Massey Documents by Type

Permanent URI for this communityhttps://mro.massey.ac.nz/handle/10179/294

Browse

Subject: search.filters.subject.Mobile robots

Search Results

Now showing 1 - 10 of 13
  • Thumbnail Image
    Item
    Robot assisted floor surface mapping and modelling for prediction of grind finish : submitted to the School of Engineering and Advanced Technology in partial fulfillment of the requirements for the degree of Master of Engineering, Mechatronics, at Massey University, Auckland
    (Massey University, 2018) Wilson, Scott D.
    Mapping and localisation is a fundamental aspect of mobile robotics as all robots must successfully know their surroundings and location for navigation and manipulation. For some navigation tasks, prior knowledge of the 3D environment, in particular the 3D surface profile, can greatly improve navigation and manipulation tasks such as contacting, sensor inspecting, terrain traversability, and modifications. This thesis presents the investigation into the capability of cheap and accessible sensors to capture the floor surface information and assesses the ability for the 3D representation of the floor to be used as prior knowledge for a model. A differential drive robotic platform was developed to perform testing and conduct the research. 2D localisation methods were extrapolated into 3D for the floor capturing process. The robotic system was able to successfully capture the floor surface profile of a number of different type floors such as carpet, asphalt, and a coated floor. Two different types of sensor, a 2D laser scanner and an RGB-D camera, were used for comparison of the floor capture ability. A basic model was developed to estimate the floor surface information. The captured surface was used as prior knowledge for the model, and testing was performed to validate the devised model. The model performed well in some areas of the floor, but requires further development to improve the performance. Further validation testing of the system is required, and the system can be improved by improvement of 3D localisation, minimisation of sensor errors, and further testing into the application.
  • Thumbnail Image
    Item
    Design of a three-wheel omni-directional mobile robot base module : a thesis in the partial fulfillment of the requirements for the degree of Master of Engineering in Mechatronics at Massey University, Turitea Campus, Palmerston North, New Zealand
    (Massey University, 2013) Deng, Jinrui
    In this research, the aim is to develop a modular autonomous mobile robot base that has a certain degree of flexibility and cost effectiveness for some indoor mobile robot applications that may have limited maneuverable space. The structure of the mobile robot and the wheel design are the major investigation areas. A modular mobile robot construction that is able to quickly integrate with different wheels and add on sub-systems has been developed for this project. The experiments made on the test model are positive. In this project, the mobile robot is built with omni-directional wheels. The omni-directional wheels make the mobile robot maneuverable in its motions. The shape of the mobile robot, base and number of wheels that are mounted on the mobile robot were decided based on the structure of the omni-directional wheels. Modular design makes the omni-directional mobile robot a very practical application. Most of the parts in the omni-directional mobile robot can be easily replaced and reused. The mobile robot itself is constrained to be one that is inexpensive and simple. This would allow others to replicate its concept and improve on it. The control system can be improved by simply replacing the control circuit board on the mobile robot. The new control system can be easily integrated with the peripheral devices, such as motors and sensors. Moreover, the size of the mobile robot base is adjustable , which makes this base design valuable for those who are interested in the development of omni-directional robot applications but are concerned about the size of the robot.
  • Thumbnail Image
    Item
    Indoor localization of a mobile robot using sensor fusion : a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering in Mechatronics with Honours at Massey University, Wellington, New Zealand
    (Massey University, 2011) Zhou, Jason
    Reliable indoor navigation of mobile robots has been a popular research topic in recent years. GPS systems used for outdoor mobile robot navigation can not be used indoor (warehouse, hospital or other buildings) because it requires an unobstructed view of the sky. Therefore a specially designed indoor localization system for mobile robot is needed. This project aims to develop a reliable position and heading angle estimator for real time indoor localization of mobile robots. Two different techniques have been developed and each consisted of three different sensor modules based on infrared sensing, calibrated odometry and calibrated gyroscope. Integration of these three sensor modules is achieved by applying the real time Kalman filter which provides filtered and reliable information of a mobile robot's current location and orientation relative to its environment. Extensive experimental results are provided to demonstrate its improvement over conventional methods like dead reckoning. In addition, a control strategy is developed to control the mobile robot to move along the planned trajectory. The techniques developed in this project have potentials for the application for mobile robots in medical service, health care, surveillances, search and rescue in indoor environments.
  • Thumbnail Image
    Item
    Tree pruning/inspection robot climbing mechanism design, kinematics study and intelligent control : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Mechatronics at Massey University, Manawatu Campus, New Zealand
    (Massey University, 2018) Gui, Pengfei
    Forestry plays an important role in New Zealand’s economy as its third largest export earner. To achieve New Zealand Wood Council’s export target of $12 billion by 2022 in forest and improve the current situation that is the reduction of wood harvesting area, the unit value and volume of lumber must be increased. Pruning is essential and critical for obtaining high-quality timber during plantation growing. Powerful tools and robotic systems have great potential for sustainable forest management. Up to now, only a few tree-pruning robotic systems are available on the market. Unlike normal robotic manipulators or mobile robots, tree pruning robot has its unique requirements and features. The challenges include climbing pattern control, anti-free falling, and jamming on the tree trunk etc. Through the research on the available pole and tree climbing robots, this thesis presents a novel mechanism of tree climbing robotic system that could serve as a climbing platform for applications in the forest industry like tree pruning, inspection etc. that requires the installation of powerful or heavy tools. The unique features of this robotic system include the passive and active anti-falling mechanisms that prevent the robot falling to the ground under either static or dynamic situations, the capability to vertically or spirally climb up a tree trunk and the flexibility to suit different sizes of tree trunk. Furthermore, for the convenience of tree pruning and the fulfilment of robot anti-jamming feature, the robot platform while the robot climbs up should move up without tilting. An intelligent platform balance control system with real-time sensing integration was developed to overcome the climbing tilting problem. The thesis also presents the detail kinematic and dynamic study, simulation, testing and analysis. A physical testing model of this proposed robotic system was built and tested on a cylindrical rod. The mass of the prototype model is 6.8 Kg and can take 2.1 Kg load moving at the speed of 42 mm/s. The trunk diameter that the robot can climb up ranges from 120 to 160 mm. The experiment results have good matches with the simulations and analysis. This research established a basis for developing wheel-driven tree or pole climbing robots. The design and simulation method, robotic leg mechanism and the control methodologies could be easily applied for other wheeled tree/pole climbing robots. This research has produced 6 publications, two ASME journal papers and 4 IEEE international conference papers that are available on IEEE Xplore. The published content ranges from robotic mechanism design, signal processing, platform balance control, and robot climbing behavior optimization. This research also brought interesting topics for further research such as the integration with artificial intelligent module and mobile robot for remote tree/forest inspection after pruning or for pest control.
  • Thumbnail Image
    Item
    On the use of optimal search algorithms with artificial potential field for robot soccer navigation : Computer Science, Master of Science
    (Massey University, 2018) Dong, Chen
    The artificial potential field (APF) is a popular method of choice for robot navigation, as it offers an intuitive model clearly defining all attractive and repulsive forces acting on the robot [3] [25] [29] [43] [50]. However, there are drawbacks that limit the usage of this method. For instance, the local minima problem that gets a robot trapped, and the Goal-Non-Reachable-with-Obstacle-Nearby (GNRON) problem, as reported in [51] [5] [23] [2] and [3]. In order to avoid these limitations, this research focuses on devising a methodology of combining the artificial potential field with a selection of optimal search algorithms. This work investigates the performance of the method when using different optimal search algorithms such as the A* algorithm and the any-angle path-planning Theta* Search, in combination with different types of artifcial potential field generators. We also present a novel integration technique, whereby the Potential Field approach is utilized as an internal component of an optimal search algorithm, considering the safeness of the calculated paths. Furthermore, this study also explores the optimization of several auxiliary algorithms used in conjunction with the APF-Optimal search integration: There are three different methods proposed for implementing the line-of-sight (LOS) component of the Theta* search, namely the simple line-of-sight checking algorithm, the modified Bresenham's line algorithm and the modified Cohen-Sutherland algorithm. Contrary to the studies presented in [5], [42], [48] and [40] where the APF and the optimal search algorithms were used separately, in this research, an integrative methodology involving the APF inside the optimal search with a newly proposed Safety Factor (SF) is explored. Experiment results indicate that the APF-A* Search with the SF can reduce the number of state expansions and therefore also the running time up to 19.61%, while maintaining the safeness of the path, as compared to APF-A* when not using the SF. Furthermore, this research also explores how the proposed hybrid algorithms can be used in developing multi-objective behaviours of single robot. In this regard, a robot soccer simulation platform with a physics engine is developed as well to support the exploration. Lastly, the performance of the proposed algorithms is examined under varying environment conditions. Evidences are provided showing that the method can be used in constructing the intelligence for a robot goal keeper and a robot attacker (ball shooter). A multitude of AI robot behaviours using the proposed methods are integrated via a finite state machine including: defensive positioning/parking, ball kicking/shooting, and target pursuing behaviours. Keywords : Artificial Potential Field, Optimal Searches, Robot Navigation, Multi- objective Behaviours.
  • Thumbnail Image
    Item
    Mechatronic design and construction of an intelligent mobile robot for educational purposes : a thesis presented in partial fulfilment of the requirements for the degree of Master of Technology in Engineering and Automation at Massey University, Palmerston North, New Zealand
    (Massey University, 2000) Phillips, Julian George
    The main aim of this project was to produce a working intelligent mechatronically designed mobile robot, which could be used for educational purposes. A secondary aim was to make the robot as a test-bed to investigate new systems (sensors, control etc.) if possible. The mechatronic design of the robot was split in to three sections: the chassis, the sensors and the control. The design and construction of the chassis unit was relatively simple and very few problems were encountered. The drive system chosen for the robot was a four-wheeled Mecanum drive. The major advantage of this system is that it allows multiple degrees of freedom while keeping the control and the number of drive motors to a minimum. The design and construction of the sensors was the main research section. The sensor design evolved around the use of ultrasonic sensors. While a phased array type arrangement was tried with the intention of improving the angular accuracy of the sensors, the use of frequency modulation was used in the end and it proved to be excellent except that the problem of angular accuracy was still not solved. The entire mechatronic system was completed except for the micro controller programming. It operated well when it was given the correct inputs and performed all of the functions it was designed for. It is strongly recommended that further work be done on the use of a computer motherboard instead of the current micro controller as this would allow for easier programming, more complex programs and easy implementation of map building.
  • Thumbnail Image
    Item
    Real time Visual SLAM for mobile robot navigation and object detection : a thesis presented in partial fulfilment of the requirement for the degree of Doctor of Philosophy in Engineering at Massey University, Albany, New Zealand
    (Massey University, 2017) Jing, Changjuan
    This study developed a real-time Visual Simultaneous Localization and Mapping (SLAM) method for mobile robot navigation and object detection (SLAM-O), in order to establish the position of a mobile robot and interesting objects in an unknown indoor environment. The VEX Robotics Competition (VRC) is one of the largest, fastest growing educational programmes in the world, and it is designed to increase student interest and involvement in Science, Technology, Engineering, and Mathematics (STEM). This study aims to enable an autonomous robot to compete with human participants in the VRC match, where robots are programmed to respond to a user’s remote control. To win the competition, the robot needs to optimise between detection of goal objects, navigation and scoring. This thesis presents a Visual SLAM technique for robot localization and field objects’ mapping, and aims to provide an innovative and practical approach to control robot navigation and maximise its scoring. For visual observation, this study consists of an evaluation and comparison of widely used RGB-D cameras. Also, this thesis describes the integration of an iterated video frames module with the Extended Kalman Filter (EKF) for accurate SLAM estimation; where, a new frame selection method is employed. A novel SLAM-O method is developed for detecting objects in a robot’s navigation process. The SLAM-O method uses a new K-Means-based colour identification method for semi-transparent object detection and a new concave-based object separation method for multi-connected objects, which outperform traditional methods. Through conducting an investigation into RGB-D cameras’ performance, in terms of repeatability and accuracy, colour images and depth point clouds accuracy from different cameras are evaluated and compared. These comparison’s results provide a reference for choosing a camera for robot localisation. Depth errors and covariance are obtained from the investigation. The obtained results provide important parameters for a RGB-D camera related computation, such as a SLAM problem, etc. An Extended Kalman Filter (EKF) based Visual SLAM method and an iterated video frames module integrated in the EKF are developed. The Visual SLAM method can handle feature detection and corresponding depth measurements in an efficient way, and the iterated video frames module is capable of maximise robot self-localization accuracy. Experimental results demonstrate the accuracy of states estimate. These two method enables a mobile robot navigates accurately in an indoor environment with low computation cost. In addition, this study also presents a SLAM-O method by integrating object detection into Visual SLAM. SLAM-O enables robots to locate objects of interest, which can be used in robotic applications, such as navigation, object grasp, etc. To locate objects which are semi-transparent or closely connected, a K-Means method to clustering pixels on a semi-transparent object’s surface and a concave based method for object separation are developed. Experimental results prove the methods efficiency. These two methods are efficient and useful tools for object detection in SLAM-O framework.
  • Thumbnail Image
    Item
    Mechatronic simulation & exploration of a mechanical context relevant to quadrupedal neuromorphic walking employing Nervous networks for control : a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering, Mechatronics at Massey University, Albany, New Zealand
    (Massey University, 2008) Read, Matthew
    Neuromorphic engineering is the studv and emulation of neural sensory and control structures found in the natural world. Currently a significant research focus in this field, and indeed, in engineering at large, is the research of robotic walking platforms - an ideal application for artificial neural controllers. To design such neuromorphic controllers, significant knowledge is needed of the robotic context to which they will he applied. The focus of this research is to explore the relationship between the mechanical design of a robot, and its resultant walking proficiency. A neuromorphic controller utilizing Nervous networks was constructed, and embedded into a typical & useful mechatronic context. This consists of a simple walking platform, of a type commonly used in Nervous network research. This robot was used to provide intuition and a reference point for development of a simulation for empirical testing. A physical simulation of the mechanical context was developed, allowing for the exploration of its behaviour, particularly with regard to the type of walking "caused" by the integration of an appropriate Nervous network controller. To evaluate the behavioural fitness of this context in various configurations, empirical simulations were run using the developed simulation, and heuristic results derived to develop optimized parameters for causing walking behaviours in the studied context. Further simulations were then run to evaluation the efficacy of these developed heuristics. From these simulations & explorations, the presence of an identifiable "critical point phenomenon" in the interaction between the robot's legs was demonstrated. This critical point was then used for parameter extraction; further simulation demonstrated that parameters extracted from this critical point provided near-optimal walking behaviour from the robot in a variety of leg topologies. These results provide significant knowledge and intuition for designers of quadrupedal walking platforms, particularly those driven from Nervous network derived neuromorphic controllers. Implementation of these results in such a robotic platform will provide useful new "real world" data, allowing the developed models & heuristics to be further refined.
  • Thumbnail Image
    Item
    Fusion-SLAM by combining RGB-D SLAM and Rat SLAM : a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering in Mechatronics at Massey University, Albany, New Zealand
    (Massey University, 2016) Tubman, Robert Tsunemichi
    Robotic Simultaneous Localization and Mapping (SLAM) is the problem of solving how to create a map of the environment while localizing the robot in the map being created. This presents a causality dilemma where the map needs to be created in order to localize the robot, but the robot also needs to be localized in order to create the map. In past research there have been many solutions to this problem ranging from Extended Kalman Filter (EKF) to Graph SLAM systems. There has also been extensive research in bioinspired methods, like ratSLAM implemented in aerial and land-based robots. The different research setups use sensors such as Time of Flight (ToF) e.g. laser scanners and passive devices e.g. cameras. Over the past few years a new type of combined apparatus has been developed by Microsoft called the Kinect. It combines active and passive sensing elements and aligns the data in a way which allows for efficient implementation in robotic systems. This has led to the Kinect being implemented in new research and many studies, mostly around RGB-D SLAM. However these methods generally require a continuous stream of images and become inaccurate when exposed to ambiguous environments. This thesis presents the design and implementation of a fusion algorithm to solve the robotic SLAM problem. The study starts by analysing existing methods to determine what research has been done. It then proceeds to introduce the components used in this study and the Fusion Algorithm. The algorithm incorporates the colour and depth data extraction and manipulation methods used in the RGB-D SLAM system while also implementing a mapping step similar to the grid cell and firing field functions found in the ratSLAM. This method improves upon the RGB-D SLAM’s weakness of requiring a continuous stream and ambiguous images. An experiment is then conducted on the developed system to determine the extent to which it has solved the SLAM problem. Moreover, the success rate for finding a node in a cell and matching its pose is also investigated. In conclusion, this research presents a novel algorithm for successfully solving the robotic SLAM problem. The proposed algorithm also helps improve the system’s efficiency in navigation, odometry error correction, and scan matching vulnerabilities in feature sparse views.
  • Thumbnail Image
    Item
    Wireless indoor mobile robot with RFID navigation map and live video : a thesis in the partial fulfilment of the requirements for the degree of Masters of Engineering in Mechatronics, Massey University, Palmerston North, New Zealand
    (Massey University, 2011) Garratt, Samuel
    A mobile robot was designed in order to move freely within a map built in an indoor environment. The aim is for the robot to move between passive RFID (Radio Frequency Identification) tags in an environment that has been previously mapped by the designed software. Passive RFID tags are inexpensive, the same size and shape as a credit card and are attached to other objects. They don’t require any power of their own but operate through an RFID reader that induces a magnetic field in their antenna, which then creates power for the tag. This makes the tags inexpensive, and easy to setup and maintain. The robot is a three wheeled vehicle driven by two stepper motors and is controlled wirelessly through a PC. It has a third omniwheel at the front for maintaining the balance of the robot. Since the PC communicates continuously with the robot, all the major processing and data management can be done on the computer making the microcontroller much simpler and less expensive. Infrared distance sensors are placed around the robot to detect short range obstacles and a sonar sensor at the front can detect obstacles further away. This data is used so that the robot can avoid obstacles in its path between tags. An electronic compass is used to provide absolute orientation of the robot at all times to correct for errors in estimating the angle. A camera is attached to the front of the robot so that an operator can see the robot and manually control it if necessary. This could also be used to a video a certain environment from a robot’s perspective. The sensors and the RFID tags are all inexpensive, making the mass reproduction of the robot feasible and implementation practically possible for small firms. The RFID tags can be quickly attached or detached from an environment leaving no trace of their prior existence. A map can then be formed on a PC automatically by the robot through its detection of the tags. This makes the entire system very flexible and quick to set up, something that is needed in the present day as changes to buildings and factories become more common. There are still many improvements needed to improve the stability of the compass’s signal in the presence of magnetic fields, the stability of the wheels so that slippage is rare, and the range of the wireless signal and camera. A flexible, easily configurable moving robot could be used to serve fields such as the medical field by transporting goods within a hospital, or in factories where goods need to be transported between locations. Since the system is flexible, and maps can be formed quickly, the robot can fit in with the changes to an industry’s environment and requirements. However, since the robot can only move to approximately five metres from the computer controlling it and the inaccuracy in sensor data, it is not currently ready for industrial use. For example, the sensors are only placed at 6 orientations around the robot and so do not cover a full span of the robots area. These sensors also only detect a two dimensional plane around the robot so could not detect obscure objects that have a part sticking out at height higher than the sensors. Therefore, further work is needed to be done to make the robot reliable, safe, and fault-proof before it could be used in industry. Since the movement of the robot with inexpensive sensors and motors is bound to have problems in perfectly moving between RFID tags, due to small dead reckoning errors, simple algorithms are shown in this research that moves the robot around its current location until it finds a tag. These algorithms involve finding a path between two RFID tags that will make use of any tags in between them to localise itself and moving in a spiral to search for a tag when, due to odometry errors, it is not detected where it is expected to be. The robot built has demonstrated being able to navigate between RFID tags within a small lab environment. It has proven to be able to avoid obstacles with a size of 30 x 30 cm.