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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....
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... parameter values of ACO in the proposed method were as follows: í µí±í µí±í µí±¡í µí± í µ 40, í µí±í µí±¡í µí±í µí±í µí±í µí±¡í µí±í µí±í µí± í µ 20, í µí»¼ í µ 2, í µí»½ í µ í µí±, í µí¼ í µ 0.1, í µí¼ í µ 0.05, í µí± § 0.5, í µí±í µí±í µí±¡í µí± is the number of ants, í µí±í µí±¡í µí±í µí±í µí±í µí±¡í µí±í µí±í µí± is the number of iterations. Figures 5-7 are the results of NN, GA, and the proposed method in the simulation environment, and Table 1 is a comparison table of the elevation distance. Figure 8 is a comparison chart of the elevation distance. ...Context 2
... 8 is a comparison chart of the elevation distance. Regarding the results in simulation environments, as shown in Table 1, NN and the proposed method showed similar results for 20 nodes. However, as the number of nodes increased, the proposed method showed a shorter elevation distance. ...Similar publications
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... To address this, a probabilistic model using a Gaussian distribution is applied to crevasse data [26], considering the variability in crevasse positions and sizes. Previous studies have proposed methods to minimize steep slopes while visiting destinations, but they were limited in fully reflecting the Antarctic environment [27,28]. Therefore, this study improves scheduling by integrating crevasse data with the elevation information used in previous research to find safer routes. ...
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.
... Kim and Lee (2023) [17] propose a practical method for multi-robot task scheduling in Antarctic environments using ant colony optimization. The method was tested in simulated and real Antarctic environments and showed that the proposed method outperforms other algorithms by finding more efficient multiple paths with lower costs. ...
In smart factories, several mobile and autonomous robots are being utilized in warehouses to reduce overhead and operating costs. In this context, this paper presents a consensus-based fault-resilient intelligent mechanism called Consensual Fault-Resilient Behavior (CFRB). The proposed approach is based on three hierarchical plans: imposition, negotiation, and consensus. Fault resilience is achieved using the collective behavior of a multi-robot system that applies ternary decisions based on these plans. The difference between this paper and our previous work is on the consensual level. As it is suitable for the analysis and design of coordinated behavior between autonomous robots, the consensus plan is restructured and enhanced. The proposed approach is tested and evaluated in a virtual warehouse based on a real environment. In addition, it is compared with other current approaches, and the results are presented, demonstrating its efficiency.
The current research intents on enhancing the service ability of mobile robot by cooperative path planning. The strategy is developed by fusion of sine cosine algorithm and particle swarm optimization approach for the transition of service robot in complex environment. To ensure the successful execution of the intended task it is essential to have a faultless and collision-less path for the mobile robot. This supposition can be achieved by producing an intelligent fault-managed approach. The proposed paper addresses the object transportation by a pair of robots from source to destination, this task can be accomplished in three step, such as fault identification, fault resolution using robot replacement and computation of a collision-less path. At each step of transition, path planning is carried out to reach the target location. Sine cosine algorithm improves the exploration capability of the robots in the multi robot environment. Particle swarm optimization being the simplest technique to exercise the path planning problem produces an optimal global position for each particle along each dimension. The fusion of both provides a balance exploration-exploitation ability of the mobile robot. Fault identification overcomes the faulty transition and unsuccessful transition of the robot by detecting the fault of any robot through its non-responding time. K- nearest neighbor approach identifies the nearest working robot to replace the faulty on. The algorithm has been exercised in C language to showcase its capability in terms of execution time, path traveled, path deviated etc. The comparative analysis proofs the supremacy of the proposed algorithm in terms of several metrics such as path planning, cooperation, fault management, etc.
