# Rough-Terrain Path Planning Based on Deep Reinforcement Learning
## 1 Introduction  
With the continuous development of artificial intelligence [1, 2, 3, 4, 5, 6], autonomous driving has become the future trend. Path planning, as the key to autonomous driving navigation, is an extremely important research area in industry and academia. Traditional path-planning algorithms include graph search-based planning and sampling-based planning. Graph search-based algorithms include Dijkstra [7], A*[8], and their improved algorithms [9, 10]. Sampling-based algorithms include Rapidly-Exploring Random Trees (RRTs) [11, 12, 13], the Probabilistic Roadmap Method (PRM) [14], etc. In the current era, autonomous driving faces increasingly complex and diverse scenarios. Traditional path-planning algorithms, such as graph-based or sampling-based approaches, are effective in structured environments but often fail to adequately address new challenges arising from high-dimensional, rugged, and dynamic terrains. Issues such as energy consumption optimization, adaptability to complex terrain features, and computational efficiency become critical. The Ant Colony Algorithm (ACO) [15, 16] is a typical three-dimensional path-planning method, and its pheromone with certain memory function is a significant advantage, but the three-dimensional path planning of self-driving cars is more likely to encounter uneven, multi-peak and valley, multi-obstruction geographic environment conditions, and it is not able to completely solve the three-dimensional-environment path-planning problem. In addition, intelligent optimization algorithms such as the Genetic Algorithm (GA) [17] and Particle Swarm Algorithm (PSO) [18, 19] have been used in rough-terrain path planning. But to adapt to rugged terrain, additional terrain-related costs need to be added to the fitting function,which makes the search multi-objective and more complex, slowing down convergence. Moreover, the GA and PSO usually incorporate multiple features of the terrain into the cost function, which may lead to a local optimal solution problem due to rough terrain and complex search space. Regarding rugged-terrain path planning, most studies have focused on obstacle avoidance and safety to ensure that the vehicle does not skid, roll over, etc. For example, considering roll dynamics and actuator dynamics, some scholars have proposed an observer-based vehicle control scheme [20]. [PERSON] et al. considered the risk of cross-country terrains and weighed the safety and efficiency of driving to propose a three-dimensional path-planning algorithm based on a kind of potential field-based RRT* algorithm (PF-RRT*) [21], which improves the trajectory quality. However, these methods ignore the global characteristics of paths. These studies on rough-path-planning methods for ground vehicles have either focused on accessibility and traveling efficiency or on real-time, localized path planning. Fewer path-planning methods study the tradeoff between the traveling distance and energy consumption under rough road conditions. The global path planning for rough terrain pays more attention to the energy consumption. For example, [PERSON] et al. proposed a three-phase global-path-planning algorithm for self-driving vehicles on off-road terrain [22]. [PERSON] et al. proposed a global path planner that considers the energy consumption of variable-terrain turns [23], which improves the energy prediction by increasing the turn energy cost with better performance. [PERSON] et al. proposed energy-efficient path planning for self-driving ground vehicles based on Ackermann steering based on the A* search algorithm [24]. [PERSON] and [PERSON] proposed a path-planning method for wheeled mobile robots in rough-terrain environments, which mainly consists of a combination of the A* algorithm and the artificial potential field method [25]. A* can handle different slope levels by adding slope-dependent costs or constraints, but this requires the careful tuning of the heuristic and cost functions.