• Adaptive bees algorithm : bioinspiration from honeybee foraging to optimize fuel economy of a semi-track air-cushion vehicle

      Xu, Shuo; Yu, Fan; Luo, Zhe; Ji, Ze; Pham, Duc Truong; Qiu, Renxi; Shanghai Jiao Tong University; Cardiff University (Oxford University Press, 2011-01-04)
      This interdisciplinary study covers bionics, optimization and vehicle engineering. Semi-track air-cushion vehicle (STACV) provides a solution to transportation on soft terrain, whereas it also brings a new problem of excessive fuel consumption. By mimicking the foraging behaviour of honeybees, the bioinspired adaptive bees algorithm (ABA) is proposed to calculate its running parameters for fuel economy optimization. Inherited from the basic algorithm prototype, it involves parallel-operated global search and local search, which undertake exploration and exploitation, respectively. The innovation of this improved algorithm lies in the adaptive adjustment mechanism of the range of local search (called ‘patch size’) according to the source and the rate of change of the current optimum. Three gradually in-depth experiments are implemented for 143 kinds of soils. First, the two optimal STACV running parameters present the same increasing or decreasing trend with soil parameters. This result is consistent with the terramechanics-based theoretical analysis. Second, the comparisons with four alternative algorithms exhibit the ABA's effectiveness and efficiency, and accordingly highlight the advantage of the novel adaptive patch size adjustment mechanism. Third, the impacts of two selected optimizer parameters to optimization accuracy and efficiency are investigated and their recommended values are thus proposed.
    • The development of a semi-autonomous framework for personal assistant robots - SRS Project

      Qiu, Renxi; Ji, Ze; Chivarov, N.; Arbeiter, Georg; Weisshardt, Florian; Rooker, M.; Lopez, R.; Kronreif, G.; Spanel, M.; Li, Dayou; et al. (IGI Global, 2013)
      SRS is a European research project for building robust personal assistant robots using ROS (Robotic Operating System) and Care-O-bot (COB) 3 as the demonstration platform. A semi-autonomous framework has been developed in the project. It consists of an autonomous control structure and user interfaces that support the semi-autonomous operation. The control structure is divided into two parts. First, it has an automatic task planner, which initialises actions on the symbolic level. The planner produces proactive robotic behaviours based on updated semantic knowledge. Second, it has an action executive for coordination actions at the level of sensing and actuation. The executive produces reactive behaviours in well-defined domains. The two parts are integrated by fuzzy logic based symbolic grounding. As a whole, they represent the framework for autonomous control. Based on the framework, SRS user interfaces are integrated on top of COB’s existing capabilities to enable robust fetch and carry in unstructured environments.
    • Integrating robot task planner with common-sense knowledge base to improve the efficiency of planning

      Al-Moadhen, Ahmed; Qiu, Renxi; Packianather, Michael; Ji, Ze; Setchi, Rossi; Cardiff University (Elsevier, 2013)
      This paper presents a developed approach for intelligently generating symbolic plans by mobile robots acting in domestic environments, such as offices and houses. The significance of the approach lies in developing a new framework that consists of the new modeling of high-level robot actions and then their integration with common-sense knowledge in order to support a robotic task planner. This framework will enable interactions between the task planner and the semantic knowledge base directly. By using common-sense domain knowledge, the task planner will take into consideration the properties and relations of objects and places in its environment, before creating semantically related actions that will represent a plan. This plan will accomplish the user order. The robot task planner will use the available domain knowledge to check the next related actions to the current one and the action's conditions met will be chosen. Then the robot will use the immediately available knowledge information to check whether the plan outcomes are met or violated.
    • Integration of symbolic task planning into operations within an unstructured environment

      Qiu, Renxi; Noyvirt, Alexandre; Ji, Ze; Soroka, Anthony; Li, Dayou; Liu, Beisheng; Arbeiter, Georg; Weisshardt, Florian; Xu, Shuo (IGI Global, 2012)
      To ensure a robot capable of robust task execution in unstructured environments, task planners need to have a high-level understanding of the nature of the world, reasoning for deliberate actions, and reacting to environment changes. Proposed is a practical task planning approach that seamlessly integrating deeper domain knowledge, real time perception and symbolic planning for robot operation. A higher degree of autonomy under unstructured environment will be endowed to the robot with the proposed approach.
    • Robotic nanoassembly: current developments and challenges

      Wang, Zuobin; Li, Dayou; Zhang, Jin; Ji, Ze; Qiu, Renxi (Inderscience, 2011)
      Robotic nanoassembly is an emerging field that deals with the controlled manipulation, handling and assembly of atoms, molecules and nano objects by robots for manufacturing of nano structures, devices and systems. Nanoassembly is expected to have revolutionary applications in almost all the scientific and technological areas. This paper presents a general review of nanoassembly by robots considering its current developments and challenges. It discusses scanning probe-based 2D nanomanipulation, gripper-based 3D nanohandling, object-oriented nanoassembly and hybrid nanoassembly techniques, which are the main topics of interest in the field. The challenging issues in robotic nanoassembly are outlined together with the topics.
    • Towards automated task planning for service robots using semantic knowledge representation

      Ji, Ze; Qiu, Renxi; Noyvirt, Alex; Soroka, Anthony; Packianather, Michael; Setchi, Rossi; Li, Dayou; Xu, Shuo; Cardiff University; University of Bedfordshire; et al. (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2012)
      Automated task planning for service robots faces great challenges in handling dynamic domestic environments. Classical methods in the Artificial Intelligence (AI) area mostly focus on relatively structured environments with fewer uncertainties. This work proposes a method to combine semantic knowledge representation with classical approaches in AI to build a flexible framework that can assist service robots in task planning at the high symbolic level. A semantic knowledge ontology is constructed for representing two main types of information: environmental description and robot primitive actions. Environmental knowledge is used to handle spatial uncertainties of particular objects. Primitive actions, which the robot can execute, are constructed based on a STRIPS-style structure, allowing a feasible solution (an action sequence) for a particular task to be created. With the Care-O-Bot (CoB) robot as the platform, we explain this work with a simple, but still challenging, scenario named “get a milk box”. A recursive back-trace search algorithm is introduced for task planning, where three main components are involved, namely primitive actions, world states, and mental actions. The feasibility of the work is demonstrated with the CoB in a simulated environment.