Publication

Abstract : In this paper, we explore the multifaceted capabilities of route-finding algorithms and their role in delivering dynamic paths for diverse navigation scenarios. In off-road route finding, the shortest path is not always the best; the route's smoothness must also be considered. Present methodologies like A* have notable limitations, such as difficulty adapting to complex terrains and producing rugged routes. This limitation hampers performance in critical scenarios, such as in emergencies like landslides or earthquakes, where off-road exploration is crucial. This paper proposes 3 methodologies that have been fine-tuned with optimal hyperparameters for optimal results using gradient descent. Two of these methodologies, Simulated Annealing and Ant Colony Optimization, overcome some limitations of A* but not all. However, Q-Learning significantly overcomes the limitations and saves travel time by providing a route with 70% fewer undulations and a 16% reduction in route length compared to A*. Compared to other implementations, the Q-Learning implementation proposed in this research not only focuses on minimizing path length but also minimizes route undulations, providing a dual objective approach that is well suited to real-world scenarios. Unlike prior implementations, which focus on a single objective, such as path length or obstacle avoidance, this work leverages a reward function that penalizes elevation variance while rewarding shorter routes, resulting in smoother, more easily traversable paths. Thus, Q-Learning overcomes the cons of the present methodologies and can form synergistic combinations, enhancing the overall performance of off-road space searching systems and accommodating the various challenges and complexities inherent in the targeted applications. The dataset used includes features such as latitude, longitude, and elevation. The strategic application of heuristics enables the swift evaluation of multiple paths, facilitating the selection of optimal routes in real-time applications. By combining various heuristics, the development of off-road path identification systems capable of discerning optimal paths across varied terrains becomes feasible.