Task assignment of multi-robot systems based on improved genetic algorithms

Li, Siding; Xu, Xin; Zuo, Lei

doi:10.1109/icma.2015.7237695

Cited by 13 publications

(3 citation statements)

References 16 publications

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“…However, robot teams are not yet successfully clustered and assigned to multiple tasks effectively. To efficiently and securely explore and recon a given region with a large number of robots, Li et al [36] introduced an enhanced genetic algorithm (IGA) to tackle the job assignment issue of a multi-robot system. Searching the numerous identical sections of the specified region is a subtask that must be completed in order to solve a challenge.…”

Section: Related Workmentioning

confidence: 99%

A Convex Optimization Approach to Multi-Robot Task Allocation and Path Planning

Lei

Chintam

Luo

et al. 2023

Sensors

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In real-world applications, multiple robots need to be dynamically deployed to their appropriate locations as teams while the distance cost between robots and goals is minimized, which is known to be an NP-hard problem. In this paper, a new framework of team-based multi-robot task allocation and path planning is developed for robot exploration missions through a convex optimization-based distance optimal model. A new distance optimal model is proposed to minimize the traveled distance between robots and their goals. The proposed framework fuses task decomposition, allocation, local sub-task allocation, and path planning. To begin, multiple robots are firstly divided and clustered into a variety of teams considering interrelation and dependencies of robots, and task decomposition. Secondly, the teams with various arbitrary shape enclosing intercorrelative robots are approximated and relaxed into circles, which are mathematically formulated to convex optimization problems to minimize the distance between teams, as well as between a robot and their goals. Once the robot teams are deployed into their appropriate locations, the robot locations are further refined by a graph-based Delaunay triangulation method. Thirdly, in the team, a self-organizing map-based neural network (SOMNN) paradigm is developed to complete the dynamical sub-task allocation and path planning, in which the robots are dynamically assigned to their nearby goals locally. Simulation and comparison studies demonstrate the proposed hybrid multi-robot task allocation and path planning framework is effective and efficient.

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Section: Related Workmentioning

confidence: 99%

A Convex Optimization Approach to Multi-Robot Task Allocation and Path Planning

Lei

Chintam

Luo

et al. 2023

Sensors

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“…Before combining the Particle Swarm Optimization and Genetic Algorithms, it is essential to analyze these two algorithms [ 51 , 52 , 53 , 54 ]. These two algorithms have their own advantages and disadvantages.…”

Section: Multi-objective Optimization Problem In Precision Farmingmentioning

confidence: 99%

A Mission Planning Approach for Precision Farming Systems Based on Multi-Objective Optimization

Zhai

Ortega

Martínez

et al. 2018

Sensors

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As the demand for food grows continuously, intelligent agriculture has drawn much attention due to its capability of producing great quantities of food efficiently. The main purpose of intelligent agriculture is to plan agricultural missions properly and use limited resources reasonably with minor human intervention. This paper proposes a Precision Farming System (PFS) as a Multi-Agent System (MAS). Components of PFS are treated as agents with different functionalities. These agents could form several coalitions to complete the complex agricultural missions cooperatively. In PFS, mission planning should consider several criteria, like expected benefit, energy consumption or equipment loss. Hence, mission planning could be treated as a Multi-objective Optimization Problem (MOP). In order to solve MOP, an improved algorithm, MP-PSOGA, is proposed, taking advantages of the Genetic Algorithms and Particle Swarm Optimization. A simulation, called precise pesticide spraying mission, is performed to verify the feasibility of the proposed approach. Simulation results illustrate that the proposed approach works properly. This approach enables the PFS to plan missions and allocate scarce resources efficiently. The theoretical analysis and simulation is a good foundation for the future study. Once the proposed approach is applied to a real scenario, it is expected to bring significant economic improvement.

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“…The UAVs act on orders, without any independent decision-making. The advantage of a centralized algorithm is that it can guarantee better global optimality, but if some unexpected situations are encountered during task execution, the task may fail, such as integer linear programming algorithm, graph theory, genetic algorithm [16], and particle swarm optimization algorithm [17]. Compared with centralized task allocation, the distributed algorithm is more flexible.…”

Section: Introductionmentioning

confidence: 99%

Real‐Time Task Allocation of Heterogeneous Unmanned Aerial Vehicles for Search and Prosecute Mission

Zhai

Zhao

et al. 2021

Wireless Communications and Mobile Computing

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In recent years, the Internet of Things (IoT) has developed rapidly after the era of computers and smart phones, which is expected to be applied to cities to improve the quality of life and realize the intelligence of smart cities. In particular, with the outbreak of coronavirus disease 2019 (COVID-19) last year, in order to reduce contact, some IoT devices, such as robots, unmanned aerial vehicles (UAVs), and unmanned vehicles, have played a great role in temperature monitoring, goods delivery, and so on. In this paper, we study the real-time task allocation problem of heterogeneous UAVs searching and delivering goods in the city. Considering the resource requirement of task and resource constraints of the UAV, when the resource of a single UAV cannot meet the requirement of the task, we propose a method of forming a UAV coalition based on contract net protocol. We analyze the coalition formation problem from two aspects: mission completion time and UAV’s energy consumption. Firstly, the mathematical model is established according to the optimization objective and condition constraints. Then, according to the established mathematical model, different coalition formation algorithms are proposed. To minimize the mission completion time, we propose a two-stage coalition formation algorithm. Aiming at minimizing the UAV’s energy consumption, it is transformed into a zero-one integer programming problem, which can be solved by the existing solver. Then, considering both mission completion time and energy consumption, we propose a coalition formation algorithm based on a resource tree. Finally, we design some simulation experiments and compare with the task allocation algorithm based on resource welfare. The simulation results show that our proposed algorithms are feasible and effective.

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Task assignment of multi-robot systems based on improved genetic algorithms

Cited by 13 publications

References 16 publications

A Convex Optimization Approach to Multi-Robot Task Allocation and Path Planning

A Convex Optimization Approach to Multi-Robot Task Allocation and Path Planning

A Mission Planning Approach for Precision Farming Systems Based on Multi-Objective Optimization

Real‐Time Task Allocation of Heterogeneous Unmanned Aerial Vehicles for Search and Prosecute Mission

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