# Real-Time Motion Planning of a Hydraulic Excavator using Trajectory Optimization and Model Predictive Control

arXiv: https://arxiv.org/abs/2107.02366 · HTML: https://mlsanity.com/paper/2107.02366/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2021.

## Built on by (most cited first, 5 of 5)

- [Reinforcement Learning Control for Autonomous Hydraulic Material Handling Machines with Underactuated Tools](https://mlsanity.com/paper/2410.05093/) (2024)
- [Progressive-Resolution Policy Distillation: Leveraging Coarse-Resolution Simulations for Time-Efficient Fine-Resolution Policy Learning](https://mlsanity.com/paper/2412.07477/) (2024)
- [3D Operation of Autonomous Excavator based on Reinforcement Learning through Independent Reward for Individual Joints](https://mlsanity.com/paper/2406.19848/) (2024)
- [Size Doesn't Matter: Material-State Reinforcement Learning for Excavator Transferable Soil Manipulation](https://mlsanity.com/paper/2609.12677/) (2026)
- [IL-ACT: Imitation Learning with Adaptive Cartesian Tracking Control for a 30-ton Excavator](https://mlsanity.com/paper/2609.16696/) (2026)

## Abstract (arXiv)

Automation of excavation tasks requires real-time trajectory planning satisfying various constraints. To guarantee both constraint feasibility and real-time trajectory re-plannability, we present an integrated framework for real-time optimization-based trajectory planning of a hydraulic excavator. The proposed framework is composed of two main modules: a global planner and a real-time local planner. The global planner computes the entire global trajectory considering excavation volume and energy minimization while the local counterpart tracks the global trajectory in a receding horizon manner, satisfying dynamic feasibility, physical constraints, and disturbance-awareness. We validate the proposed planning algorithm in a simulation environment where two types of operations are conducted in the presence of emulated disturbance from hydraulic friction and soil-bucket interaction: shallow and deep excavation. The optimized global trajectories are obtained in an order of a second, which is tracked by the local planner at faster than 30 Hz. To the best of our knowledge, this work presents the first real-time motion planning framework that satisfies constraints of a hydraulic excavator, such as force/torque, power, cylinder displacement, and flow rate limits.