In forestry, thinning operations place considerable cognitive demands on harvester operators. The operators are responsible not only for tree selection but also for manoeuvring the harvester head between the remaining trees without causing damage on them. Consequently, crane work accounts for approximately 90% of total time consumption during thinnings. Assistance systems facilitating crane work, such as boom-tip control (BTC), allow for improved productivity during thinning. With BTC, the operator controls the movement of the boom tip directly, rather than manipulating individual crane joints. We conducted a standardised field experiment simulating crane movement between remaining trees, as in thinning operations. The harvester used was a midsized (18-tonne) Komatsu 911, equipped with Smart Crane, i.e. Komatsu’s version of BTC. The operators (n = 18) were students enrolled in a three-year forest machine operator education programme. Each experimental run involved visiting and gripping 13 standing stems with the harvester head. None of the stems were felled, allowing them to be reused throughout the experiment. We applied repeated-measures analysis of covariance to compare conventional boom control with Smart Crane. On average, operators completed the task 9.5% faster when they used Smart Crane. Based on our findings and current literature, BTC appears to offer greater time-saving potential in thinnings than during clearcutting. Furthermore, as most operators benefitted from BTC, we recommend full implementation of BTC on all new forest machines.
Back Tomas Ersson (Wed,) studied this question.