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This paper is concerned with near-optimal source search problem using a multiagent system in cluttered indoor environments. The goal of the problem is to maximize the detection probability within the minimum search time. We propose a two-stage strategy to achieve this goal. In the first stage, a greedy approach is used to define a set of grid cells with the aim of maximizing the detection probability. In the second stage, an iterative branch-and-bound procedure is used to design the search paths of all agents so that all grid cells are visited by one agent and the largest search path among all agents is minimized. Simulation results show that the proposed search algorithm has better performance in terms of exploration time compared to other existing methods.

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Most existing methods for unsupervised domain adaptation (UDA) only involve two domains, i.e., source domain and the target domain. However, such trained adaptive models have poor performance when applied to a new domain without learning. Moreover, using UDA methods to adapt from the source domain to the new domains will lead to catastrophic forgetting of the previous target domain. To handle these issues, inspired by the ability to balance the maintenance of old knowledge and learning new knowledge of the human brain, in this article, we propose a new incremental learning framework for domain-incremental cases, which can harmonize the memorability and discriminability of the existing and the novel domains. By this means, the model can imitate the learning process of the human brain and, thus, improve its adaptability. To evaluate the effectiveness of the proposed methods, we conduct two groups of experiments, including virtual-to-real and diverse-weather cases. The experimental results demonstrate that our approach can avoid catastrophic forgetting, mitigate performance degradation in the previous domains, and improve the object detection accuracy of the novel target domain significantly.