# Adaptive Navigation in Collaborative Robots: A Reinforcement Learning and Sensor Fusion Approach
## 2 Literature Review
Classical techniques [21], such as cell decomposition, roadmap methods, and artificial potential fields, combined with the use of reactive strategies that apply artificial intelligence methods, such as Genetic Algorithms and fuzzy logic, for real-time adaptability. There area lot of works available that have compared methods, showing strengths and weaknesses in different scenarios and accentuating the trend toward hybrid techniques in which techniques are combined for improved performance. Also, based on the detected research gaps, these works have identified future directions for further enhancement of path-planning strategies to address the challenges generated by dynamic and complex environments. A new system [22] that fuses 2D LiDAR data with RGB-D cameras can be used, leading to vastly superior mobile robot navigation in dynamic environments. Already, Bayesian estimation for multi-sensor data fusion is applied in this approach to further enhance the two-dimensional grid maps' accuracy and enable the discovery of obstacles below the scanning height of the LiDAR. The dynamic window method integrates a global path of planning and a local path of planning with an improved algorithm for effective global path planning and efficient local path planning, respectively. The experimental results demonstrated that this system could navigate static and dynamic environments, showing one application of a real robot system. Moreover, the use of deep learning architectures in path planning for mobile robots has been explored, showing promising results in enhancing navigation and obstacle avoidance capabilities [23]. Among various models, the hybrid CNN-LSTM model exhibited superior performance, indicating the potential of deep learning in improving the efficiency and adaptability of path planning compared to traditional algorithms. A hybrid sampling-based RRT * algorithm was introduced to address the limitations of conventional path-planning methods [24]. By integrating uniform and non-uniform sampling strategies, this algorithm demonstrated improved navigation efficiency in complex environments. The findings suggest that hybrid approaches can significantly contribute to autonomous navigation performance. Another complex way to develop an autonomous navigation system [25] for mobile robots is by combining DRL with sensor fusion techniques, particularly using LiDAR and depth camera data. This leverages the heuristic global navigation function to select optimal points of navigation while employing the Soft Actor-Critic algorithm for local navigation, which in turn ensures that the robot can pave its way into the unknown environment. It underlines the system's capacity to move effectively around obstacles and respond to dynamic situations with superior performance in experimental trials compared to those under traditional methods, especially in the aspects of travel time and distance. This consequently implies that a quantifiable performance increase in mobile robot navigation within complex environments has been achieved using the approach taken here. Neuromorphic computing and spiking neural networks (SNNs) have the potential to greatly enhance robotic navigation and obstacle avoidance algorithms by mimicking the biological information processing in the human brain. [PERSON] discusses [26] how event-driven neuromorphic sensors such as Dynamic Vision Sensors (DVSs) capture relevant changes in a scene, ceasing to be clock-driven and thus reducing irrelevant data to be processed. It minimizes duplicate information, increases the temporal resolution of the observation, and allows for neural-semantic-motor integration, which is essential to traverse unstructured spaces. [PERSON] [27] in turn highlights the importance of SNNs in the context of autonomous driving, where their capacity for temporal processing allows for real-time decision-making with a high degree of energy efficiency, especially when implemented on neuromorphic hardware.