Mobile Interface for a Smart Wheelchair

Smart wheelchairs are designed for severely motor impaired people that have difficulties to drive standard -manual or electric poweredwheelchairs. Their goal is to automate driving tasks as much as possible in order to minimize user intervention. Nevertheless, human involvement is still necessary to maintain high level task control. Therefore in the interface design it is necessary to take into account the restrictions imposed by the system (mobile and small), by the type of users (people with severe motor restrictions) and by the task (to select a destination among a number of choices in a structured environment). This paper describes the structure of an adaptive mobile interface for smart wheelchairs that is driven by the context.Comisión Interministerial de Ciencia y Tecnología TER96-2056-C02-0

Collaborative signal and information processing for target detection with heterogeneous sensor networks

In this paper, an approach for target detection and acquisition with heterogeneous sensor networks through strategic resource allocation and coordination is presented. Based on sensor management and collaborative signal and information processing, low-capacity low-cost sensors are strategically deployed to guide and cue scarce high performance sensors in the network to improve the data quality, with which the mission is eventually completed more efficiently with lower cost. We focus on the problem of designing such a network system in which issues of resource selection and allocation, system behaviour and capacity, target behaviour and patterns, the environment, and multiple constraints such as the cost must be addressed simultaneously. Simulation results offer significant insight into sensor selection and network operation, and demonstrate the great benefits introduced by guided search in an application of hunting down and capturing hostile vehicles on the battlefield

Adaptive Path Planning for Depth Constrained Bathymetric Mapping with an Autonomous Surface Vessel

This paper describes the design, implementation and testing of a suite of algorithms to enable depth constrained autonomous bathymetric (underwater topography) mapping by an Autonomous Surface Vessel (ASV). Given a target depth and a bounding polygon, the ASV will find and follow the intersection of the bounding polygon and the depth contour as modeled online with a Gaussian Process (GP). This intersection, once mapped, will then be used as a boundary within which a path will be planned for coverage to build a map of the Bathymetry. Methods for sequential updates to GP's are described allowing online fitting, prediction and hyper-parameter optimisation on a small embedded PC. New algorithms are introduced for the partitioning of convex polygons to allow efficient path planning for coverage. These algorithms are tested both in simulation and in the field with a small twin hull differential thrust vessel built for the task.Comment: 21 pages, 9 Figures, 1 Table. Submitted to The Journal of Field Robotic

Particle Swarm Optimization Based Source Seeking

Signal source seeking using autonomous vehicles is a complex problem. The complexity increases manifold when signal intensities captured by physical sensors onboard are noisy and unreliable. Added to the fact that signal strength decays with distance, noisy environments make it extremely difficult to describe and model a decay function. This paper addresses our work with seeking maximum signal strength in a continuous electromagnetic signal source with mobile robots, using Particle Swarm Optimization (PSO). A one to one correspondence with swarm members in a PSO and physical Mobile robots is established and the positions of the robots are iteratively updated as the PSO algorithm proceeds forward. Since physical robots are responsive to swarm position updates, modifications were required to implement the interaction between real robots and the PSO algorithm. The development of modifications necessary to implement PSO on mobile robots, and strategies to adapt to real life environments such as obstacles and collision objects are presented in this paper. Our findings are also validated using experimental testbeds.Comment: 13 pages, 12 figure

GAOS: Spatial optimisation of crop and nature within agricultural fields

This paper proposes and demonstrates a spatial optimiser that allocates areas of inefficient machine manoeuvring to field margins thus improving the use of available space and supporting map-based Controlled Traffic Farming. A prototype web service (GAOS) allows farmers to optimise tracks within their fields and explore planning alternatives prior to downloading the plans to their RTK GPS-guided steering system. GAOS retrieves accurate data on field geometry from a geo-database. Via a web interface, the farmer sets options regarding operation properties, potential locations for field margins and headlands, etc. Next, an optimisation script that employs an open source geospatial library (osgeo.ogr) is called. The objective function considers costs involved with un-cropped areas, turning at headlands and subsidies received for field margins. Optimisation results are stored in a database and are available for (1) viewing via the web interface, (2) downloading to the GPS-guided steering system and (3) communication to third parties