Mikael Svenstrup

Estimation of Rotor Effective Wind Speed: A Comparison

Modern wind turbine controllers use wind speed information to improve power production and reduce loads on the turbine components. The turbine top wind speed measurement is unfortunately imprecise and not a good representative of the rotor effective wind speed. Consequently, many different model-based algorithms have been proposed that are able to estimate the wind speed using common turbine measurements. In this paper, we present a concise yet comprehensive analysis and comparison of these techniques, reviewing their advantages and drawbacks. We implement these techniques and compare the results on both aero-servo-elastic turbine simulations and real turbine field experiments in different wind scenarios.

Trajectory planning for robots in dynamic human environments

This paper presents a trajectory planning algorithm for a robot operating in dynamic human environments. Environments such as pedestrian streets, hospital corridors, train stations or airports. We formulate the problem as planning a minimal cost trajectory through a potential field, defined from the perceived position and motion of persons in the environment.

Pose estimation and adaptive robot behaviour for human-robot interaction

This paper introduces a new method to determine a persons pose based on laser range measurements. Such estimates are typically a prerequisite for any human-aware robot navigation, which is the basis for effective and timeextended interaction between a mobile robot and a human. The robot uses observed information from a laser range finder to detect persons and their position relative to the robot. This information together with the motion of the robot itself is fed through a Kalman filter, which utilizes a model of the human kinematic movement to produce an estimate of the persons pose. The resulting pose estimates are used to identify humans who wish to be approached and interacted with. The behaviour of the robot is based on adaptive potential functions adjusted accordingly such that the persons social spaces are respected. The method is tested in experiments that demonstrate the potential of the combined pose estimation and adaptive behaviour approach.

Adaptive human aware navigation based on motion pattern analysis

Respecting peoples social spaces is an important prerequisite for acceptable and natural robot navigation in human environments. In this paper, we describe an adaptive system for mobile robot navigation based on estimates of whether a person seeks to interact with the robot or not. The estimates are based on run-time motion pattern analysis compared to stored experience in a database. Using a potential field centered around the person, the robot positions itself at the most appropriate place relative to the person and the interaction status. The system is validated through qualitative tests in a real world setting. The results demonstrate that the system is able to learn to navigate based on past interaction experiences, and to adapt to different behaviors over time.

Adaptive Human-Aware Robot Navigation in Close Proximity to Humans

For robots to be able coexist with people in future everyday human environments, they must be able to act in a safe, natural and comfortable way. This work addresses the motion of a mobile robot in an environment, where humans potentially want to interact with it. The designed system consists of three main components: a Kalman filter-based algorithm that derives a persons state information (position, velocity and orientation) relative to the robot; another algorithm that uses a Case-Based Reasoning approach to estimate if a person wants to interact with the robot; and, finally, a navigation system that uses a potential field to derive motion that respects the persons social zones and perceived interest in interaction. The operation of the system is evaluated in a controlled scenario in an open hall environment. It is demonstrated that the robot is able to learn to estimate if a person wishes to interact, and that the system is capable of adapting to changing behaviours of the humans in the environment.

Minimising computational complexity of the RRT algorithm a practical approach

Sampling based techniques for robot motion planning have become more widespread during the last decade. The algorithms however, still struggle with for example narrow passages in the configuration space and suffer from high number of necessary samples, especially in higher dimensions. A widely used method is Rapidly-exploring Random Trees (RRTs). One problem with this method is the nearest neighbour search time, which grows significantly when adding a large number of vertices. We propose an algorithm which decreases the computation time, such that more vertices can be added in the same amount of time to generate better trajectories. The algorithm is based on subdividing the configuration space into boxes, where only specific boxes needs to be searched to find the nearest neighbour. It is shown that the computational complexity is lowered from a theoretical point of view. The result is an algorithm that can provide better trajectories within a given time period, or alternatively compute trajectories faster. In simulation the algorithm is verified for a simple RRT implementation and in a more specific case where a robot has to plan a path through a human inhabited environment.

Pilot Study of Person Robot Interaction in a Public Transit Space

This paper describes a study of the effect of a human interactive robot placed in an urban transit space. The underlying hypothesis is that it is possible to create interesting new living spaces and induce value in terms of experiences, information or economics, by putting socially interactive mobile agents into public urban transit area. To investigate the hypothesis, an experiment was carried out at a bus terminal serving both as a transit space and a shopping mall, where an autonomous robot were to detect and follow random people. The people that were followed were asked to fill out a questionnaire for quantitative analysis of the experiment. In addition video documentation of the experiment was used in the evaluation. The results showed that people were generally positive towards having mobile robots in this type of environment where shopping is combined with transit. However, it also showed harder than expected to start interaction with commuters due to their determination and speed towards their goal. Further it was demonstrated that it was possible to track and follow people, who were not beforehand informed on the experiment. The evaluation indicated, that the distance to initiate interaction was shorter than initially expected, but complies with the distance for normal human to human interaction.

Certificate for safe emergency shutdown of wind turbines

To avoid damage to a wind turbine in the case of a fault or a large wind gust, a detection scheme for emergency shutdown is developed. Specifically, the concept of a safety envelope is introduced. Within the safety envelope, the system can be shutdown without risking structural damage to the turbine. To demarcate the boundary of the safety envelope, a protection certificate, is computed. To this end, a model-based framework of barrier certificates is used. As a result, the protection certificate problem is formulated as a sum-of-squares program with the optimisation criterion related to the volume of the safety envelope. The framework enables the inclusion of a bounded wind disturbance and the a priori known emergency shutdown procedure. For this purpose, the model of a wind turbine is developed that includes structural safety critical components.

The SantaBot experiment: a pilot study of human-robot interaction

The video shows how an autonomous mobile robot dressed as Santa Claus is interacting with people in a shopping mall. The underlying hypothesis is that it is possible to create interesting new living spaces and induce value in terms of experiences, information or economics, by putting socially interactive mobile agents into public urban transit area. To investigate the hypothesis, an experiment was carried out using a robot capable of navigating autonomously based on the input of an onboard laser scanner. The robot would detect and follow random people, who afterwards were asked to fill out a questionnaire for quantitative analysis of the experiment. The presented video is the corresponding video documentation of the experiment used in the evaluation. The results showed that people were generally positive towards having mobile robots in this type of environment where shopping is combined with transit. However, it also showed harder than expected to start interaction with commuters due to their determination and speed towards their goal. Further it was demonstrated that it was possible to track and follow people, who were not beforehand informed on the experiment. The evaluation indicated, that the distance to initiate interaction was shorter than initially expected, but complies with the distance for normal human to human interaction.