Deep reinforcement learning has exhibited exceptional capabilities in a variety of sequential decision-making problems, providing a standardized learning paradigm for the development of intelligent multi-robot systems. Nevertheless, when confronted with dynamic and unstructured environments, the security of decision-making strategies encounters serious challenges. The absence of security will leave multi-robot susceptible to unknown risks and potential physical damage. To tackle the safety challenges in autonomous decision-making of multi-robot systems, this manuscripts concentrates on a uniformly ultimately bounded constrained hierarchical safety reinforcement learning strategy (UBSRL). Initially, the approach innovatively proposes an event-triggered hierarchical safety reinforcement learning framework based on the constrained Markov decision process. The integrated framework achieves a harmonious advancement in both decision-making security and efficiency, facilitated by the seamless collaboration between the upper-tier evolutionary network and the lower-tier restoration network. Subsequently, by incorporating supplementary Lyapunov safety cost networks, a comprehensive strategy optimization mechanism that includes multiple safety cost constraints is devised, and the Lagrange multiplier principle is employed to address the challenge of identifying the optimal strategy. Finally, leveraging the principles of uniformly ultimate boundedness, the stability of the autonomous decision-making system is scrutinized. This analysis reveals that the action trajectories of multiple robots can be reverted to a safe space within a finite time frame from any perilous state, thereby theoretically substantiating the efficacy of the safety constraints embedded within the proposed strategy. Subsequent to exhaustive training and meticulous evaluation within a multitude of standardized scenarios, the outcomes indicate that the UBSRL strategy can effectively restricts the safety indicators to remain below the threshold, markedly enhancing the stability and task completion rate of the motion strategy.

In response to problems concerning the low autonomous localization accuracy of mobile robots in unknown environments and large cumulative errors due to long time running, a spatial location representation method incorporating boundary information (SLRB) is proposed, inspired by the mammalian spatial cognitive mechanism. In modeling the firing characteristics of boundary cells to environmental boundary information, we construct vector relationships between the mobile robot and environmental boundaries with direction-aware information and distance-aware information. The self-motion information (direction and velocity) is used as the input to the lateral anti-Hebbian network (LAHN) to generate grid cells. In addition, the boundary cell response values are used to update the grid cell distribution law and to suppress the error response of the place cells, thus reducing the localization error of the mobile robot. Meanwhile, when the mobile robot reaches the boundary cell excitation zone, the activated boundary cells are used to correct the accumulated errors that occur due to long running times, which thus improves the localization accuracy of the system. The main contributions of this paper are as follows: 1. We propose a novel method for constructing boundary cell models. 2. An approach is presented that maps the response values of boundary cells to the input layer of LAHN (Location-Adaptive Hierarchical Network), where grid cells are generated through LAHN learning rules, and the distribution pattern of grid cells is adjusted using the response values of boundary cells. 3. We correct the cumulative error caused by long-term operation of place cells through the activation of boundary cells, ensuring that only one place cell responds to the current location at each individual moment, thereby improving the positioning accuracy of the system.