Hello,

Thanks for posting this question . We all know that deep learning models have 3 different layers - Input layer , Hidden layer and Output layer . Sensors are devices which collects info from surroundings , just like agents in AI . We can feed this information collected from sensors as input to the input layer . By using suitable model , the information should be processed in hidden layer resulting in the output. Hence , advanced deep learning models are recommended to be used in order to serve the purpose mentioned in the above question.

Hello Dr. Adnan,

While deep learning is an effective technology, it is insufficient to address the issue of "light sensing and guidance in robots" on its own. Deep learning is best suited for perception tasks such as detection or classification of light sources or the regions. However, to achieve actionable robotic behavior, we typically need to either modify the model's architecture for example, by adjusting the output layer to provide specific features or design a downstream algorithm that can interpret the model's output and translate it into control decisions. In essence, deep learning provides informative outputs, but for actual navigation or guidance, an additional logic or control module is required to process those outputs and generate the desired robotic response. You might consider hybrid approaches, such as: whether through multi-task learning (where a model simultaneously performs detection and guidance-related tasks), post-processing with guidance algorithms (to translate perception into motion), or reinforcement learning (where the robot learns optimal behavior from interaction with its environment using light-based rewards).

I hope this brief insight is helpful to your research.

Best regards,

Ahmed

Hello Farkad Adnan

Deep learning is brought together with robots through a seamless end-to-end pipeline for light sensing and guidance, involving perception, processing, and action. First, the raw light data captured by sensors-cameras, LiDARs, and infrared-these are then preprocessed using autoencoders to denoise these low-light inputs (for example, enhancing night vision). Then CNNs analyze the similarities in spatial patterns (shadows, intensity gradients, object edges) in real time for mapping static environments. In the case of dynamic conditions, RNNs or transformers handle temporal sequences (for example, flickering lights and moving obstacles) to project trajectories. Sensor fusion architectures (for example, multimodal transformers) combine LiDAR depths, visual feeds, and ambient lights into an equivalent 3D map, which mitigates ambiguities that come with single sensors. Reinforcement learning makes use of this fused data to generate navigation policies, rewarding actions that converge toward light cues (e.g. avoiding glare-following beacon signals). Transfer learning just fine-tunes the models towards the domain-specific task (for instance, a warehouse robot now has to adapt to the flickering fluorescent light), while online learning allows instantaneous adjustment to sudden illumination changes (like those during smoke operations in search/rescue bots). This closed-loop system enables robots to dynamically adjust perception and motion concerning efficiency and safety in individual light cases-from industrial automation to outdoor exploration.