Hybrid Trajectory Optimization for Autonomous Terrain Traversal of Articulated Tracked Robots

TL;DR

This paper introduces a hybrid trajectory optimization method for autonomous terrain traversal of articulated tracked robots, combining real-time planning, mode switching, and multi-objective optimization to improve efficiency, stability, and smoothness in complex environments.

Contribution

The paper presents a novel hybrid trajectory optimization approach that integrates mode switching, reduced configuration space, and multi-objective cost functions for autonomous terrain traversal.

Findings

Validated in simulation and real-world scenarios with the Searcher robot

Outperforms expert control and existing methods in time and energy efficiency

Demonstrates improved stability and motion smoothness

Abstract

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.