## 10 Conclusions and Future Directions
For instance, agricultural robots managing tasks like planting or weed removal would benefit from energy-efficient navigation strategies that maximize battery life across large fields. The exploration of unstructured terrains presents additional challenges and opportunities for DRL. Robots operating in forests, disaster sites, or underwater environments require policies capable of handling incomplete environmental data and complex dynamics. Exploration-based DRL approaches, which use curiosity-driven rewards, could enable robots to navigate unknown terrains effectively. For example, underwater robots conducting coral reef surveys could leverage these techniques to explore and document fragile ecosystems while avoiding obstacles. Deep reinforcement learning presents significant computational challenges when deployed on real-world robotic systems, particularly due to the high-dimensional sensory inputs required for robust decision-making. LiDAR and vision-based sensing are widely used in mobile robot path planning, but processing these inputs in real-time demands specialized hardware configurations. [PERSON] et al. [91] highlighted that multi-robot systems relying on DRL for task allocation face severe computational bottlenecks, particularly when cloud resources are unavailable. Their study demonstrated that distributing computation across local robotic clusters improves efficiency but introduces coordination challenges. Similarly, [PERSON] et al. [92] explored Hyperdimensional Computing (HDC) as a lightweight alternative to deep learning for sensorimotor control, achieving a 14.2\(\times\) improvement in computational efficiency while maintaining comparable accuracy. For autonomous navigation, high-dimensional image data processing is another major concern. [PERSON] and [PERSON] [93] proposed a state abstraction technique to transform raw vision data into compact representations, significantly reducing the computational overhead of DRL policies running on resource-constrained devices. Additionally, [PERSON] et al. [94] introduced RMBench, a benchmarking framework for DRL-based robotic manipulation, revealing that Soft Actor-Critic (SAC) achieves the best trade-off between computational cost and task performance. Another critical challenge is balancing computational efficiency with training stability. [PERSON] et al. [16] proposed a deep stochastic Koopman operator (DeSKO) to model complex robot dynamics, improving decision-making efficiency while mitigating the impact of computational constraints. Their approach demonstrated robust performance in real-world soft robotic systems. Given these considerations, future research should focus on optimizing DRL architectures for edge computing and developing hybrid approaches that integrate learning-based methods with classical control techniques to alleviate hardware limitations. Finally, advancements in integrating vision and multimodal sensory systems could significantly enhance DRL's robustness in real-world applications. Combining inputs from cameras, LiDAR, and other sensors could enable robots to build richer environmental models. Autonomous drones delivering packages in urban areas, for instance, could utilize multimodal inputs for precise obstacle detection and landing. As DRL continues to evolve, its potential to generalize across unseen scenarios will become increasingly important. Approaches like meta-reinforcement learning and few-shot learning could enable robots to adapt rapidly to new tasks or environments with minimal additional training. For example, autonomous robots in industrial settings could quickly learn to navigate newly reconfigured layouts or operate in entirely different facilities. By addressing these challenges and directions, DRL could become a cornerstone of mobile robot navigation, paving the way for its widespread deployment in diverse applications. These advancements will not only improve the adaptability and efficiency of robots but also enhance their safety and reliability, enabling them to meet the demands of real-world scenarios effectively.