John Oyekan

Mobile sensor networks for modelling environmental pollutant distribution

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.

Exploiting bacteria swarms for pollution mapping

Inspired by the simplicity of how nature solves its problems, we develop a flocking controller that would enable the localisation and subsequent mapping of environmental pollutants. Pollutants could range from checimal leaks to invisible air borne harzardous materials. We use simulation results to validate our approach and then briefly discuss how to implement the controller onto a real robotic platform. Our motivation is to use the advantages offered by swarm robotics- simple, multiple and cheap agents- to achieve a collective complex single goal of mapping an environmental pollutant spread over a large area. We aim to make our approach as simple as possible yet highly effective in generating the map.

A Novel Bio-Controller for Localizing Pollution Sources in a Medium Peclet Environment

Nature inspired solutions enable biological systems to adapt and accomplish their tasks in very noisy and uncertain environments. Taking inspiration from nature, a novel bacteria controller capable of finding the source of pollution in an underwater medium turbulent environment is presented in this paper. Experiments prove that the controller is capable of performing pollution source exploration, pollution plume transverse and source declaration in a medium Peclet environment without distinctively separating these three components as most researchers did. The results obtained from these experiments are considered as a step towards the deployment of robotic fish in a highly turbulent marine environment. Finally, a brief conclusion and future extension are presented.

Bacteria controller implementation on a physical platform for pollution monitoring

Inspired by the simplicity of how nature solves its problems, we implement a bacteria controller on a physical platform that would enable the localisation and subsequent mapping of environmental pollution. We investigate the effects of each parameter in the controller on the localisation and exploration ability of the platform used. We also present how we can tune the controller for a given environmental condition depending on whether localisation or exploration is of a major priority. Some experimental results are presented to show the feasibility and performance of the proposed bacteria control.

Toward bacterial swarm for environmental monitoring

Nature inspires many interesting ideas of solving our day to day problems. In this paper, we take inspiration from the chemotaxis behaviour of the bacterium and create a novel algorithm. Simulated results show the feasibility of using a bacterial inspired algorithm in finding a pollution source and then generating a visual 3D representation of the pollutant. We believe that by providing a visual 3D representation of an air pollutant for example, it could lead to an effective, timely and systematic evacuation of humans in a densely populated urban area.

Visual Imaging of Invisible Hazardous Substances Using Bacterial Inspiration

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.

Exploiting bacterial swarms for optimal coverage of dynamic pollutant profiles

Inspired by the simplicity of how nature solves its problems, an approach that would enable robotic agents form a visual representation of an invisible hazardous substance is presented. Such an approach would be very useful if emergency services need to systematically and strategically evacuate an area affected by invisible substance especially in a situation where evacuation resources are limited. The approach presented is computationally cheap and yet highly effective when compared to other methods such as Voronoi partitioning or simulated annealing. In addition, it does not require a polygon derived environment or a prior knowledge of the environment or pollutant and can be used to trace dynamically changing pollutant profiles. The experimental results prove that the algorithm presented in this work always converges to a pollutants distribution even though the pollutants profile is of a complex nature with no clear gradient boundaries.

K-Order Surrounding Roadmaps Path Planner for Robot Path Planning

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.

Biologically-inspired behaviour based robotics for making invisible pollution visible: a survey

Behaviour-based robotics is a paradigm that was proposed by Rodney Brooks in 1986. This paradigm proposes that robotic solutions should be developed by combining various reactive behaviours in an architecture. This survey overviews various biologically inspired source-seeking and multi-agent algorithms that could be combined in a behavior-based architecture towards providing visual concentration or profile information of invisible hazardous pollutants in dangerous environments. The need for this becomes even more necessary when hazardous substances that cannot be observed by human eyes are present in the environment. An example of such a scenario was the nuclear disaster caused by a Tsunami in Japan in 2011. Causalities from this disaster event could have been greatly reduced if the concentration profile of the resulting invisible pollution and radiation was made visible by using a swarm of flying microrobots.

A survey on assistive chair and related integrated sensing techniques

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.