We propose a novel method to analyze how human operators use hydraulic manipulators of heavy-duty equipment. The approach is novel in the sense that it applies knowledge of motion planning and optimization techniques used in robotics. As an example, we consider the case of operating a forestry crane. To that end, we use motion data that has been recorded during standard operation with the help of sensors and a data acquisition unit. The data backs up the notion that operators work by performing repeatable patterns observed in the trajectories of the manipulator's joints. We show how this nominal behavior is computed, and consequently, this allows us to present the following: 1) an analytical procedure to analyze motions, 2) how to represent the “performance” of the operator in a 2-D plot, 3) an example of how to use this information to suggest customized control settings, and 4) some complementary ideas needed for improving efficiency through automation.

We analyze the problem of dynamic non-prehensile manipulation by considering the example of the butterfly robot. Our main objective is to study the problem of stabilizing periodic motions, which resemble some form of juggling acrobatics. To this end, we approach the problem by considering the framework of virtual holonomic constraints. Under this basis, we provide an analytical and systematic solution to the problems of trajectory planning and design of feedback controllers to guarantee orbital exponential stability. Results are presented in the form of simulation tests.

We consider the example of a three-link planar biped walker with two passive links. The main objective is to design symmetric periodic gaits in flat ground, that can be exponentially stabilized by feedback control. To this end, we apply recent advances in nonlinear control, to propose a systematic procedure to the problems of gait synthesis and control design. The core of the method lays on a nontrivial coordinate transformation, in order to approach the problem in a state-dependent form. For gait synthesis, such procedure allows a reduction of the search space, with the feasibility of considering energetic performance for optimization. For control design, this allows to apply concepts of transverse linearization, to design a nonlinear feedback control law, which performance is studied by numerical simulations.

We present results of modeling dynamics of a forestry manipulator, in which we consider its mechanics, as well as its hydraulic actuation system. The mathematical model of its mechanics is formulated by Euler-Lagrange equations, for which the addition of friction forces is straightforward. Dynamics of the hydraulic system is modeled upon first principle laws, which concern flow through orifices and fluid compressibility. These models lead to a set of equations with various unknown parameters, which are related to the inertias, masses, location of center of masses, friction forces, and valve coefficients. The numerical values of these parameters are estimated by the use of least-square methods, which is made feasible by transforming the models into linear representations. The results of simulation tests show a significant correspondence between measured and estimated variables, validating our modeling and identification approach.

A short term goal in the forest industry is semi-automation of existing machines for the tasks of logging and harvesting. One way to assist drivers is to provide a set of predefined trajectories that can be used repeatedly in the process. In recent years much effort has been directed to the design of control strategies and task planning as part of this solution. However, commercialization of such automatic schemes requires the installation of various sensing devices, computers and most of all a redesign of the machine itself, which is currently undesired by manufacturers. Here we present an approach of implementing predefined trajectories in an open-loop fashion, which avoids the complexity of sensor and computer integration. The experimental results are carried out on a commercial hydraulic crane to demonstrate that this solution is feasible in practice.

In the forest industry, trees are logged and harvested by human-operated hydraulic manipulators. Eventually, these tasks are expected to be automated with optimal performance. However, with todays technology the main problem is implementation. While prototypes may have rich sensing information, real cranes lack certain sensing devices, such as encoders for position sensing. Automating these machines requires unconventional solutions. In this paper, we consider the motion planning problem, which involves a redesign of optimal trajectories, so that open loop control strategies can be applied using feed-forward control signals whenever sensing information is not available.