This research focuses on the problem of increasing productivity for the task of autonomous mass excavation. Autonomous excavation has the benefits of higher productivity, lower labor costs, increased safety, and the ability to work in hazardous environments. Mass excavation involves rapidly loading trucks with soil/rock/ore using a mobile digging machine with a bucket on an arm-like appendage. It is desirable for this operation to proceed very quickly, load the trucks evenly, avoid excessive spillage, and perform safely while operating in a wide variety of possible digging conditions and worksite configurations. Preliminary work has been done on planning the excavator’s motions using a script-based approach, which takes advantage of the fact that the excavator’s motions are very similar for each bucket load, but the kinematic details can change due to changes in digging, dumping, and truck locations. Currently these kinematic details, also known as the script parameters, are computed using inverse kinematics, simple machine models, heuristics, and “magic ” numbers which need to be adjusted from time to time. To date, this technique has worked very well on our test site, with autonomous excavator productivity approaching tha
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