Design and Development of a Mobile Climbing Robot for Wind Turbine Inspection

Wind turbines (WT) have become an essential renewable energy source as the contribution of WT farms has reached megawatts scale. However, wind turbine blades (WTB) are subjected to failure due to many loading effects such as aerodynamic, gravity and centrifugal loads and operation in harsh environments such as ultraviolet (UV) radiation, ice, hail, temperature variation, dirt, and salt. As a result, the blades suffer different types of damage. Consequently, a periodic inspection process is required to detect and repair defects before a catastrophic failure happens. This thesis presents a literature review of wall climbing robots to identify the most appropriate locomotion and adhesion method to use for a WT climbing machine that can take a large payload of non-destructive testing (NDT) sensors up to a blade and deploy them with scanning arms. A review of wind turbine blade construction, various loading effects on blades and types of damage in blades is followed by a review of the NDT techniques used for inspecting WTB. The above review determines the design requirements to achieve the aim of the current research which is to design a low-cost and reliable mobile robot which will be able to climb the WT tower and subsequently scan the blade surface to perform the inspection using various sensors to identify and classify damages. This robot system should be able to access all the critical areas of the blade structure in a stable and secure way. It should be stable enough to allow the various test sensors to scan the blade structure in the shortest possible time. The thesis describes the development of a tower climbing robot that uses magnetic adhesion to adhere to the WT. As a preliminary study, a simulation model is developed using COMSOL Multiphysics to simulate the magnetic adhesion force while climbing the tower. A test rig is designed and fabricated to measure the magnetic adhesion force experimentally to validate the simulation model. The response surface methodology (RSM) using Box-Behnken design (BBD) is used to design and perform experiments to optimise different independent variables i.e. air gap, the distance between magnets in an array and backplate (yoke) thickness that affect the magnetic adhesion force. A scaled-down prototype magnetic adhesion climbing robot has been designed and constructed for wind turbine blade inspection. The robot is 0.29 m long with two 1.0 m long arms, weighs 10.0 kg and can carry a maximum 2.0 kg payload of NDT sensors. Optimum design of a magnetic adhesion mechanism has been developed for the climbing robot prototype that maximises the magnetic adhesion force. The robot is equipped with two arms that can be extended by one meter to come close to the blade for inspection. Each arm is equipped with a gripper that can hold an inspection tool of weight up to one kilogram. A scaled-down wind turbine has been modelled using SolidWorks and a portion of it constructed to experimentally test the scaled-down climbing robot. To scale up the robot prototype for operation on a normal sized wind turbine, a 100 m tall wind turbine with three 76 m long blades has been modelled and the prototype robot scaled up based on these dimensions. The scaled-up robot is 3.0 m long, weighs 1135 kg and has two 10 m long arms. Static stress analysis and flow simulation have been carried out to check the durability of the scaled-up robot while climbing the wind turbine tower. The procedure for scaling up the adhesion mechanism to achieve equilibrium of the robot has been introduced based on the reaction force concluded from the static stress and flow simulation study. As a result, the maximum payload that each arm can carry has been calculated for both the scaled-down prototype (1 kg) and the scaled-up design (50 kg). This concludes the utility and robustness of the wall climbing robot as a robotic solution for wind turbine blade inspection

Biologically Inspired Climbing with a Hexapedal Robot

This paper presents an integrated, systems-level view of several novel design and control features associated with the biologically inspired, hexapedal, RiSE (Robots in Scansorial Environments) robot. RiSE is the first legged machine capable of locomotion on both the ground and a variety of vertical building surfaces including brick, stucco, and crushed stone at speeds up to 4 cm/s, quietly and without the use of suction, magnets, or adhesives. It achieves these capabilities through a combination of bioinspired and traditional design methods. This paper describes the design process and specifically addresses body morphology, hierarchical compliance in the legs and feet, and sensing and control systems that enable robust and reliable climbing on difficult surfaces. Experimental results illustrate the effects of various behaviors on climbing performance and demonstrate the robot\u27s ability to climb reliably for long distances

A Self-Exciting Controller for High-Speed Vertical Running

Traditional legged runners and climbers have relied heavily on gait generators in the form of internal clocks or reference trajectories. In contrast, here we present physical experiments with a fast, dynamical, vertical wall climbing robot accompanying a stability proof for the controller that generates it without any need for an additional internal clock or reference signal. Specifically, we show that this “self-exciting” controller does indeed generate an “almost” globally asymptotically stable limit cycle: the attractor basin is as large as topologically possible and includes all the state space excluding a set with empty interior. We offer an empirical comparison of the resulting climbing behavior to that achieved by a more conventional clock-generated gait trajectory tracker. The new, self-exciting gait generator exhibits a marked improvement in vertical climbing speed, in fact setting a new benchmark in dynamic climbing by achieving a vertical speed of 1.5 body lengths per second. For more information: Kod*La

Design of Autonomous Cleaning Robot

Today, the research is concentrated on designing and developing robots to address the challenges of human life in their everyday activities. The cleaning robots are the class of service robots whose demands are increasing exponentially. Nevertheless, the application of cleaning robots is confined to smaller areas such as homes. Not much autonomous cleaning products are commercialized for big areas such as schools, hospitals, malls, etc. In this thesis, the proof of concept is designed for the autonomous floor-cleaning robot and autonomous board-cleaning robot for schools. A thorough background study is conducted on domestic service robots to understand the technologies involved in these robots. The components of the vacuum cleaner are assembled on a commercial robotic platform. The principles of vacuum cleaning technology and airflow equations are employed for the component selection of the vacuum cleaner. As the autonomous board-cleaning robot acts against gravity, a magnetic adhesion is used to adhere the robot to the classroom board. This system uses a belt drive mechanism to manoeurve. The use of belt drive increases the area of magnetic attraction while the robot is in motion. A semi-systematic approach using patterned path planning techniques for the complete coverage of the working environment is discussed in this thesis. The outcome of this thesis depicts a new and conceptual mechanical design of an autonomous floor-cleaning robot and an autonomous board-cleaning robot. This evidence creates a preliminary design for proof-of-concept for these robots. This proof of concept design is developed from the basic equations of vacuum cleaning technology, airflow and magnetic adhesion. A general overview is discussed for collaborating the two robots. This research provides an extensive initial step to illustrate the development of an autonomous cleaning robot and further validates with quantitative data discussed in the thesis

Marine Vessel Inspection as a Novel Field for Service Robotics: A Contribution to Systems, Control Methods and Semantic Perception Algorithms.

This cumulative thesis introduces a novel field for service robotics: the inspection of marine vessels using mobile inspection robots. In this thesis, three scientific contributions are provided and experimentally verified in the field of marine inspection, but are not limited to this type of application. The inspection scenario is merely a golden thread to combine the cumulative scientific results presented in this thesis. The first contribution is an adaptive, proprioceptive control approach for hybrid leg-wheel robots, such as the robot ASGUARD described in this thesis. The robot is able to deal with rough terrain and stairs, due to the control concept introduced in this thesis. The proposed system is a suitable platform to move inside the cargo holds of bulk carriers and to deliver visual data from inside the hold. Additionally, the proposed system also has stair climbing abilities, allowing the system to move between different decks. The robot adapts its gait pattern dynamically based on proprioceptive data received from the joint motors and based on the pitch and tilt angle of the robot's body during locomotion. The second major contribution of the thesis is an independent ship inspection system, consisting of a magnetic wall climbing robot for bulkhead inspection, a particle filter based localization method, and a spatial content management system (SCMS) for spatial inspection data representation and organization. The system described in this work was evaluated in several laboratory experiments and field trials on two different marine vessels in close collaboration with ship surveyors. The third scientific contribution of the thesis is a novel approach to structural classification using semantic perception approaches. By these methods, a structured environment can be semantically annotated, based on the spatial relationships between spatial entities and spatial features. This method was verified in the domain of indoor perception (logistics and household environment), for soil sample classification, and for the classification of the structural parts of a marine vessel. The proposed method allows the description of the structural parts of a cargo hold in order to localize the inspection robot or any detected damage. The algorithms proposed in this thesis are based on unorganized 3D point clouds, generated by a LIDAR within a ship's cargo hold. Two different semantic perception methods are proposed in this thesis. One approach is based on probabilistic constraint networks; the second approach is based on Fuzzy Description Logic and spatial reasoning using a spatial ontology about the environment