In real manufacturing environments, the number of automatic guided vehicles (AGV) is limited. Therefore, the scheduling problem that considers a limited number of AGVs is much nearer to real production and very important. In this paper, we studied the flexible job shop scheduling problem with a limited number of AGVs (FJSP-AGV) and propose an improved genetic algorithm (IGA) to minimize makespan. Compared with the classical genetic algorithm, a population diversity check method was specifically designed in IGA. To evaluate the effectiveness and efficiency of IGA, it was compared with the state-of-the-art algorithms for solving five sets of benchmark instances. Experimental results show that the proposed IGA outperforms the state-of-the-art algorithms. More importantly, the current best solutions of 34 benchmark instances of four data sets were updated.

In this article, an effective solution is presented for efficient robotic object search by leveraging metric-topological map. Based on the reference of objects, we construct a novel metric-topological map that is particularly suitable for object search, where the topological nodes represent the object-related locations in the environment. To effectively determine the creation of nodes on the map, a rapid adjustment method of the robot viewing angle is introduced. Compared with traditional maps, the map built in this article not only removes redundant nodes irrelevant to objects, but also facilitates the mobile robot to locate the target object. Besides, inspired by human search behavior, a search strategy is proposed by considering the tradeoff between the current position of the robot and the distance from the robot to the promising location of finding the target object, in order to further improve the search efficiency. The effectiveness and efficiency of our solution are verified through comparative experiments in both simulation and real-world environments. Results show that the presented approach always allows the mobile robot to find the target object efficiently and reliably while achieving humanlike search behavior.

This paper focuses on skeleton-based few-shot action recognition. Since skeleton is essentially a sparse representation of human action, the feature maps extracted from it, through a standard encoder network in the few-shot condition, may not be sufficiently discriminative for some action sequences that look partially similar to each other. To address this issue, we propose a self and mutual adaptive matching (SMAM) module to convert such feature maps into more discriminative feature vectors. Our method, named as SMAM-Net, first leverages both the temporal information associated with each individual skeleton joint and the spatial relationship among them for feature extraction. Then, the SMAM module adaptively measures the similarity between labeled and query samples and further carries out feature matching within the query set to distinguish similar skeletons of various action categories. Experimental results show that the SMAM-Net outperforms other baselines on the large-scale NTU RGB+D 120 dataset in the tasks of one-shot and five-shot action recognition. We also report our results on smaller datasets including NTU RGB+D 60, SYSU and PKU-MMD to demonstrate that our method is reliable and generalises well on different datasets. Codes and the pretrained SMAM-Net will be made publicly available.