• 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.
    • 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.
    • 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.