## 1 Introduction  
Mobile robots, which can move independently, are of utmost importance in robotics. The intricate systems of mobile robots are equipped with sensors to detect the surroundings of the systems, a control system to plan the movements, and the ability to execute these systems. Various types of mobile robots exist, including humanoid robots [1], unmanned vehicles [2], entertainment companions [3], and drones [4]. What sets mobile robots apart is their capacity to make decisions based on their perception of their environment. To navigate unfamiliar surroundings, they require a robust cognitive system to process information from diverse sources and adapt to changing circumstances [5]. Mobile robots require a steady flow of input data, decoding methods, and action plans to respond to an ever-changing environment effectively. They can be classified into different categories based on their design, operation setting, and mode of operation. The most prevalent classification is autonomous robots, which can navigate unrestricted environments without physical or electromechanical guidance devices [6]. Ordinary differential equations frequently serve as models for neurons. Numerous mathematical descriptions of spiking neural models can be found in the literature, including the Hodgkin-Huxley model [7], the Integrate-and-Fire (IF) model [8], and the Izhikevich model [9]. The present study employs the Izhikevich model [10], which combines the biological plausibility of Hodgkin-Huxley dynamics with the computational efficiency of the IF neuron. In SNNs, the learning process is predicated on alterations in the strength of synapses between neurons, similar to biological neural networks. If a presynaptic neuron fires before a postsynaptic neuron, the synaptic strength is enhanced; conversely, if the order is reversed, a decrease occurs. The timing of neuronal impulses is crucial in the learning process and is called STDP. The study utilizes the STDP algorithm modulated by dopamine as a learning algorithm [11]. Mobile robots rely significantly on sensor input to gather information about their surroundings, enabling them to execute actions like moving, stopping, or rotating. These sensors encompass ultrasonic, laser, torque, and vision sensors, transmitting data to the robot's controller. Research conducted by [PERSON] and [PERSON] (2015) and [PERSON] (2017) has put forward algorithms to interpret this incoming data, typically transmitted as reflected light beams or sound signals, thus aiding the robot in gaining insights about its environment [12]. Nevertheless, object signal obstructions can lead to occasional inaccuracies in the data these sensors receive [13]. Several studies have investigated the use of SNNs in designing robot control systems. For example, [PERSON] and [PERSON] [14] analyzed using SNNs inspired by the brain's circuitry for robot behavioral learning. They found that teaching the spiking architecture of these circuits can lead to successful target attraction. Similarly, [PERSON] et al. [15] used modified neuron models to create an autonomous learning paradigm for robots, allowing them to explore complicated settings independently. [PERSON] et al. [16] developed a mobile robot control system based on SNNs and a reward-modulated algorithm, enabling multitask autonomous learning. [PERSON], Z. has come up with a new way to make decisions for swarm robots. They use a system called ESB-MADDPG, which combines an expert system with deep reinforcement learning. This approach makes training faster and allows the robots to use pre-trained paths without needing to be retrained often [17]. On the other hand, [PERSON] et al. [18] investigated how the spatial features of STDP (spike-timing-dependent plasticity) can facilitate associative learning in small SNNs. [PERSON] et al. [19] proposed Hough Transform-based SNNs for target recognition in non-visual sensor (NVS) fed asynchronous event streams, demonstrating their potential for identifying targets in real-world settings. [PERSON] et al.