Towards Controlling Bucket Fill Factor in Robotic Excavation by Learning Admittance Control Setpoints

Abstract

This paper investigates the extension of an admittance control scheme to- ward learning and adaptation of its setpoints to achieve controllable bucket fill factor for robotic excavation of fragmented rock. A previously developed Dig Admittance Controller (DAC) is deployed on a 14-tonne capacity robotic load-haul-dump (LHD) machine, and full-scale excavation experiments are conducted with a rock pile at an underground mine to determine how varying DAC set points affect bucket fill factor. Results show that increasing the throttle setpoint increases the bucket fill factor and increasing the bucket’s reference velocity setpoint decreases the bucket fill factor. Further, the bucket fill factor is consistent for different setpoint values. Based on these findings, a learning framework is postulated to learn DAC setpoint values for a desired bucket fill factor over successive excavation iterations. Practical implementation problems such as bucket stall and wheel-slip are also addressed, and improvements to the DAC design are suggested to mitigate these problems.