  Autonomous terrain traversal of articulated tracked robots can reduce
operator cognitive load to enhance task efficiency and facilitate extensive
deployment. We present a novel hybrid trajectory optimization method aimed at
generating efficient, stable, and smooth traversal motions. To achieve this, we
develop a planar robot-terrain contact model and divide the robot's motion into
hybrid modes of driving and traversing. By using a generalized coordinate
description, the configuration space dimension is reduced, which facilitates
real-time planning. The hybrid trajectory optimization is transcribed into a
nonlinear programming problem and divided into subproblems to be solved in a
receding-horizon planning fashion. Mode switching is facilitated by associating
optimized motion durations with a predefined traversal sequence. A
multi-objective cost function is formulated to further improve the traversal
performance. Additionally, map sampling, terrain simplification, and tracking
controller modules are integrated into the autonomous terrain traversal system.
Our approach is validated in simulation and real-world scenarios with the
Searcher robotic platform. Comparative experiments with expert operator control
and state-of-the-art methods show advantages in terms of time and energy
efficiency, stability, and smoothness of motion.
