Multi-Robot Task Allocation Based on Cloud Ant Colony Algorithm

Li, Xu; Liu, Zhengyan; Tan, Fuxiao

doi:10.1007/978-3-319-70093-9_1

Cited by 12 publications

(9 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The multiple mobile robots' dynamic task allocation problem is generalized as multiple travelling salesman problem (Arif and Haider, 2017) and classified as a combinatorial optimization problem (dos Reis and Bastos, 2017). It is solved by evolutionary optimization algorithms: genetic algorithm (Arif and Haider, 2017;Arif and Haider, 2018;Bänziger et al, 2018), particle swarm optimization (PSO) (Alshawi and Shalan, 2017), ant colony optimization (ACO) (Li et al, 2017b), the variants of PSO and ACO algorithms (Muhuri and Rauniyar, 2017).…”

Section: Optimization-based Task Allocationmentioning

confidence: 99%

“…Few researchers considered integer programming (Li and Li, 2017;Su et al, 2018;Zhou et al, 2019) and various search algorithms (Zhao et al, 2015;Kartal et al, 2016;Mitiche et al, 2019). Many researchers used metaheuristic algorithms (Liu and Kroll, 2012;Alshawi and Shalan, 2017;Li et al, 2017b, Z. Zhu et al, 2017Arif and Haider, 2018;Chen et al, 2018b, Padmanabhan Panchu et al, 2018Wang et al, 2018;Zhou et al, 2019) to solve this optimization problem, and this could be because of the ease of implementation of such algorithms.…”

Section: Optimization-based Task Allocationmentioning

confidence: 99%

See 1 more Smart Citation

Review on state-of-the-art dynamic task allocation strategies for multiple-robot systems

Seenu

Chetty

Ramya

et al. 2020

View full text Add to dashboard Cite

Purpose This paper aims to present a concise review on the variant state-of-the-art dynamic task allocation strategies. It presents a thorough discussion about the existing dynamic task allocation strategies mainly with respect to the problem application, constraints, objective functions and uncertainty handling methods. Design/methodology/approach This paper briefs the introduction of multi-robot dynamic task allocation problem and discloses the challenges that exist in real-world dynamic task allocation problems. Numerous task allocation strategies are discussed in this paper, and it establishes the characteristics features between them in a qualitative manner. This paper also exhibits the existing research gaps and conducive future research directions in dynamic task allocation for multiple mobile robot systems. Findings This paper concerns the objective functions, robustness, task allocation time, completion time, and task reallocation feature for performance analysis of different task allocation strategies. It prescribes suitable real-world applications for variant task allocation strategies and identifies the challenges to be resolved in multi-robot task allocation strategies. Originality/value This paper provides a comprehensive review of dynamic task allocation strategies and incites the salient research directions to the researchers in multi-robot dynamic task allocation problems. This paper aims to summarize the latest approaches in the application of exploration problems.

show abstract

Section: Optimization-based Task Allocationmentioning

confidence: 99%

Section: Optimization-based Task Allocationmentioning

confidence: 99%

Review on state-of-the-art dynamic task allocation strategies for multiple-robot systems

Seenu

Chetty

Ramya

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Zheng and Li describe artificial fish swarm to minimize the task execution time [12], and this allocation approach simulates the fish swarm behaviors, such as swarm, follow, and prey. Li et al establish cloud ant colony algorithm, and cloud model updates pheromone by using expectation, entropy, and hyper-entropy [13]. Chen et al propose multi-robot patrolling problems by using ant colony optimization based memetic algorithm, while simultaneously optimizing the maximum traveled distance and the total traveled distance [14].…”

Section: Related Work a Multi-robot Task Allocationmentioning

confidence: 99%

“…. , ET NR }, and ET i is the task time for R i in (13). The fitness function of our model is given in (14), and max() returns the maximum value of a set.…”

Section: Soltrawaitmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Robot Cooperative Task Allocation With Definite Path-Conflict-Free Handling

et al. 2019

View full text Add to dashboard Cite

Modeling and solving multi-robot task allocation with definite path-conflict-free handling is an important research, especially in real working environments. Some of the research lines are unable to obtain definite path-conflict-free solutions for multi-robot task allocations, such as using the penalty-term method in the fitness function to restrict the survival probabilities of the solutions with path conflicts. In some cases, these solutions are only able to satisfy the objective of minimizing task time. We formulate this problem based on grid maps, while focusing on the frequently used cooperative task allocation. In our model, two subtasks of each cooperative task must be executed by two robots, simultaneously. We propose vitality-driven genetic task allocation algorithm (VGTA), which is able to simultaneously minimize task time and realize definite conflict-free path planning. VGTA consists of local operators, such as random mutations, greedy crossovers, and vitality selection. Meanwhile, VGTA includes schedule conflict and path conflict handling strategies. In path conflict handling strategy, we not only consider the common path conflicts in a grid cell, but also focus on the path conflicts between robots when exchanging positions in the adjacent grid cells. Besides, we construct our benchmarks based on real working environments, such as factory, powerhouse, and airport environments. Experimental results indicate that VGTA's search capability and computation cost are satisfactory. Meanwhile, its solutions are able to be really executed.INDEX TERMS Multi-robot task allocation, cooperative task, schedule conflict, path conflict, unmanned multi-robot swarm.

show abstract

Task Allocation and Collaborative Planning Algorithm of the Sub-mother Unmanned Platform under Multi-task Constraints

Liu,

Li,

et al. 2024

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

Multi-Robot Task Allocation Based on Cloud Ant Colony Algorithm

Cited by 12 publications

References 2 publications

Review on state-of-the-art dynamic task allocation strategies for multiple-robot systems

Review on state-of-the-art dynamic task allocation strategies for multiple-robot systems

Multi-Robot Cooperative Task Allocation With Definite Path-Conflict-Free Handling

Task Allocation and Collaborative Planning Algorithm of the Sub-mother Unmanned Platform under Multi-task Constraints

Contact Info

Product

Resources

About