Planning for Automatic Excavator Operations

Abstract

The operations of an excavator can be automated by making a digital computer control its motion so that the system functions autonomously. In order to realize the automatic operations the nominal (desired) trajectory for the motion of the excavator must be preplanned and stored in the computer. This trajectory should specify the pose (the position and orientation) of the bucket as a function of time in a fixed (Cartesian) coordinate system. Since the pose of the bucket is determined by the configuration of the excavator, the planned trajectory should then be converted into the joint space in order to obtain the nominal trajectories for the joint variables corresponding to the Cartesian space trajectory. This conversion can be accomplished by solving the kinematic equations for the excavator in the backward direction.The kinematic equations relate the pose of the bucket to the joint variables of the excavator. They can conveniently be written if the local (moving) coordinate frames are first assigned to all links (joints) by following the Denavit-Hartenberg procedure which is a well-known method in robotics. Then after the structural (kinematic) parameters have been determined for the given excavator, the transformation matrices relating any two adjacent coordinate frames can be written. The recursive equations are then utilized to obtain the kinematic equations which relate the joint variable values to the bucket pose. These trigonometric equations can then be solved for the joint variables in terms of the given pose of the bucket. We will present these equations which represent the solution to the inverse kinematic problem for the excavators. These relations are then used to plan the motion of the excavator bucket. The problem of removing soil contained in a sector to a specified depth is then solved. The trajectory for the bucket is planned so that this task will be accomplished.

Research on the Prediction Method of Braking Rotation Angle for Remote-Controlled Excavator

“…Among them, the development of intelligent technology for excavators is of paramount importance, mainly including autonomous control [ 8 , 9 ], automatic navigation [ 10 , 11 ], and automatic scheduling of excavators. For example, through machine learning and other technologies, excavators can recognize and adapt to complex terrain and obstacles [ 12 , 13 , 14 ], achieving more efficient operations.…”