Expert-Emulating Excavation Trajectory Planning for Autonomous Robotic Industrial Excavator

We propose a novel excavation (i.e., digging) trajectory planning framework for industrial autonomous robotic excavators, which emulates the strategies of human expert operators to optimize the excavation of (complex/unmodellable) soils while also upholding robustness and safety in practice. First, we encode the trajectory with dynamic movement primitives (DMP), which is known to robustly preserve qualitative shape of the trajectory and attraction to (variable) end-points (i.e., start-points of swing/dumping), while also being data-efficient due to its structure, thus, suitable for our purpose, where expert data collection is expensive. We further shape this DMPbased trajectory to be expert-emulating, by learning the shaping force of the DMP-dynamics from the real expert excavation data via a neural network (i.e., MLP (multi-layer perceptron)). To cope with (possibly dangerous) underground uncertainties (e.g., pipes, rocks), we also real-time modulate the expert-emulating (nominal) trajectory to prevent excessive build-up of excavation force by using the feedback of its online estimation. The proposed framework is then validated/demonstrated by using an industrial-scale autonomous robotic excavator, with the associated data also presented here.

I. Introduction

Excavators are the mostly widely-used one among heavy machinery equipments at construction sites. Automation or robotization of these excavators have received great attention for a long time (e.g., [1]), since it can improve construction efficiency via over-the-clock operation while eliminating operator health, fatigue or safety concerns. It is becoming equally challenging to find a skilled operator for excavators, particularly in many fast-aging countries. With the advancements of sensors, computing, communication and actuators, the construction machinery industry now starts to embark on the commercialization of this autonomous (or automated) excavator, with some of its component technologies already commercialized or very close to that (e.g., machine control [2]–[4], machine guidance [5]).