchical modeling and simulation (M&S) framework can help federal agencies integrate the myriad business resourcing decisions they face as unmanned aerospace vehicle (UAV) systems are deployed within their federally authorized... more

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Fire-fighting UAV with shooting mechanism of fire extinguishing ball for smart city

by Bernadette Durante

2021, Indonesian Journal of Electrical Engineering and Computer Science

With the growth of technology and massive city development, firefighting services have become more challenging to cope with a smart-city concept. One of the challenges that firefighters are facing is reaching the top floors of high-raised... more

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Wildfire Monitoring in Remote Areas using Autonomous Unmanned Aerial Vehicles

by Abolfazl Razi

2021, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

In this paper, we propose a drone-based wildfire monitoring system for remote and hard-to-reach areas. This system utilizes autonomous unmanned aerial vehicles (UAVs) with the main advantage of providing on-demand monitoring service... more

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Wildfire Monitoring in Remote Areas using Autonomous Unmanned Aerial Vehicles

by Abolfazl Razi

2021, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

In this paper, we propose a drone-based wildfire monitoring system for remote and hard-to-reach areas. This system utilizes autonomous unmanned aerial vehicles (UAVs) with the main advantage of providing on-demand monitoring service... more

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Distributed Optimization of Multi-Robot Motion with Time-Energy Criterion

by Moustafa Elshafei

2021, Path Planning for Autonomous Vehicles [Working Title]

This paper is an application of a special case of distributed optimization problem. It is applied on optimizing the motion of multiple robot systems. The problem is decomposed into L subproblems with L being the number of robot systems.... more

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Mobile robot path planning in dynamic environment based on cuckoo optimization algorithm

by Frimpong Kyeremeh

2021, International Journal of Advanced Robotic Systems

Nowadays, the usage of autonomous mobile robots that fulfill various activities in enormous number of applications without human’s interference in a dynamic environment are thriving. A dynamic environment is the robot’s environment which... more

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Cooperative Control of Multiple Uninhabited Aerial Vehicles for Monitoring and Fighting Wildfires

by Manish Kumar

2021

Uninhabited aerial vehicles provide numerous advantages in fighting wildland fires that include persistent operation and elimination of humans from performing what can be dull, dangerous, and dirty work. Multiple cooperating uninhabited... more

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Saez etal RISE08

by Lyuba Alboul

2021

The GUARDIANS project develops a swarm of autonomous robots to navigate in a warehouse in smoke. We concentrate on non-communicative swarming to reproduce basic robot swarm behaviours as formation generation and keeping. We apply the... more

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Applying Area Extension PSO in Robotic Swarm

by Chin Heng Ho

2021, Journal of Intelligent and Robotic Systems

Particle Swarm optimization (PSO) is a search method inspired from the social behaviors of animals. PSO has been found to outperform other methods in various tasks. Area Extended PSO (AEPSO) is an enhanced version of PSO that achieves... more

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Towards a Distributed Solution to the Multi-Robot Task Allocation Problem with Energetic and Spatiotemporal Constraints

by Ramdane MAAMRI

2021, Computer Science

The multi-robot task allocation problem consists of two distinct sets: a set of tasks (requiring resources) and a set of robots (offering resources); then tasks are allocated to robots; while optimizing a certain objective function,... more

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Fire Commander: An Interactive, Probabilistic Multi-agent Environment for Joint Perception-Action Tasks - Presentation Slides

by Esmaeil Seraj

2020, arXiv

The purpose of this tutorial is to help individuals use the \underline{FireCommander} game environment for research applications. The FireCommander is an interactive, probabilistic joint perception-action reconnaissance environment in... more

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FireCommander: An Interactive, Probabilistic Multi-agent Environment for Joint Perception-Action Tasks

by Esmaeil Seraj

2020, arXiv

The purpose of this tutorial is to help individuals use the \underline{FireCommander} game environment for research applications. The FireCommander is an interactive, probabilistic joint perception-action reconnaissance environment in... more

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Coordinated Control of UAVs for Human-Centered Active Sensing of Wildfires - Presentation Slides

by Esmaeil Seraj

2020, American Control Conference

Fighting wildfires is a precarious task, imperiling the lives of engaging firefighters and those who reside in the fire's path. Firefighters need online and dynamic observation of the firefront to anticipate a wildfire's unknown... more

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Coordinated Control of UAVs for Human-Centered Active Sensing of Wildfires

by Esmaeil Seraj

2020, 2020 American Control Conference

Fighting wildfires is a precarious task, imperiling the lives of engaging firefighters and those who reside in the fire's path. Firefighters need online and dynamic observation of the firefront to anticipate a wildfire's unknown... more

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Towards Robots-Assisted Ambient Intelligence

by Marin Lujak

2017, Proceedings of the Fifth International Conference on Agreement Technologies 2017, AT 2017. In Press.

An integrated network of mobile robots, personal smart devices, and smart spaces called " Robots-Assisted Ambient Intelligence " (RAmI) can provide for a more effective user assistance than if the former resources are used individually.... more

Fig. 1. Proposed RAmI architecture implemented in each robot The architecture used for the distributed coordination of robots in task assignment and routing is implemented in each one of the robots and is presented in Fig. 1. It

Fig. 2. Overview of the two-step coordination mechanism In the second step, designing a mathematical program related with the well-being of a patient is a challenging task since it is related to a patient’s physical condition, mental state, habits, and the context: time of day, location, etc. This is why the patient should be included in the decision-making preferably by learning his/her preferences according to rewards. These rewards could be awarded directly by the patient depend- ing on the sequence of offered activities or by physiological measures. For example, an evening activity sequence that improves the patient’s sleep would have a high reward and we may use a reinforcement learning approach. Users can be considered as sources

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Multi-robot routing with linear decreasing rewards over time

by Pinar Keskinocak

2016, 2009 IEEE International Conference on Robotics and Automation

We study multi-robot routing problems (MR-LDR) where a team of robots has to visit a set of given targets with linear decreasing rewards over time, such as required for the delivery of goods to rescue sites after disasters. The objective... more

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A distributed auction algorithm for the assignment problem

by George Pappas

2016, 2008 47th IEEE Conference on Decision and Control

The assignment problem constitutes one of the fundamental problems in the context of linear programming. Besides its theoretical significance, its frequent appearance in the areas of distributed control and facility allocation, where the... more

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Multi-Agent versus Multi-Robot and Other Byzantine Discussions

by Vicente Matellan

2016, Journal of Physical Agents

This special issue of the Journal of Physical Agents is devoted to multi-robot systems. Some might perhaps argue that the term should have been ”multi-agent systems” or ”physical multi-agent systems”, in accordance with the title of the... more

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A Decentralized Scheduling Policy for a Dynamically Reconfigurable Production System

by Stefano Giordani and

2015, Holonic and Multi-Agent Systems for Manufacturing. Proceedings of the 4th Int. Conf. on Industrial Applications of Holonic and Multi-Agent Systems, HoloMAS 2009, Linz, Austria, August 31-September 2, 2009. Marìk, V., Strasser, T. and Zoitl, A., Eds., LNCS

In this paper, the static layout of a traditional multi-machine factory producing a set of distinct goods is integrated with a set of mobile production units - robots. The robots dynamically change their work position to increment the... more

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A Particle Swarm Optimization Approach to Robotic Drill Route Optimization

by Zuwairie Ibrahim

2015, 2010 Fourth Asia International Conference on Mathematical/Analytical Modelling and Computer Simulation

Drill is spent on moving the drill bit between the holes. This operational time can be kept at a minimal level by optimizing the route taken by the robot. An optimized route translates to a minimal cost of operating the robot. This paper... more

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Value of Incomplete Information in Mobile Target Allocation

by Marin Lujak and

2015, Multiagent System Technologies. Proceedings of the 9th German Conference, MATES 2011, Berlin, Germany, October 6-7, 2011. Klügl, F. and Ossowski, S. (Eds.), LNCS

In this paper, we consider a decentralized approach to the multi-agent target allocation problem where agents are partitioned in two groups and every member of each group is a possible target for the members of the opposite group. Each... more

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ORCAS: Optimized Robots Conﬁguration and Scheduling System

by Marin Lujak and

2015, Proceedings of the 30th Annual ACM Symposium on Applied Computing - SAC '15

In this paper, we study a distributed intelligent multi-robot system (MRS) in assembly setting where robots have partially overlapping capabilities. We treat the problem of the system's self-(re)configurability and self-optimization. In... more

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Multi-Agent Robotic Systems

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