## 1 Introduction
[20] introduced a technique that uses photographs to provide environmental data for robot control, reducing dimensionality while maintaining performance. Also, SNNs have been explored in obstacle avoidance and navigation. [PERSON] et al. [21] presented an SNN-based algorithm for mobile robot obstacle avoidance, showing its effectiveness in navigating complex environments. [PERSON] et al. [22] used Deep Reinforcement Learning (DRL) methods to successfully teach a mobile robot to navigate crowded indoor environments. [PERSON] et al. [23] proposed an SNN-based algorithm for obstacle avoidance using ultrasonic sensors and demonstrated its success in avoiding obstacles in a simulated environment. These studies highlight the potential of SNNs and DRL methods in developing effective robot control systems for various tasks, including target attraction, navigation, and obstacle avoidance, in real-world settings. In the exploration of reinforcement learning (RL), an endeavor laden with challenges due to the necessity for numerous interactions with the environment, [PERSON] et al. address the challenges of real-world reinforcement learning (RL) tasks by proposing an innovative offline RL algorithm. This algorithm combines hindsight relabeling and supervised regression to predict actions autonomously, eliminating the need for oracle information. The authors recognize the expense and potential danger associated with real-world tasks, prompting the development of this solution [24]. In their pursuit of enhancing the sparsity of the standard broad learning system for pattern classification and regression, [PERSON], [PERSON] et al. introduce the smoothing group L1/2 regularized discriminative broad learning system. This system aims to improve recognition and generalization performance by incorporating the \(\varepsilon\)-dragging technique and group L1/2 regularization. To address the non-convex and non-smooth nature of the original regularization objective, the authors propose an effective smoothing technique. Simulation results validate the algorithm's success in redundancy control, highlighting its achievement in improving recognition and generalization [25]. Using Q-learning and deep Q-networks (DQNs) for robot control in unknown environments presents several noteworthy challenges. While effective in certain applications, both methods suffer from inefficiencies related to trial-and-error learning, resulting in significant time consumption. Achieving real-time control remains challenging, particularly for Q-learning, limiting its practical applicability in dynamic and rapidly changing environments. The complex control structures required by these methods can be intricate, posing difficulties in designing strategies that seamlessly guide robots through various dynamic scenarios. Additionally, extended convergence times and high computational demands constrain the practical implementation of DQNs, especially in resource-constrained settings. Furthermore, the limited adaptability of these methods to evolving environments and the dependence on extensive training data present significant hurdles. Addressing these gaps is crucial for enhancing the robustness and applicability of Q-learning and DQNs in robot control in unknown and dynamic environments [26]. SNN is a new approach that uses unique learning methods and characteristics to address these challenges. This proposed AMR learning and control system aims to assist robots in effectively navigating and operating within unfamiliar surroundings. It utilizes a customized SNN based on the biologically accurate Izhikevich neuron model. The SNN incorporates STDP and dopamine modulation features to enhance learning and adaptation. STDP adjusts neuron connections based on their spiking timing, while dopamine reinforcement encourages desired behaviors. These mechanisms improve the adaptability and the performance of the robot in new environments, enabling it to process sensory information and make decisions in a biologically realistic manner [27].