Distributed Task Rescheduling With Time Constraints for the Optimization of Total Task Allocations in a Multirobot System

“…In [30], Whitbrook et al modified the performance impact (PI) task-allocation algorithm with ε-greedy and softmax auction selection methods to explore assignments with less rescue time; furthermore, in [31] the researchers presented a new algorithm based on the work in [30] to solve the problem of being trapped into the local minima and a static structure. To maximize the number of task allocations in a multi-robot system under strict time constraints, Turner et al [32] proposed an effective algorithm to improve the solutions' performance. However, they primarily focus on the application of market-based algorithms rather than generating global optimal solutions.…”

Section: Related Workmentioning

confidence: 99%

Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints

Geng

Chen

Nguyen

et al. 2021

Complex Intell. Syst.

View full text Add to dashboard Cite

This paper focuses on the problem of robot rescue task allocation, in which multiple robots and a global optimal algorithm are employed to plan the rescue task allocation. Accordingly, a modified particle swarm optimization (PSO) algorithm, referred to as task allocation PSO (TAPSO), is proposed. Candidate assignment solutions are represented as particles and evolved using an evolutionary process. The proposed TAPSO method is characterized by a flexible assignment decoding scheme to avoid the generation of unfeasible assignments. The maximum number of successful tasks (survivors) is considered as the fitness evaluation criterion under a scenario where the survivors’ survival time is uncertain. To improve the solution, a global best solution update strategy, which updates the global best solution depends on different phases so as to balance the exploration and exploitation, is proposed. TAPSO is tested on different scenarios and compared with other counterpart algorithms to verify its efficiency.

show abstract

“…It is a distributed algorithm that iterates between the bundle construction phase and the conflict resolution phase. Based on it, Turner et al [10] presented a decentralized method for maximizing the number of tasks allocation under strict time constraints. It is based on auction theory, and multiple reassignments among networked robots may be required to create a feasible time according to the performance requirements.…”

Section: Introductionmentioning

confidence: 99%

How Good are Distributed Allocation Algorithms for Solving Urban Search and Rescue Problems? A Comparative Study With Centralized Algorithms

Geng

Meng

Gong

et al. 2019

IEEE Trans. Automat. Sci. Eng.

Self Cite

View full text Add to dashboard Cite

In this paper, a modified centralized algorithm based on particle swarm optimization (MCPSO) is presented to solve the task allocation problem in the search and rescue domain. The reason for this paper is to provide a benchmark against distributed algorithms in search and rescue application area. The hypothesis of this paper is that a centralized algorithm should perform better than distributed algorithms because it has all the available information at hand to solve the problem. Therefore, the centralized approach will provide a benchmark for evaluating how well the distributed algorithms are working and how much improvement can still be gained. Among the distributed algorithms, the consensus-based bundle algorithm (CBBA) is a relatively recent method based on the market auction mechanism, which is receiving considerable attention. Other distributed algorithms, such as PI and PI with softmax, have shown to perform better than CBBA. Therefore, in this paper, the three distributed algorithms mentioned earlier are compared against three centralized algorithms. They are particle swarm optimization, MCPSO, described in this paper, and genetic algorithms. Two experiments were conducted. The first involved comparing all the abovementioned algorithms, both centralized and distributed, using the same set of application scenarios. It is found that MCPSO always outperforms the other five algorithms in time cost. Due to the high failure rate of CBBA and the other two centralized methods, the second experiment focused on carrying out more tests to compare MCPSO against PI and PI with softmax. All the results are shown and analyzed to determine the performance gaps between the distributed algorithms and the MCPSO. Note to Practitioners-This paper was motivated by the limitation of current distributed task allocation algorithms as they cannot achieve performances that are as good as the centralized ones. Therefore, a centralized algorithm is designed to evaluate the performance gap between the state-of-the-art distributed and centralized approaches. In the future research section, a new distributed particle swarm optimization (PSO) algorithm

show abstract

Distributed Task Rescheduling With Time Constraints for the Optimization of Total Task Allocations in a Multirobot System

Cited by 107 publications

References 51 publications

Fast consensus for fully distributed multi-agent task allocation

Fast consensus for fully distributed multi-agent task allocation

Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints

How Good are Distributed Allocation Algorithms for Solving Urban Search and Rescue Problems? A Comparative Study With Centralized Algorithms

Contact Info

Product

Resources

About