scholarly journals Emergent Task Allocation for Mobile Robots

2007 ◽  
Author(s):  
N. Atay ◽  
B. Bayazit
Keyword(s):  
Mobile Robots ◽  
Task Allocation

scholarly journals A Balanced Heuristic Mechanism for Multirobot Task Allocation of Intelligent Warehouses

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Luowei Zhou ◽  
Yuanyuan Shi ◽  
Jiangliu Wang ◽  
Pei Yang
Keyword(s):  
Mobile Robots ◽  
Task Allocation ◽  
System Performance ◽  
Travel Cost ◽  
Allocation Problem ◽  
Good System ◽  
Allocation Methods

This paper presents a new mechanism for the multirobot task allocation problem in intelligent warehouses, where a team of mobile robots are expected to efficiently transport a number of given objects. We model the system with unknown task cost and the objective is twofold, that is, equally allocating the workload as well as minimizing the travel cost. A balanced heuristic mechanism (BHM) is proposed to achieve this goal. We raised two improved task allocation methods by applying this mechanism to the auction and clustering strategies, respectively. The results of simulated experiments demonstrate the success of the proposed approach regarding increasing the utilization of the robots as well as the efficiency of the whole warehouse system (by 5~15%). In addition, the influence of the coefficientαin the BHM is well-studied. Typically, this coefficient is set between 0.7~0.9 to achieve good system performance.

scholarly journals A Study on Various Task-Work Allocation Algorithms in Swarm Robotics

2020 ◽  
Vol 2 (2) ◽  
pp. 123-134
Author(s):  
Annamalai .L ◽  
Mohammed Siddiq. M ◽  
Ravi Shankar. S ◽  
Vigneshwar .S
Keyword(s):  
Mobile Robots ◽  
Swarm Intelligence ◽  
Task Allocation ◽  
Swarm Robotics ◽  
Entire System ◽  
Complex Tasks ◽  
Work Allocation ◽  
Allocation Algorithms

This paper discusses the various task allocation algorithms that have been researched, analyzed, and used in swarm robotics. The main reason for switching over to swarm robotics from ordinary mobile robots is because of its ability to perform complex tasks co-operatively with other bots rather than individually. Furthermore, they can be scaled to perform any kind of tasks. To carry out tasks like foraging, surveying and other such tasks that require swarm intelligence, task allocation plays an important role. It is the crux of the entire system and plays a huge role in the success of the implementation of swarm robotics. Few algorithms that address this task allocation have been briefly discussed here.

The call of duty: Self-organised task allocation in a population of up to twelve mobile robots

2000 ◽  
Vol 30 (1-2) ◽  
pp. 65-84 ◽  
Author(s):  
Michael J.B. Krieger ◽  
Jean-Bernard Billeter
Keyword(s):  
Mobile Robots ◽  
Task Allocation

scholarly journals 2A1-G23 Task allocation of mobile robots using infection model in an ad-hoc network

2008 ◽  
Vol 2008 (0) ◽  
pp. _2A1-G23_1-_2A1-G23_4
Author(s):  
Kazuki OGAWA ◽  
Daisuke KURABAYASHI ◽  
Tetsuro FUNATO
Keyword(s):  
Mobile Robots ◽  
Task Allocation ◽  
Ad Hoc Network ◽  
Ad Hoc ◽  
Infection Model

scholarly journals Development of an Artificial Intelligence System for the Instruction and Control of Cooperating Mobile Robots

2021 ◽  
Author(s):  
◽  
Praneel Chand
Keyword(s):  
Artificial Intelligence ◽  
Path Planning ◽  
Mobile Robots ◽  
Task Allocation ◽  
Expert Knowledge ◽  
Artificial Intelligence System ◽  
Task Execution ◽  
Simulation Experiments ◽  
Intelligence System ◽  
Weight Variation

<p>This thesis focuses on the development of an artificial intelligence system for a heterogeneous ensemble of mobile robots. Many robots in the ensemble may have limited processing, communication, sensing, and/or actuation capabilities. This means that each robot may not be able to execute all tasks that are input to the system. A hierarchical system is proposed to permit robots with superior processing and communication abilities to assign tasks and coordinate the less computationally able robots. The limited processing robots may also utilise the resources of superior robots during task execution. Effective task allocation and coordination should result in efficient execution of a global task. Many existing approaches to robot task allocation assume expert knowledge for task specification. This is not ideal if a non-expert human user wants to modify the task requirements. A novel reduced human user input task allocation and feedback coordination technique for limited capability mobile robots is developed and implemented. Unlike existing approaches, the presented method focuses on expressing tasks and robots in terms of processing, communication, sensing, and actuation physical resources. This has the potential to allow non-expert human users to specify tasks to the team of robots. Fuzzy inference systems are utilised to simplify detailed robot information for comparison with simple human user inputs that represent task resource requirements. Like many existing task allocation methods, a greedy algorithm is employed to select robots. This can result in suboptimal task allocation. In addition to this, the non-expert user’s task specifications might be erroneous in some instances. Hence, a feedback coordination component monitors robot performance during task execution. In this thesis, a customised multi-robot mapping and exploration task is utilised as a model task to test the effectiveness of the developed task allocation and feedback coordination strategy. Extensive simulation experiments with various robot team configurations are executed in environments of varying sizes and obstacle densities to assess the performance of the technique. Task allocation is able to identify suitable robots and is robust to selection weight variation. The task allocation process is subjective to fuzzy membership function parameters which may vary for different This thesis focuses on the development of an artificial intelligence system for a heterogeneous ensemble of mobile robots. Many robots in the ensemble may have limited processing, communication, sensing, and/or actuation capabilities. This means that each robot may not be able to execute all tasks that are input to the system. A hierarchical system is proposed to permit robots with superior processing and communication abilities to assign tasks and coordinate the less computationally able robots. The limited processing robots may also utilise the resources of superior robots during task execution. Effective task allocation and coordination should result in efficient execution of a global task. Many existing approaches to robot task allocation assume expert knowledge for task specification. This is not ideal if a non-expert human user wants to modify the task requirements. A novel reduced human user input task allocation and feedback coordination technique for limited capability mobile robots is developed and implemented. Unlike existing approaches, the presented method focuses on expressing tasks and robots in terms of processing, communication, sensing, and actuation physical resources. This has the potential to allow non-expert human users to specify tasks to the team of robots. Fuzzy inference systems are utilised to simplify detailed robot information for comparison with simple human user inputs that represent task resource requirements. Like many existing task allocation methods, a greedy algorithm is employed to select robots. This can result in suboptimal task allocation. In addition to this, the non-expert user’s task specifications might be erroneous in some instances. Hence, a feedback coordination component monitors robot performance during task execution. In this thesis, a customised multi-robot mapping and exploration task is utilised as a model task to test the effectiveness of the developed task allocation and feedback coordination strategy. Extensive simulation experiments with various robot team configurations are executed in environments of varying sizes and obstacle densities to assess the performance of the technique. Task allocation is able to identify suitable robots and is robust to selection weight variation. The task allocation process is subjective to fuzzy membership function parameters which may vary for different users. Feedback coordination is robust to variation in weights and thresholds for failure detection. This permits the correction of suboptimal allocations arising from greedy task allocation, incorrect initial task specifications or unexpected failures. By being robust within the tested limits, weights and thresholds can be intuitively selected. However, other parameters such as ideal achievement data can be difficult to accurately characterise in some instances. A hierarchical hybrid deliberative-reactive navigation system for memory constrained heterogeneous robots to navigate obstructed environments is developed. Deliberative control is developed using a modified version of the A* algorithm and a rectangular occupancy grid map. A novel two-tiered path planner executes on limited memory mobile robots utilising the memory of a computationally powerful robot to enable navigation beyond localised regions of a large environment. Reactive control is developed using a modified dynamic window approach and a polar histogram technique to remove the need for periodic path planning. A range of simulation experiments in different sized environments is conducted to assess the performance of the two-tiered path planning strategy. The path planner is able to achieve superior or comparable execution times to non-memory constrained path planning when small sized local maps are employed in large global environments. Performance of hybrid deliberative-reactive navigation is assessed in a range of simulated environments and is also validated on a real robot. The developed reactive control system outperforms the dynamic window method.</p>

Indoor Surveillance Application using Wireless Robots and Sensor Networks

2013 ◽  
pp. 19-57
Author(s):  
Anis Koubaa ◽  
Sahar Trigui ◽  
Imen Chaari
Keyword(s):  
Sensor Networks ◽  
Path Planning ◽  
Mobile Robots ◽  
Task Allocation ◽  
Wireless Sensor ◽  
Robot Path Planning ◽  
Research Fields ◽  
Robot Task ◽  
Pervasive Applications ◽  
Surveillance Application

Mobile robots and Wireless Sensor Networks (WSNs) are enabling technologies of ubiquitous and pervasive applications. Surveillance is one typical example of such applications for which the literature proposes several solutions using mobile robots and/or WSNs. However, robotics and WSNs have mostly been considered as separate research fields, and little work has investigated the marriage of these two technologies. In this chapter, the authors propose an indoor surveillance application, SURV-TRACK, which controls a team of multiple cooperative robots supported by a WSN infrastructure. They propose a system model for SURV-TRACK to demonstrate how robots and WSNs can complement each other to efficiently accomplish the surveillance task in a distributed manner. Furthermore, the authors investigate two typical underlying problems: (1) Multi-Robot Task Allocation (MRTA) for target tracking and capturing and (2) robot path planning. The novelty of the solutions lies in incorporating a WSN in the problems’ models. The authors believe that this work advances the literature by demonstrating a concrete ubiquitous application that couples robotic and WSNs and proposes new solutions for path planning and MRTA problems.

scholarly journals Indoor Surveillance Application using Wireless Robots and Sensor Networks

Robotics ◽  
2013 ◽  
pp. 838-875
Author(s):  
Anis Koubaa ◽  
Sahar Trigui ◽  
Imen Chaari
Keyword(s):  
Sensor Networks ◽  
Path Planning ◽  
Mobile Robots ◽  
Task Allocation ◽  
Wireless Sensor ◽  
Robot Path Planning ◽  
Research Fields ◽  
Robot Task ◽  
Pervasive Applications ◽  
Surveillance Application

Mobile robots and Wireless Sensor Networks (WSNs) are enabling technologies of ubiquitous and pervasive applications. Surveillance is one typical example of such applications for which the literature proposes several solutions using mobile robots and/or WSNs. However, robotics and WSNs have mostly been considered as separate research fields, and little work has investigated the marriage of these two technologies. In this chapter, the authors propose an indoor surveillance application, SURV-TRACK, which controls a team of multiple cooperative robots supported by a WSN infrastructure. They propose a system model for SURV-TRACK to demonstrate how robots and WSNs can complement each other to efficiently accomplish the surveillance task in a distributed manner. Furthermore, the authors investigate two typical underlying problems: (1) Multi-Robot Task Allocation (MRTA) for target tracking and capturing and (2) robot path planning. The novelty of the solutions lies in incorporating a WSN in the problems’ models. The authors believe that this work advances the literature by demonstrating a concrete ubiquitous application that couples robotic and WSNs and proposes new solutions for path planning and MRTA problems.

scholarly journals Development of an Artificial Intelligence System for the Instruction and Control of Cooperating Mobile Robots

2021 ◽  
Author(s):  
◽  
Praneel Chand
Keyword(s):  
Artificial Intelligence ◽  
Path Planning ◽  
Mobile Robots ◽  
Task Allocation ◽  
Expert Knowledge ◽  
Artificial Intelligence System ◽  
Task Execution ◽  
Simulation Experiments ◽  
Intelligence System ◽  
Weight Variation

<p>This thesis focuses on the development of an artificial intelligence system for a heterogeneous ensemble of mobile robots. Many robots in the ensemble may have limited processing, communication, sensing, and/or actuation capabilities. This means that each robot may not be able to execute all tasks that are input to the system. A hierarchical system is proposed to permit robots with superior processing and communication abilities to assign tasks and coordinate the less computationally able robots. The limited processing robots may also utilise the resources of superior robots during task execution. Effective task allocation and coordination should result in efficient execution of a global task. Many existing approaches to robot task allocation assume expert knowledge for task specification. This is not ideal if a non-expert human user wants to modify the task requirements. A novel reduced human user input task allocation and feedback coordination technique for limited capability mobile robots is developed and implemented. Unlike existing approaches, the presented method focuses on expressing tasks and robots in terms of processing, communication, sensing, and actuation physical resources. This has the potential to allow non-expert human users to specify tasks to the team of robots. Fuzzy inference systems are utilised to simplify detailed robot information for comparison with simple human user inputs that represent task resource requirements. Like many existing task allocation methods, a greedy algorithm is employed to select robots. This can result in suboptimal task allocation. In addition to this, the non-expert user’s task specifications might be erroneous in some instances. Hence, a feedback coordination component monitors robot performance during task execution. In this thesis, a customised multi-robot mapping and exploration task is utilised as a model task to test the effectiveness of the developed task allocation and feedback coordination strategy. Extensive simulation experiments with various robot team configurations are executed in environments of varying sizes and obstacle densities to assess the performance of the technique. Task allocation is able to identify suitable robots and is robust to selection weight variation. The task allocation process is subjective to fuzzy membership function parameters which may vary for different This thesis focuses on the development of an artificial intelligence system for a heterogeneous ensemble of mobile robots. Many robots in the ensemble may have limited processing, communication, sensing, and/or actuation capabilities. This means that each robot may not be able to execute all tasks that are input to the system. A hierarchical system is proposed to permit robots with superior processing and communication abilities to assign tasks and coordinate the less computationally able robots. The limited processing robots may also utilise the resources of superior robots during task execution. Effective task allocation and coordination should result in efficient execution of a global task. Many existing approaches to robot task allocation assume expert knowledge for task specification. This is not ideal if a non-expert human user wants to modify the task requirements. A novel reduced human user input task allocation and feedback coordination technique for limited capability mobile robots is developed and implemented. Unlike existing approaches, the presented method focuses on expressing tasks and robots in terms of processing, communication, sensing, and actuation physical resources. This has the potential to allow non-expert human users to specify tasks to the team of robots. Fuzzy inference systems are utilised to simplify detailed robot information for comparison with simple human user inputs that represent task resource requirements. Like many existing task allocation methods, a greedy algorithm is employed to select robots. This can result in suboptimal task allocation. In addition to this, the non-expert user’s task specifications might be erroneous in some instances. Hence, a feedback coordination component monitors robot performance during task execution. In this thesis, a customised multi-robot mapping and exploration task is utilised as a model task to test the effectiveness of the developed task allocation and feedback coordination strategy. Extensive simulation experiments with various robot team configurations are executed in environments of varying sizes and obstacle densities to assess the performance of the technique. Task allocation is able to identify suitable robots and is robust to selection weight variation. The task allocation process is subjective to fuzzy membership function parameters which may vary for different users. Feedback coordination is robust to variation in weights and thresholds for failure detection. This permits the correction of suboptimal allocations arising from greedy task allocation, incorrect initial task specifications or unexpected failures. By being robust within the tested limits, weights and thresholds can be intuitively selected. However, other parameters such as ideal achievement data can be difficult to accurately characterise in some instances. A hierarchical hybrid deliberative-reactive navigation system for memory constrained heterogeneous robots to navigate obstructed environments is developed. Deliberative control is developed using a modified version of the A* algorithm and a rectangular occupancy grid map. A novel two-tiered path planner executes on limited memory mobile robots utilising the memory of a computationally powerful robot to enable navigation beyond localised regions of a large environment. Reactive control is developed using a modified dynamic window approach and a polar histogram technique to remove the need for periodic path planning. A range of simulation experiments in different sized environments is conducted to assess the performance of the two-tiered path planning strategy. The path planner is able to achieve superior or comparable execution times to non-memory constrained path planning when small sized local maps are employed in large global environments. Performance of hybrid deliberative-reactive navigation is assessed in a range of simulated environments and is also validated on a real robot. The developed reactive control system outperforms the dynamic window method.</p>

A Probabilistic Technique for Allocation of Tasks With Priorities in a School of Autonomous Mobile Robots

2019 ◽  
Author(s):  
Soovadeep Bakshi ◽  
Tianheng Feng ◽  
Zeyu Yan ◽  
Dongmei Chen
Keyword(s):  
Mobile Robots ◽  
Real Time ◽  
Task Allocation ◽  
Autonomous Mobile Robots ◽  
Maximization Problem ◽  
Planning Problem ◽  
Optimization Scheme ◽  
Manufacturing Automation ◽  
Task List ◽  
Probabilistic Technique

Abstract Using Autonomous Mobile Robots (AMRs) to collaboratively complete tasks has received a lot of attention in recent years from both industry and acedemia, especially in applications related to manufacturing automation. However, in spite of the technological progress, there are many challenges yet to be addressed in prioritized task allocation and scheduling of a school of AMRs in real time. This paper focuses on the real-time task allocation problem for a school of AMRs, i.e., given a prioritized task list and multiple AMRs, determining the set of tasks to be completed by each AMR. This paper proposes a probabilistic task allocation method which formulates the problem as a log-likelihood maximization problem, and uses a cyclic optimization scheme. This algorithm is shown to perform better when compared to commonly-used algorithms for asymmetric clustering. This proposed algorithm can be combined with scheduling methods to generate a ‘cluster-first, order-second’ solution to the multi-AMR task planning problem.

Sign in / Sign up

Export Citation Format

Share Document