Unstructured environment Autonomous navigation is a relatively new field of high priority research in robotics due to the wide range of applications it can be applied to disaster response, exploration of other planets, farming, and military applications. In contrast to structured terrains, unstructured ones are distinguished by irregular surfaces, unpredictable obstacles and changing environmental conditions that significantly make movement of a robot confident. In this review paper, a general discussion has been made on some of the key aspects of autonomous navigation including path planning approaches, real-time obstacle avoidance, trajectory optimization and AI-driven decision-making approaches. Classical methods of route generation include sampling-based algorithms such as sampling-based planning (RRT, PRM) and heuristic search algorithms (A*), but are insufficient in the highly dynamic world. To overcome these challenges, real-time forecasting of the impediments and path optimization with models such as the Model Predictive Control (MPC) has gained immense popularity in the provision of efficient and safe navigation. Furthermore, machine learning with artificial intelligence, particularly, deep reinforcement learning and sensor fusion algorithms has significantly improved adaptability and perception features of robots in dynamic settings. Despite these developments, there are still certain problems like the restriction of computational capabilities and sensor error, sim-to-real transfer error and safety guarantee in dynamic environments. Other trends outlined in this review are lifelong learning, work with multiple robots, lightweight AI models and bio-inspired navigation systems. Overall, the paper highlights the change in classical rule-based systems of navigation to intelligent, data-driven, and adaptive robotic systems that potentially could be applied to the most unstructured and uncertain environments.
