Forwarder crane’s boom tip control system and beginner-level operators

Abstract

Highlights• Boom tip control (BTC) allows the operator to control boom tip movements directly, instead of controlling each movement separately to achieve the desired boom tip movement.• BTC eased boom control, so beginner-level operators using BTC achieved higher productivity than beginner-level operators using a conventional (reference) system. • There were no significant differences in the slopes of learning curves between the systems. AbstractThe forwarder loads processed wood and transports it to a landing. Productivity of forwarding could be improved by increasing driving speed, but difficult forest terrain limits this. According to current literature, crane work is the most time-consuming work element of forwarding, so improving crane work productivity is essential for improving forwarding productivity. One way to do this is through automation of recurrent boom movement patterns, or alternatively automation can be used to ease crane work. When using conventional boom control (CBC), the operator manually controls each of the independent boom joint movements and combines them to achieve a desired boom tip movement, but boom tip control (BTC) allows the operator to control boom tip movements directly. The objective of the present study was to examine whether BTC facilitates crane work and affects the slopes of learning curves for beginner-level forwarder operators. The study was carried out using a standardised test routine to evaluate effects of two fixed factors, system (levels: CBC, BTC) and point of time (four levels), on five dependent variables. Four of the five dependent variables measured ease of boom control and the fifth measured crane work productivity. The results showed that there were no significant differences in the slopes of learning curves between the systems but the BTC did increase crane work productivity and made boom control easier.

Influence of Loading Distance, Loading Angle and Log Orientation on Time Consumption of Forwarder Loading Cycles: A Pilot Case Study

“…Through automation of work processes interlinked with digitalization [47,48], further research should aim to explore the suitability of technical devices such as rotating cabins or automated crane control to reduce the loading cycle time or the ergonomic strain on the operator. Results from other studies showed that, for example, boom tip control can improve crane work and loading productivity [49,50], in particular since the telescope no longer needs to be operated separately anymore [51][52][53].…”

A Decade of Forest Engineering: Achievements and Future Directions

“…Another example of the software aiding machine control is the new John Deere 'Intelligent Boom Control' system that allows the operator to control the movement of the head directly, as opposed to moving each individual component of the boom. It has already been shown that the use of such technology makes it easier to learn to operate harvester or forwarder machines and to achieve sustained productivity within a shorter learning time [24,25]. Komatsu recently published a vision of using drones, including communication technologies and cloud services to improve harvesting efficiency.…”

Reinforcement Learning Control of a Forestry Crane Manipulator

“…Semi-automation of forestry machines has been success-fully explored before, increasing productivity and reducing the immediate workload on the operator [6,20]. In contrast to methods previously adopted in forestry automation research, the RL framework has shown potential for intelligent and independent end-to-end learning of complex tasks in simulated environments, completely removing the need for analytic low-level controllers.…”