Task-Space Constrained Stochastic Trajectory Optimization for Time-Optimal Forestry Crane Motion Planning

2026-07-20

AI Summary

This study addresses the suboptimal motion planning of forestry cranes caused by predefined end-effector joint configurations that restrict the utilization of redundant degrees of freedom. To overcome this limitation, the authors propose a task-space-constrained variational policy stochastic trajectory optimization method (TSC-VP-STO), which jointly optimizes motion trajectories and redundant end-effector degrees of freedom through configuration space decomposition and reachability constraint modeling. By eliminating fixed terminal constraints in joint space, the approach enables environment-adaptive co-optimization of trajectories and hydraulic resource allocation. Experimental validation on a real forestry crane demonstrates successful execution of full log-loading cycles, achieving a 12–15% reduction in average trajectory duration and significantly improved hydraulic pump flow utilization.

Abstract

Efficient, collision-free, and time-optimal motion planning is a fundamental requirement for autonomous forestry cranes operating under hydraulic pump-flow constraints. The Via-Point-based Stochastic Trajectory Optimization (VP-STO) algorithm has demonstrated near-time-optimal hybrid motion planning in this domain, but requires a fixed terminal joint configuration specified prior to optimization. For kinematically redundant manipulators such as forestry cranes, this pre-commitment to a single inverse kinematics solution restricts the planner's ability to exploit redundancy, particularly under the nonlinear, globally coupled pump-flow constraint where admissible joint velocities depend on their combined hydraulic demand. This paper presents TSC-VP-STO, a task-space-constrained extension of VP-STO that replaces the strict terminal joint-space constraint with a task-space constraint, jointly optimizing the trajectory and the redundant degrees of freedom of the terminal configuration. This enables the planner to adapt end configurations to the environment-dependent motion and hydraulic flow allocation, yielding more balanced pump utilization and shorter trajectory durations. We formalize the approach through a configuration space decomposition and derive a concrete reachability constraint for the forestry crane kinematics. Experimental evaluations across multiple planning targets and via-point configurations demonstrates a reduction on trajectory durations by 12-15% on average and improved pump-flow utilization compared to the baseline VP-STO. The practical applicability of TSC-VP-STO is validated through real-world deployment on a forestry crane, including a full log-loading cycle.

Problem

• motion planning
• kinematic redundancy
• hydraulic constraints
• time-optimal control
• task-space constraint

Innovation

• task-space constraint
• kinematic redundancy
• hydraulic pump-flow constraint
• stochastic trajectory optimization
• time-optimal motion planning