Distributed Task Allocation in Swarms of Robots

2012 ◽  
pp. 170-193
Author(s):  
Aleksandar Jevtic ◽  
Diego Andina ◽  
Mo Jamshidi
Keyword(s):  
Genetic Algorithm ◽  
Task Allocation ◽  
Autonomous Robots ◽  
Average Distance ◽  
Bees Algorithm ◽  
Inherent Feature ◽  
Decentralized Decision Making ◽  
Target Allocation ◽  
Available Information ◽  
Deployment Cost

This chapter introduces a swarm intelligence-inspired approach for target allocation in large teams of autonomous robots. For this purpose, the Distributed Bees Algorithm (DBA) was proposed and developed by the authors. The algorithm allows decentralized decision-making by the robots based on the locally available information, which is an inherent feature of animal swarms in nature. The algorithm’s performance was validated on physical robots. Moreover, a swarm simulator was developed to test the scalability of larger swarms in terms of number of robots and number of targets in the robot arena. Finally, improved target allocation in terms of deployment cost efficiency, measured as the average distance traveled by the robots, was achieved through optimization of the DBA’s control parameters by means of a genetic algorithm.

scholarly journals Distributed Task Allocation in Swarms of Robots

Robotics ◽  
2013 ◽  
pp. 450-473 ◽  
Author(s):  
Aleksandar Jevtić ◽  
Diego Andina ◽  
Mo Jamshidi
Keyword(s):  
Genetic Algorithm ◽  
Task Allocation ◽  
Autonomous Robots ◽  
Average Distance ◽  
Bees Algorithm ◽  
Inherent Feature ◽  
Decentralized Decision Making ◽  
Target Allocation ◽  
Available Information ◽  
Deployment Cost

This chapter introduces a swarm intelligence-inspired approach for target allocation in large teams of autonomous robots. For this purpose, the Distributed Bees Algorithm (DBA) was proposed and developed by the authors. The algorithm allows decentralized decision-making by the robots based on the locally available information, which is an inherent feature of animal swarms in nature. The algorithm’s performance was validated on physical robots. Moreover, a swarm simulator was developed to test the scalability of larger swarms in terms of number of robots and number of targets in the robot arena. Finally, improved target allocation in terms of deployment cost efficiency, measured as the average distance traveled by the robots, was achieved through optimization of the DBA’s control parameters by means of a genetic algorithm.

scholarly journals Reliability Based Task Allocation Analysis for Multi-Robot Systems by Using Fuzzy Logic

2018 ◽  
pp. 8-12
Author(s):  
Muhammed Oguz Tas ◽  
Ugur Yayan ◽  
Hasan Serhan Yavuz ◽  
Ahmet Yazici
Keyword(s):  
Fuzzy Logic ◽  
Task Allocation ◽  
System Reliability ◽  
Fuzzy Inference ◽  
Autonomous Robots ◽  
Robotic Systems ◽  
Inference System ◽  
Load Carrying ◽  
Robot Systems

Robotic systems are used many areas where it is dangerous or difficult for people to do. The importance of autonomous robots increased with the Industry 4.0, and the concept of reliability needed more attention for long term operability of robotic systems. In this study, reliability based task allocation analysis is performed for robots by using fuzzy logic. With the help of fuzzy inference system, the result of reliability based task allocation are obtained using the amount of carried load and load carrying distances. In the study, cases of task allocation based on nearest and reliability were analyzed and compared. Experimental results showed that, the system reliability that occurs with reliability based task allocation is higher than the system reliability that occurs with nearest based task allocation.

Genetic Planning Method and its Application to Planetary Exploration

2002 ◽  
Vol 124 (4) ◽  
pp. 698-701 ◽  
Author(s):  
Shane Farritor ◽  
Steven Dubowsky
Keyword(s):  
Genetic Algorithm ◽  
Actuator Saturation ◽  
Autonomous Robots ◽  
Action Plan ◽  
Planetary Exploration ◽  
Planning Method ◽  
Analytical Models ◽  
Wheel Slip ◽  
Physical Constraints ◽  
Mars Pathfinder

This paper describes a genetic algorithm planning method for autonomous robots in unstructured environments. It presents the approach and demonstrates its application to a laboratory planetary exploration problem. The method represents activities of the robot with discrete actions, or action modules. The action modules are assembled into an action plan with a Genetic Algorithm (GA). A successful plan allows the robot to complete the task without violating any physical constraints. Plans are developed that explicitly consider constraints such as power, actuator saturation, wheel slip, and vehicle stability. These are verified using analytical models of the robot and environment. The methodology is described in the context of planetary exploration similar to the NASA Mars Pathfinder mission. More aggressive missions are planned where rovers will explore scientifically important areas that are difficult to reach (e.g., ravines, craters, dry riverbeds, and steep cliffs). The proposed approach is designed for such areas.

scholarly journals Genetic Algorithm-Based Variable Value Control Method for Solving the Ground Target Attacking Weapon-Target Allocation Problem

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Chao Wang ◽  
Guangyuan Fu ◽  
Daqiao Zhang ◽  
Hongqiao Wang ◽  
Jiufen Zhao
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Control Method ◽  
Search Space ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
Mutation Strategy ◽  
Ground Target ◽  
Value Range ◽  
Target Allocation

Key ground targets and ground target attacking weapon types are complex and diverse; thus, the weapon-target allocation (WTA) problem has long been a great challenge but has not yet been adequately addressed. A timely and reasonable WTA scheme not only helps to seize a fleeting combat opportunity but also optimizes the use of weaponry resources to achieve maximum battlefield benefits at the lowest cost. In this study, we constructed a ground target attacking WTA (GTA-WTA) model and designed a genetic algorithm-based variable value control method to address the issue that some intelligent algorithms are too slow in resolving the problem of GTA-WTA due to the large scale of the problem or are unable to obtain a feasible solution. The proposed method narrows the search space and improves the search efficiency by constraining and controlling the variable value range of the individuals in the initial population and ensures the quality of the solution by improving the mutation strategy to expand the range of variables. The simulation results show that the improved genetic algorithm (GA) can effectively solve the large-scale GTA-WTA problem with good performance.

Resource Optimization Based on Adaptive Genetic Algorithm

2013 ◽  
Vol 339 ◽  
pp. 784-788
Author(s):  
Lei Wang ◽  
Yu Yun Kang
Keyword(s):  
Genetic Algorithm ◽  
Task Allocation ◽  
Optimization Problem ◽  
Resource Optimization ◽  
Adaptive Genetic Algorithm ◽  
Manufacturing Environment ◽  
Simulation Results

In order to allocate tasks and optimize resources well in dynamical manufacturing environment, the model for task allocation is established. An adaptive genetic algorithm (AGA) is applied to deal with it. A machine-based encoding approach is also adopted. The simulation results testify the validity of this method, and therefore the task allocation and resources optimization problem could be dealt with efficiently.

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