The process of forest harvesting is highly mechanized in most industrialized countries, with felling and processing of trees performed by technologically advanced forestry machines. However, the maneuvering of the vehicles through the forest as well as the control of the on-board hydraulic boom crane is currently performed through continuous manual operation. This complicates the introduction of further incremental productivity improvements to the machines, as the operator becomes a bottleneck in the process. A suggested solution strategy is to enhance the production capacity by increasing the level of automation. At the same time, the working environment for the operator can be improved by a reduced workload, provided that the human-machine interaction is adapted to the new automated functionality. The objectives of this thesis are 1) to describe and analyze the current logging process and to locate areas of improvements that can be implemented in current machines, and 2) to investigate future methods and concepts that possibly require changes in work methods as well as in the machine design and technology. The thesis describes the development and integration of several algorithmic methods and the implementation of corresponding software solutions, adapted to the forestry machine context. Following data recording and analysis of the current work tasks of machine operators, trajectory planning and execution for a specific category of forwarder crane motions has been identified as an important first step for short term automation. Using the method of path-constrained trajectory planning, automated crane motions were demonstrated to potentially provide a substantial improvement from motions performed by experienced human operators. An extension of this method was developed to automate some selected motions even for existing sensorless machines. Evaluation suggests that this method is feasible for a reasonable deviation of initial conditions. Another important aspect of partial automation is the human-machine interaction. For this specific application a simple and intuitive interaction method for accessing automated crane motions was suggested, based on head tracking of the operator. A preliminary interaction model derived from user experiments yielded promising results for forming the basis of a target selection method, particularly when combined with some traded control strategy. Further, a modular software platform was implemented, integrating several important components into a framework for designing and testing future interaction concepts. Specifically, this system was used to investigate concepts of teleoperation and virtual environment feedback. Results from user tests show that visual information provided by a virtual environment can be advantageous compared to traditional video feedback with regards to both objective and subjective evaluation criteria.

Whether it is a car, a mobile phone, or a computer, we are noticing how automation and production with robots plays an important role in the industry of our modern world. We find it in factories, manufacturing products, automotive cruise control, construction equipment, autopilot on airplanes, and countless other industrial applications.         Automation technology can vary greatly depending on the field of application. On one end, we have systems that are operated by the user and rely fully on human ability. Examples of these are heavy-mobile equipment, remote controlled systems, helicopters, and many more. On the other end, we have autonomous systems that are able to make algorithmic decisions independently of the user.         Society has always envisioned robots with the full capabilities of humans. However, we should envision applications that will help us increase productivity and improve our quality of life through human-robot collaboration. The questions we should be asking are: “What tasks should be automated?'', and “How can we combine the best of both humans and automation?”. This thinking leads to the idea of developing systems with some level of autonomy, where the intelligence is shared between the user and the system. Reasonably, the computerized intelligence and decision making would be designed according to mathematical algorithms and control rules.         This thesis considers these topics and shows the importance of fundamental mathematics and control design to develop automated systems that can execute desired tasks. All of this work is based on some of the most modern concepts in the subjects of robotics and control, which are synthesized by a method known as the Virtual Holonomic Constraints Approach. This method has been useful to tackle some of the most complex problems of nonlinear control, and has enabled the possibility to approach challenging academic and industrial problems. This thesis shows concepts of system modeling, control design, motion analysis, motion planning, and many other interesting subjects, which can be treated effectively through analytical methods. The use of mathematical approaches allows performing computer simulations that also lead to direct practical implementations.

Motion planning and control synthesis are challenging problems for underactuated mechanical systems due to the presence of passive (non-actuated) degrees of freedom. For those systems that are additionally not feedback linearizable and with unstable internal dynamics there are no generic methods for planning trajectories and their feedback stabilization. For fully actuated mechanical systems, on the other hand, there are standard tools that provide a tractable solution. Still, the problem of generating efficient and optimal trajectories is nontrivial due to actuator limitations and motion-dependent velocity and acceleration constraints that are typically present. It is especially challenging for manipulators with kinematic redundancy. A generic approach for solving the above-mentioned problems is described in this work. We explicitly use the geometry of the state space of the mechanical system so that a synchronization of the generalized coordinates can be found in terms of geometric relations along the target motion with respect to a path coordinate. Hence, the time evolution of the state variables that corresponds to the target motion is determined by the system dynamics constrained to these geometrical relations, known as virtual holonomic constraints. Following such a reduction for underactuated mechanical systems, we arrive at integrable second-order dynamics associated with the passive degrees of freedom. Solutions of this reduced dynamics, together with the geometric relations, can be interpreted as a motion generator for the full system. For fully actuated mechanical systems the virtually constrained dynamics provides a tractable way of shaping admissible trajectories. Once a feasible target motion is found and the corresponding virtual holonomic constraints are known, we can describe dynamics transversal to the orbit in the state space and analytically compute a transverse linearization. This results in a linear time-varying control system that allows us to use linear control theory for achieving orbital stabilization of the nonlinear mechanical system as well as to conduct system analysis in the vicinity of the motion. The approach is applicable to continuous-time and impulsive mechanical systems irrespective of the degree of underactuation. The main contributions of this thesis are analysis of human movement regarding a nominal behavior for repetitive tasks, gait synthesis and stabilization for dynamic walking robots, and description of a numerical procedure for generating and stabilizing efficient trajectories for kinematically redundant manipulators.