Pseudo-Kinematic Trajectory Control and Planning of Tracked Vehicles

TL;DR

This work tackles the challenge of accurate navigation for tracked vehicles across soft and sloped terrains by combining a faithful distributed-parameter terramechanics simulator with a control-oriented pseudo-kinematic model and a Lyapunov-based tracking controller. It introduces ML-based estimators for lateral and longitudinal slip, links slip parameters to the dynamic model, and develops a spectrum of slippage-aware planning methods—from closed-form Dubins and clothoids to an optimization-based planner. The approach yields certifiable tracking guarantees and improved real-world performance compared with traditional unicycle controllers, demonstrated through extensive simulations and indoor experiments on MaxxII and LIMO platforms under flat and inclined conditions. The framework enables tractable, slippage-aware navigation and planning, with practical implications for precision agriculture and terrain-robust robotic operation, while also outlining pathways for outdoor validation and advanced perception-driven parameter estimation.

Abstract

Tracked vehicles distribute their weight continuously over a large surface area (the tracks). This distinctive feature makes them the preferred choice for vehicles required to traverse soft and uneven terrain. From a robotics perspective, however, this flexibility comes at a cost: the complexity of modelling the system and the resulting difficulty in designing theoretically sound navigation solutions. In this paper, we aim to bridge this gap by proposing a framework for the navigation of tracked vehicles, built upon three key pillars. The first pillar comprises two models: a simulation model and a control-oriented model. The simulation model captures the intricate terramechanics dynamics arising from soil-track interaction and is employed to develop faithful digital twins of the system across a wide range of operating conditions. The control-oriented model is pseudo-kinematic and mathematically tractable, enabling the design of efficient and theoretically robust control schemes. The second pillar is a Lyapunov-based feedback trajectory controller that provides certifiable tracking guarantees. The third pillar is a portfolio of motion planning solutions, each offering different complexity-accuracy trade-offs. The various components of the proposed approach are validated through an extensive set of simulation and experimental data.

Figures (24)

• Figure 1: (left) Picture of the MaxxII tracked vehicle maxxii_webpage and (right) of the LIMO robot limo_webpage used for the experiments.
• Figure 2: Top view of a differentially steered tracked mobile robot (left) and its unicycle approximation (right). Standard definitions for frames and variables are also provided for both models.
• Figure 3: Left track discretisation and vector-field of the shear velocity for a right turn.
• Figure 4: Side view of a differentially steered tracked mobile robot on a slope highlighting the standard definitions for frames and variables. $\mathcal{W}$ is the inertial frame, $\mathcal{B}$ is the body frame.
• Figure 5: Different projection directions for the ground reaction forces: (left) along the $z$-axis of the inertial frame, (middle) along the $z$-axis of the body frame, (right) along the terrain normal $\mathbf{n}_t$.