Elastokinetics modeling and characteristic analysis of the parallel robot mechanism

This paper focuses a new 4-UPS-RPS five degree of freedom (DOF) spatial parallel robot mechanism with independent intellectual property rights obtained. Based on KED method and together with finite element method, Lagrange equation and substructure modeling method, the elastokinetics analytical model of this parallel robot mechanism is established under the ideal situation. Subsequently, the research results, such as elastokinetics model, stress and frequency characteristic analysis, are obtained. Combined with typical examples, key design parameters which significantly influence the dynamic characteristics of the system, are explicated. The work done in this paper lays a solid foundation for the dynamic optimum design of parallel robot mechanism and the physical prototype development

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

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

Modelling, Control and Optimization of Modular Reconfigurable Robots

Modular reconfigurable robots are robotic systems offering new opportunities to rapidly create fit-to-task flexible automation lines. The recent trends of increasingly varying market needs in low-volume high-mix manufacturing demands for highly adaptable robotic systems like this. In this context, methods for quickly and automatically generating a modular robot model and controller should be developed. Moreover, modularity and reconfigurabilty open up new opportunities for on-demand robot morphology optimization for varying tasks. Therefore a method to optimize the robot design for a certain criterion should be provided in order to exploit the full potential of reconfigurable robots. In this thesis, a complete hard- and software architecture for a modular reconfigurable EtherCAT-based robot is presented. This novel approach allows to automatically reconstruct the topology of different robot structures, composed of a set of body modules, each of which represents an EtherCAT slave. This approach enables to obtain in an automatic way the kinematic and dynamic model of the robot and store it in URDF format as soon as the physical robot is assembled or reconfigured. The method also automatically reshapes a generic optimization-based controller to be instantly used after reconfiguration. Finally, a study and analysis on how to find the best suited reconfigurable robot morphology for a given task are presented, starting from a fixed set of joint and link modules. In particular, is shown how exploiting multi-arm robotic systems and modifying the relative and absolute positions of their bases, can expand the solution space for a given task. Results obtained in simulations for different tasks, are verified with real-world experiments using a in-house developed reconfigurable robot prototype

Model based kinematic & dynamic simulation of 6-DOF upper-limb rehabilitation robot

Globally, a large population is suffering from motor disabilities caused by acute lesions to brain nervous system. One example is stroke, which is the third largest killer in New Zealand and the United States. Traditional manual therapy usually requires cooperative and intensive efforts from therapists and patients. Robot-assisted upper-limb rehabilitation techniques have been actively researched in the past few decades. However, limitations still exist such as inappropriate robotic modelling, mechanical design or limited Range of Motion (ROM). This paper proposes a mathematical model for a 6-Degree of Freedom (DOF) Universal Robot to be used in a rehabilitation system. This study focuses on the kinematics and dynamic analysis by using the Denavit-Hartenberg (D-H) parameters method with coordinate transformation theory. In order to simplify the computation process, Kane equation method is introduced in this paper. Simulation results show that the proposed model is correct although the fluctuation is possible to be reduced further. It concludes that the mathematical model can provide an intuitive and effective environment for designing the rehabilitation robot and planning the clinical trials

Model based kinematic & dynamic simulation of 6-DOF upper-limb rehabilitation robot

Globally, a large population is suffering from motor disabilities caused by acute lesions to brain nervous system. One example is stroke, which is the third largest killer in New Zealand and the United States. Traditional manual therapy usually requires cooperative and intensive efforts from therapists and patients. Robot-assisted upper-limb rehabilitation techniques have been actively researched in the past few decades. However, limitations still exist such as inappropriate robotic modelling, mechanical design or limited Range of Motion (ROM). This paper proposes a mathematical model for a 6-Degree of Freedom (DOF) Universal Robot to be used in a rehabilitation system. This study focuses on the kinematics and dynamic analysis by using the Denavit-Hartenberg (D-H) parameters method with coordinate transformation theory. In order to simplify the computation process, Kane equation method is introduced in this paper. Simulation results show that the proposed model is correct although the fluctuation is possible to be reduced further. It concludes that the mathematical model can provide an intuitive and effective environment for designing the rehabilitation robot and planning the clinical trials

Dynamics and controls for robot manipulators with open and closed kinematic chain mechanisms

This dissertation deals with dynamics and controls for robot manipulators with open and closed kinematic chain mechanisms;Part I of this dissertation considers the problem of designing a class of robust algorithms for the trajectory tracking control of unconstrained single robot manipulator. The general control structure consists of two parts: The nominal control laws are first introduced to stabilize the system in the absence of uncertainties, then a class of robust nonlinear control laws are adopted to compensate for both the structured uncertainties and the unstructured uncertainties by using deterministic approach. The possible upper bounds of uncertainties are assumed to be known for the nonadaptive version of robust nonlinear controls. If information on these bounds is not available, then the adaptive bound of the robust controller is presented to overcome possible time-varying uncertainties (i.e., decentralized adaptive control scheme);Part II of the dissertation presents the efficient methodology of formulating system dynamics and hybrid position/force control for a single robot manipulator under geometric end-effector constraints. In order to facilitate dynamic analysis and control synthesis, the original joint-space dynamics (or a set of DAEs) is transformed into the constraint-space model through nonlinear transformations. Using the transformed dynamic model, a class of hybrid control laws are presented to manipulate the position and contact force at the end-effector simultaneously and accurately: the modified computed torque method, the robust adaptive controller, and the adaptive hybrid impedance controller;Part III of the dissertation deals with a mathematical modeling and coordinated control of multiple robot manipulators holding and transporting a rigid common object on the constraint surfaces. First, the kinematics and dynamics of multiple robot systems containing the closed-chain mechanisms are formulated from a unified viewpoint. After a series of model transformations, a new combined dynamic model is derived for dynamic analysis and control synthesis. Next, a class of hybrid position/force controllers are developed. The control laws can be used to simultaneously control the position of the object along the constraint surfaces and the contact forces (the internal grasping forces and the external constraint forces)

Robot Mapping and Navigation in Real-World Environments

Robots can perform various tasks, such as mapping hazardous sites, taking part in search-and-rescue scenarios, or delivering goods and people. Robots operating in the real world face many challenges on the way to the completion of their mission. Essential capabilities required for the operation of such robots are mapping, localization and navigation. Solving all of these tasks robustly presents a substantial difficulty as these components are usually interconnected, i.e., a robot that starts without any knowledge about the environment must simultaneously build a map, localize itself in it, analyze the surroundings and plan a path to efficiently explore an unknown environment. In addition to the interconnections between these tasks, they highly depend on the sensors used by the robot and on the type of the environment in which the robot operates. For example, an RGB camera can be used in an outdoor scene for computing visual odometry, or to detect dynamic objects but becomes less useful in an environment that does not have enough light for cameras to operate. The software that controls the behavior of the robot must seamlessly process all the data coming from different sensors. This often leads to systems that are tailored to a particular robot and a particular set of sensors. In this thesis, we challenge this concept by developing and implementing methods for a typical robot navigation pipeline that can work with different types of the sensors seamlessly both, in indoor and outdoor environments. With the emergence of new range-sensing RGBD and LiDAR sensors, there is an opportunity to build a single system that can operate robustly both in indoor and outdoor environments equally well and, thus, extends the application areas of mobile robots. The techniques presented in this thesis aim to be used with both RGBD and LiDAR sensors without adaptations for individual sensor models by using range image representation and aim to provide methods for navigation and scene interpretation in both static and dynamic environments. For a static world, we present a number of approaches that address the core components of a typical robot navigation pipeline. At the core of building a consistent map of the environment using a mobile robot lies point cloud matching. To this end, we present a method for photometric point cloud matching that treats RGBD and LiDAR sensors in a uniform fashion and is able to accurately register point clouds at the frame rate of the sensor. This method serves as a building block for the further mapping pipeline. In addition to the matching algorithm, we present a method for traversability analysis of the currently observed terrain in order to guide an autonomous robot to the safe parts of the surrounding environment. A source of danger when navigating difficult to access sites is the fact that the robot may fail in building a correct map of the environment. This dramatically impacts the ability of an autonomous robot to navigate towards its goal in a robust way, thus, it is important for the robot to be able to detect these situations and to find its way home not relying on any kind of map. To address this challenge, we present a method for analyzing the quality of the map that the robot has built to date, and safely returning the robot to the starting point in case the map is found to be in an inconsistent state. The scenes in dynamic environments are vastly different from the ones experienced in static ones. In a dynamic setting, objects can be moving, thus making static traversability estimates not enough. With the approaches developed in this thesis, we aim at identifying distinct objects and tracking them to aid navigation and scene understanding. We target these challenges by providing a method for clustering a scene taken with a LiDAR scanner and a measure that can be used to determine if two clustered objects are similar that can aid the tracking performance. All methods presented in this thesis are capable of supporting real-time robot operation, rely on RGBD or LiDAR sensors and have been tested on real robots in real-world environments and on real-world datasets. All approaches have been published in peer-reviewed conference papers and journal articles. In addition to that, most of the presented contributions have been released publicly as open source software