This paper introduces an innovative hierarchical model with a leader to facilitate navigation of a swarm of underwater robots, inspired by the collective behaviours observed in natural animal groups, such as schools of fish and flocks of birds. In this model, the leader robot carries a comprehensive set of navigation information, while the other robots are stratified based on the relative distances between them and follow the leader during the navigation process. The model incorporates repulsion and attraction forces to enable clustering and collision avoidance among the robots. Initial simulation results confirm the scalability of the model and its robustness against noise, while further simulations demonstrate that the proposed layered strategy effectively manages polyline and circular trajectory navigation and guides the robotic group around obstacles while maintaining the group’s structural stability and efficiency. In addition, the decentralised nature of the model and its minimal communication requirements make it highly suitable for practical underwater tasks. This research not only provides an effective and deployable solution for the cooperative synchronisation of underwater robots but also offers valuable insights for understanding and designing other types of robotic swarm systems.

In view of the fragile ecological health of the Futian mangrove wetland ecosystem, an index system for evaluating ecosystem health of the Futian mangrove wetland was established based on the ‘pressure-state-response’ (PSR) model. The ecosystem health evaluation of the Futian mangrove wetland was carried out based on three aspects: pressure subsystem, state subsystem and response subsystem. Analytical hierarchy process (AHP) was used to determine the index weight. The index system is established and evaluated by the comprehensive index method. The results showed that the ecosystem health index of the Futian mangrove wetland was 0.25262. According to ecosystem health assessment criteria, it falls into the ‘poor’ range. Based on our assessment, we consider that the structure and function of the Futian mangrove wetland ecosystem are degraded, that there has been a decline in biodiversity and that the prevailing environmental problems are serious. The results of this study can provide an effective scientific basis for the management of the Futian mangrove wetland ecosystem.

In this paper, the position–force–based approximate optimal control method is developed for reconfigurable robots using zero-sum game strategy. By utilizing the Newton–Euler iteration technique, the robotic system’s dynamic model is formulated and the state space equation is derived. According to adaptive dynamic programming (ADP) and neural network algorithm, the trajectory tracking control problem is transformed into a zero-sum game-based optimal control issue. The optimal control policy and worst disturbance policy are obtained by Hamilton–Jacobi–Issacs (HJI) function, respectively. Unlike the conventional learning–based robotic control method, the proposed zero-sum game-based method no need extra sub-controller that can reduce the computational burden. The reconfigurable robot system’s tracking error is uniformly ultimately bounded by the Lyapunov theorem. Finally, simulation experiments demonstrate the advantages of the proposed method.

For biological groups, the behaviors of individuals will have an impact on the alignment efficiency of the collective movement. Motivated by Vicsek’s pioneering research on self-organized particles and other related works about flocking behaviors, we propose two mathematical models based on the local information of individuals to include more realistic details in the interaction mechanism between individuals and the rest of the group during the flocking process. The local information of the individual refers to the local consistency, representing the degree of alignment with its neighbors. These two models are the self-introspection model, where the process of orientation adjustment of one individual is ruled by the degree of local consistency with the neighborhood, and the credit evaluation model, where the average orientation of the neighborhoods is weighed using the local consistency of the interacting individuals. Different metrics are calculated to analyze the effects of the model parameters and flocking parameters on groups. Simulation calculations indicate that the two improved models have certain advantages in terms of alignment efficiency for the group. Finally, the optimal model parameters are determined, and the effects of random noise on groups with a single behavior and mixed behaviors are analyzed. The results confirm that individuals with mixed behaviors still possess robustness against noise. This research would contribute to the further interdisciplinary cooperation that involves biology, ethology, and multi-agent complex systems.

In order to effectively cope with the pain points of stair cleaning and complex terrain movement in the home environment, this paper designs an intelligent stair climbing robot based on ANSYS analysis. The device integrates the rack and pinion adaptive climbing mechanism, the crawler high-pressure cleaning system, the dual robotic arm corner cleaning unit and the McNamee wheel omnidirectional moving chassis, and has compound functions such as ground sweeping, step climbing, corner cleaning and bulky garbage grabbing. The static simulation of the core components such as the stair climbing device, the robotic arm, the mobile wheel and the base station by ANSYS finite element analysis shows that the maximum equivalent stress of each structure is much lower than the allowable stress of the material, and has good mechanical properties and working reliability. This design provides a theoretical basis and engineering reference for the innovation of mechanical structure and performance optimization of intelligent cleaning equipment.