Multi-robot Task Scheduling Research Articles

Probabilistic Multi-Robot Task Scheduling for the Antarctic Environments with Crevasses

This paper deals with the problem of multi-robot task scheduling in the Antarctic environments with crevasses. Because the crevasses may cause hazardous situations when robots are operated in the Antarctic environments, robot navigation should be planned to safely avoid the positions of crevasses. However, the positions of the crevasses may be inaccurately measured due to the lack of sensor performance, the asymmetry of sensor data, and the possibility of crevasses drifting irregularly as time passes. To overcome these uncertain and asymmetric problems, this paper proposes a probabilistic multi-robot task scheduling method based on the Nearest Neighbors Test (NNT) algorithm and the probabilistic modeling of the positions of crevasses. The proposed method was tested with a Google map of the Antarctic environments and showed a better performance than the Ant Colony Optimization (ACO) algorithm and the Genetic Algorithm (GA) in the context of total cost and computational time.

Swiss Round Selection Algorithm for Multi-Robot Task Scheduling

Efficient and stable control and task assignment optimization in electronic commerce logistics and warehousing systems involving multiple robots executing multiple tasks is highly challenging. Hence, this paper proposes a Swiss round selection algorithm for multi-robot task allocation to address the challenges mentioned. Firstly, based on the shipping process of electronic commerce logistics and warehousing systems, the tasks are divided into packaging and sorting stages, and a grid model for the electronic commerce warehousing system is established. Secondly, by increasing the probabilities of crossover and mutation in the population and adopting a full crossover and full mutation approach, the search scope of the population is expanded. Then, a Swiss round selection mechanism with burst probability is proposed, which ensures the smooth inheritance of high-quality individuals while improving the diversity of the population. Finally, 12 comparative experiments are designed with different numbers of robots and tasks. The experimental results demonstrate that the Swiss round selection algorithm outperforms the genetic algorithm in terms of maximum task completion time and convergence time to reach the optimal value. Thus, the effectiveness of the Swiss round selection algorithm in solving the multi-robot task allocation problem is verified.

Strength Learning Particle Swarm Optimization for Multiobjective Multirobot Task Scheduling

Cooperative heterogeneous multirobot systems have attracted increasing attention in recent years. They use multiple heterogeneous robots to execute complex tasks in a coordinated way. The allocation of heterogeneous robots to cooperative tasks is a significant and challenging optimization problem. However, little work has gone into scheduling large-scale cooperative tasks with precedence constraints and multiple conflicting optimization objectives. Existing methods are insufficient to address the issue. We propose a multiobjective model and develop strength learning particle swarm optimization (SLPSO) to optimize multiple objectives. In this article, the problem is converted into a two-step problem of task permutation construction and robot subset selection. In order to coordinate with the time-extended property of the problem, SLPSO utilizes a hybrid encode scheme: an element-based representation for task permutations and a binary representation for robot coalitions. A strength learning strategy with heuristic information guides particles to enhance their best-performing objectives for improving swarm convergence. In addition, an estimation-based local search is developed to improve spare solutions for enhancing swarm diversity, which determines the search direction by estimating fitness improvements. Experimental results on thirty problem instances are elaborated to demonstrate that the proposed SLPSO significantly outperforms the state-of-the-art algorithms in terms of inverted generational distance and hypervolume metrics. The proposed SLPSO can obtain a set of high-quality and diversified solutions.

남극 환경에서 다중 로봇 시스템을 위한 최근접 이웃 알고리즘 기반 효율적 다중 작업 스케줄링

This paper addresses the problem of multi-robot task scheduling in Antarctic environments. It is difficult to operate multiple robots in Antarctic environments due to the icy ground condition and the lack of powers. In our previous work, the multi-robot task scheduling in Antarctic environments has been solved using ant colony optimization. Although it worked successfully, it caused much computation time. This paper proposes an efficient multi-robot task scheduling approach using nearest neighbor algorithm in Antarctic environments. The proposed method was tested in both simulated environments and Antarctic environments. The proposed method showed better performance in terms of computation time and cost than ant colony optimization and genetic algorithm.

Multi-Robot Task Scheduling with Ant Colony Optimization in Antarctic Environments

This paper addresses the problem of multi-robot task scheduling in Antarctic environments. There are various algorithms for multi-robot task scheduling, but there is a risk in robot operation when applied in Antarctic environments. This paper proposes a practical multi-robot scheduling method using ant colony optimization in Antarctic environments. The proposed method was tested in both simulated and real Antarctic environments, and it was analyzed and compared with other existing algorithms. The improved performance of the proposed method was verified by finding more efficiently scheduled multiple paths with lower costs than the other algorithms.

Coupled task scheduling for heterogeneous multi-robot system of two robot types performing complex-schedule order fulfillment tasks

This paper addresses multi-robot task scheduling for two robot types arising from heterogeneous robotic order fulfillment systems. The heterogeneous multi-robot system comprises two types of robots with specialized and complementary capabilities to achieve long-cycle and multi-station order fulfillment tasks on a logistic network. This problem is extremely challenging because of innate complex-schedule constraints of tasks and coupled temporal–spatial relations between all robots. After set-theoretic and mixed integer linear programming problem formulations, we use coupled approach, instead of decoupled approaches to explore the synergy between heterogeneous robots, which is different from most existing similar works. To model the structural (complex-schedule) and quantitative (temporal–spatial) coupledness of robots’ time-extended task schedules, an edge-weighted and vertex-weighted block sequence graph is introduced. Based on this model, time-extended task scheduling is achieved using rank-minimal heuristic and genetic algorithm metaheuristic. Theoretically, this model is complete and non-redundant. Empirically, compared with decoupled approach, optimality and efficiency of the proposed methods are evaluated on designed instances. The results demonstrate that coupled methods can achieve near-optimal solutions with higher performance ratio than decoupled methods in moderate time. At the same time, coupled methods can leverage spatial and temporal properties of miscellaneous tasks, and balance instantaneous and time-extended decisions to achieve tight collective synergy in the long run.

Research on multi-robot scheduling algorithms based on machine vision

In the multi-robot system, how to achieve effective and reasonable task coordination between multi-robots is an important problem;, multi-robot task scheduling is the term used for the coordination of the key technologies. Therefore, in this paper we combined the pilot scheduling method with the following method and the behavior method of the robot based on task scheduling, and we then studied how to improve the traditional robot scheduling effect, which is a deep-learning algorithm that is applied to multi-robot scheduling to formulate an action selection strategy. We thus proved the effectiveness of this idea experimentally. Based on the above research foundation, this paper continues to build a simple simulation experiment platform, which simply sets up three obstacles and completes the task of robot scheduling on the platform.

Project-oriented task scheduling for mobile robot team

This paper presents a project-oriented framework for multi-robot task scheduling. An agent-based architecture is designed to schedule tasks where robots are considered as resources. The study focused on the problems when the number of available robots is less than the required number. In this case, the problem becomes a resource-constrained scheduling problem. Initially, tasks are scheduled by using Critical path method (CPM), resource leveling method is used to smooth the deviation between the resource requirements and available resource levels, and tasks are allocated to robots. As robots perform their tasks, a monitoring agent observes them and tasks are rescheduled if the difference between the planned and actual completion time of tasks exceeds a predefined threshold. Effectiveness of the proposed approach is shown by using a nine-task two-robot project simulation.

COMPLEXITY REDUCTION FOR OPTIMIZATION OF DETERMINISTIC TIMED PETRI-NET SCHEDULING BY TRUNCATION

Methods of applying Petri nets to model and analyze scheduling problems, with constraints such as precedence relationships and multiple resource allocation, have been available in the literature. Searching for an optimum schedule can be implemented by combining the branch-and-bound technique with the execution of the timed Petri net. The resulting complexity problem in a large Petri net is handled by a truncation technique such that the original large Petri net is divided into several smaller subnets. The complexity involved in the analysis of each subnet individually is greatly reduced. However, as illustrated in this paper, the schedules for the subnets obtained by treating them separately may not lead to an optimal overall schedule for the original Petri net. To circumvent this problem, algorithms are developed that can be used to search for a proper schedule for each subnet such that the combination of these schedules yields an overall optimum schedule for the original timed Petri net. These algorithms are based on the idea of Petri net execution and branch-and-bound with modification. Finally, the practical application of the timed Petri net truncation technique to scheduling problems in manufacturing systems is illustrated by an example of multirobot task scheduling.