• Ant robotic swarm for visualizing invisible hazardous substances

      Oyekan, John O.; Hu, Huosheng (2013)
      Inspired by the simplicity of how nature solves its problems, this paper presents a novel approach that would enable a swarm of ant robotic agents (robots with limited sensing, communication, computational and memory resources) form a visual representation of distributed hazardous substances within an environment dominated by diffusion processes using a decentralized approach. Such a visual representation could be very useful in enabling a quicker evacuation of a city’s population affected by such hazardous substances. This is especially true if the ratio of emergency workers to the population number is very small.
    • Bio-inspired coverage of invisible hazardous substances in the environment

      Oyekan, John O.; Hu, Huosheng; Gu, Dongbing; University of Essex (World Scientific Publishing Company, 2010)
      Inspired by the simplicity of how nature solves its problems, a controller based upon the bacteria chemotaxis behavior and flocking of starlings in nature is developed and presented. It would enable the localization and subsequent mapping of pollutants in the environment. The pollutants could range from chemical leaks to invisible air borne hazardous materials. Simulation is used to explore the feasibility of the proposed controller and then a brief discussion on how to implement it onto a real robotic platform is presented. By using the advantages offered by swarm robotics, it is possible to achieve a collective mapping of an invisible pollutant spread over a large area. The approach presented is very simple, computational efficient, easily tuned and yet highly effective (desirable characteristics of biological systems) in generating a representation of an invisible pollutant.
    • K-order surrounding roadmaps path planner for robot path planning

      Li, Yueqiao; Li, Dayou; Maple, Carsten; Yue, Yong; Oyekan, John O. (Springer, 2014-09)
      Probabilistic roadmaps are commonly used in robot path planning. Most sampling-based path planners often produce poor-quality roadmaps as they focus on improving the speed of constructing roadmaps without paying much attention to the quality. Poor-quality roadmaps can cause problems such as poor-quality paths, time-consuming path searching and failures in the searching. This paper presents a K-order surrounding roadmap (KSR) path planner which constructs a roadmap in an incremental manner. The planner creates a tree while answering a query, selects the part of the tree according to quality measures and adds the part to an existing roadmap which is obtained in the same way when answering the previous queries. The KSR path planner is able to construct high-quality roadmaps in terms of good coverage, high connectivity, provision of alternative paths and small size. Comparison between the KSR path planner and Reconfigurable Random Forest (RRF), an existing incremental path planner, as well as traditional probabilistic roadmap (PRM) path planner shows that the roadmaps constructed using the KSR path planner have higher quality that those that are built by the other planners.
    • Mobile sensor networks for modelling environmental pollutant distribution

      Lu, Bowen; Oyekan, John O.; Gu, Dongbing; Hu, Huosheng; Nia, Hossein Farid Ghassem; University of Essex (Taylor and Francis, 2011)
      This article proposes to deploy a group of mobile sensor agents to cover a polluted region so that they are able to retrieve the pollutant distribution. The deployed mobile sensor agents are capable of making point observation in the natural environment. There are two approaches to modelling the pollutant distribution proposed in this article. One is a model-based approach where the sensor agents sample environmental pollutant, build up an environmental pollutant model and move towards the region where high density pollutant exists. The modelling technique used is a distributed support vector regression and the motion control technique used is a distributed locational optimising algorithm (centroidal Voronoi tessellation). The other is a model-free approach where the sensor agents sample environmental pollutant and directly move towards the region where high density pollutant exists without building up a model. The motion control technique used is a bacteria chemotaxis behaviour. By combining this behaviour with a flocking behaviour, it is possible to form a spatial distribution matched to the underlying pollutant distribution. Both approaches are simulated and tested with a group of real robots.
    • A survey on assistive chair and related integrated sensing techniques

      Lu, Hang; Li, Dayou; Oyekan, John O.; Maple, Carsten; University of Bedfordshire (IEEE, 2013-08)
      This paper presents a survey of the current approaches of sit-to-stand assistive chairs. Sitting in a chair and standing up from a seated position are common activities performed by humans on a daily basis. However, older people often encounter difficulties with these activities. The difficulties may cause substantial decreasing of the elderly mobility, leading to inactive participation in society and increasing the risk of chronic diseases that may cause premature death. Therefore, assisting older people to overcome these difficulties has significance for their independent living and active ageing. The assistive devices can be allocated in terms of market available ones and experimental prototypes. Both classes of these devices are discussed in this survey. We also discuss sensing techniques that are currently used with experimental prototypes in addition to those that could be used and build a high level taxonomy of sensing techniques. Following from this survey, a chair capable of delivering assistance-as-needed is proposed.
    • Visual imaging of invisible hazardous substances using bacterial inspiration

      Oyekan, John O.; Gu, Dongbing; Hu, Huosheng (2013)
      Providing a visual image of a hazardous substance such as nerve gas or nuclear radiation using multiple robotic agents could be very useful particularly when the substance is invisible. Such visual representation could show where the hazardous substance concentration is highest through the deployment of a higher density of robotic agents to that area enabling humans to avoid such areas. We present an algorithm that is capable of doing the aforementioned with very minimal cost when compared with other techniques such as Voronoi partition methods. Using a mathematical proof, we show that the algorithm would always converge to the distribution of a spatial quantity under investigation. The mathematical model of the bacterium as developed by Berg and Brown is used in this paper, and through simulations and physical experiments, we show that a controller based upon the model is capable of being used to visually represent an invisible spatial hazardous substance using simplistic agents with the future possibility of the same algorithm being used to track a rapidly changing spatiotemporal substance. We believe that the algorithm has this potential because of its low communication and computational needs.