  Terrain traversability analysis is a fundamental issue to achieve the
autonomy of a robot at off-road environments. Geometry-based and
appearance-based methods have been studied in decades, while behavior-based
methods exploiting learning from demonstration (LfD) are new trends.
Behavior-based methods learn cost functions that guide trajectory planning in
compliance with experts' demonstrations, which can be more scalable to various
scenes and driving behaviors. This research proposes a method of off-road
traversability analysis and trajectory planning using Deep Maximum Entropy
Inverse Reinforcement Learning. To incorporate vehicle's kinematics while
solving the problem of exponential increase of state-space complexity, two
convolutional neural networks, i.e., RL ConvNet and Svf ConvNet, are developed
to encode kinematics into convolution kernels and achieve efficient forward
reinforcement learning. We conduct experiments in off-road environments. Scene
maps are generated using 3D LiDAR data, and expert demonstrations are either
the vehicle's real driving trajectories at the scene or synthesized ones to
represent specific behaviors such as crossing negative obstacles. Different
cost functions of traversability analysis are learned and tested at various
scenes of capability in guiding the trajectory planning of different behaviors.
We also demonstrate the performance and computation efficiency of the proposed
method.
