Robots to navigate hiking trails

If you’ve ever gone hiking, you know trails can be challenging and unpredictable. A path that was clear last week might be blocked today by a fallen tree. Poor maintenance, exposed roots, loose rocks, and uneven ground further complicate the terrain, making trails difficult for a robot to navigate autonomously. After a storm, puddles can form, mud can shift, and erosion can reshape the landscape. This was the fundamental challenge in our work: how can a robot perceive, plan, and adapt in real time to safely navigate hiking trails?

Autonomous trail navigation is not just a fun robotics problem; it has potential for real-world impact. In the United States alone, there are over 193,500 miles of trails on federal lands, with many more managed by state and local agencies. Millions of people hike these trails every year.

Robots capable of navigating trails could help with maintenance, search and rescue, and environmental monitoring. Driving off-trail introduces even more uncertainty. From an environmental perspective, leaving the trail can damage vegetation, accelerate erosion, and disturb wildlife. Still, there are moments when staying strictly on the trail is unsafe or impossible. So our question became: how can a robot get from A to B while staying on the trail when possible, and intelligently leaving it when necessary for safety?

Our main contribution is handling uncertainty by combining two complementary ways of understanding and mapping the environment: Geometry and Semantic perception. Geometry is great for detecting big hazards, but it struggles with small obstacles. Semantic perception can visually distinguish these cases, especially the trail the robot is meant to follow. By fusing geometry and semantics, we obtain a far more robust representation of what is safe to drive on.

We built a hiking trail dataset, labeling images into eight terrain classes, and trained a semantic segmentation model. These semantic labels were projected into 3D using depth and combined with the LiDAR based geometric terrain analysis map. Using a dual k-d tree structure, we fuse everything into a single traversability map, where each point in space has a cost representing how safe it is to traverse, prioritizing trail terrain.

The next step is deciding where the robot should go next, which we address using a hierarchical planning approach. At the global level, the planner operates in a receding-horizon manner, continuously replanning as the robot moves. We developed a custom RRT* that biases its search toward areas with higher traversability probability. A local planner then handles motion between waypoints using precomputed arc trajectories and collision avoidance.

In practice, this makes the robot prefer staying on the trail, but not stubborn. If the trail ahead is blocked by a hazard, it can temporarily route through grass or another safe area around the trail and then rejoin it once conditions improve. We tested our system at the West Virginia University Core Arboretum using a Clearpath Husky robot. Overall, this work shows that robots do not need perfectly paved roads to navigate effectively. With the right combination of perception and planning, they can handle winding, messy, and unstructured hiking trails.