  In the context of ground robot navigation in unstructured hazardous
environments, the coupling of efficient path planning with an adequate
environment representation is a crucial topic in order to guarantee the robot
safety while ensuring the accomplishment of its mission. This paper discusses
the exploitation of an environment representation obtained via Gaussian process
regression (GPR) for smooth path planning using gradient descent B\'ezier curve
optimisation (BCO). A continuous differentiable GPR of the terrain
traversability and obstacle distance is used to plan paths with a weighted A*
discrete planner, a T-RRT sampling-based planner and BCO using A* or T-RRT
computed paths as prior. Numerical experiments in procedurally generated 2D
environments allowed to compare the paths planned by the described methods and
highlight the benefits of the joint use of the GPR continuous representation
and the BCO smooth path planning with these different priors.
