Amphibious Spherical Robots (ASRs) use an electric field to communicate and collaborate effectively in a turbid water of confined spaces where other mode communication modalities failed. This paper proposes an embedded architecture formation strategy for a group of turtle-inspired amphibious robots to maintain a long distance-parameterized path based on dynamic visual servoing. Inspired by this biological phenomenon, we design an artificial multi-robot cooperative mode and explore an electronic communication and collaborate devices, the control method is based in particular on underwater environment and also conduct a detailed analysis of control motion module. The objectives of control strategies are divided into four categories: The first strategy is that the leader robot controls the action of the overall robots to maintain collaborate together during motion along a desired geometric path and to follow a timing law that the communication efficiency and the arrival times to assigned sites. Furthermore, we design an adaptive visual servoing controller for trajectory tracking task, taking into account system dynamics with environment interactions. After that, the third strategy is a centralized optimization algorithm for the redistribution of target mission changes. Finally, this paper also proposes a new method of control strategies in order to guarantee that each robot in the team moves together according to the preset target toward its location in the group formation based on communication and stability modules.

This study centers on leveraging smoke detection for preemptive forest smoke detection. Owing to the inherent ambiguity and uncertainty in smoke characteristics, existing smoke detection algorithms suffer from reduced detection accuracy, elevated false alarm rates, and occurrences of omissions. To resolve these issues, this paper employs an efficient YOLOv8 network and integrates three novel detection modules for enhancement. These modules comprise the edge feature enhancement module, designed to identify smoke ambiguity features, alongside the multi-feature extraction module and the global feature enhancement module, targeting the detection of smoke uncertainty features. These modifications improve the accuracy of smoke area identification while notably lowering the rate of false alarms and omission phenomenon occurrences. Meanwhile, a large forest smoke dataset is created in this paper, which includes not only smoke images with normal forest backgrounds but also a considerable quantity of smoke images with complex backgrounds to enhance the algorithm’s robustness. The proposed algorithm in this paper achieves an AP of 79.1%, 79.2%, and 93.8% for the self-made dataset, XJTU-RS, and USTC-RF, respectively. These results surpass those obtained by the current state-of-the-art target detection-based and neural network-based improved smoke detection algorithms.