Across many consumer product industries, the prevailing practice is to design families of product variants that exploit commonality to provide the ability to easily customize a base platform for particular uses and to take advantage of commonality for streamlining design, manufacturing, maintenance and logistic; examples include Black & Decker, Seagate, and Volkswagen. This paper describes the application of product family concepts to the design and development of a family of robots to satisfy requirements for explosive ordnance disposal. To facilitate this process, we have developed a market segmentation grid that plots the desired capabilities and cost versus the target use cases. The product family design trade space is presented using a multi-dimensional trade space visualization tool which helps identify dependencies between different design variables and identify Pareto frontiers along which optimal design choices will lie. The EOD robot product family designs share common components and subsystems yet are modularized and scalable to provide functionality to satisfy a range of user requirements. This approach has been shown to significantly reduce development time and costs, manufacturing costs, maintenance and spare parts inventory, and operator and maintainer training.
This paper describes recent efforts to develop integrated multi-sensor payloads for small robotic platforms for improved
operator situational awareness and ultimately for greater robot autonomy. The focus is on enhancements to perception
through integration of electro-optic, acoustic, and other sensors for navigation and inspection. The goals are to provide
easier control and operation of the robot through fusion of multiple sensor outputs, to improve interoperability of the
sensor payload package across multiple platforms through the use of open standards and architectures, and to reduce
integration costs by embedded sensor data processing and fusion within the sensor payload package.
The solutions investigated in this project to be discussed include: improved capture, processing and display of sensor
data from multiple, non-commensurate sensors; an extensible architecture to support plug and play of integrated sensor
packages; built-in health, power and system status monitoring using embedded diagnostics/prognostics; sensor payload
integration into standard product forms for optimized size, weight and power; and the use of the open Joint Architecture
for Unmanned Systems (JAUS)/ Society of Automotive Engineers (SAE) AS-4 interoperability standard.
This project is in its first of three years. This paper will discuss the applicability of each of the solutions in terms of its
projected impact to reducing operational time for the robot and teleoperator.
Advances in information technology enable a capability to gather large amounts of raw data from all parts of our society to a degree of efficiency that can actually encumber surveillance missions. Today one can create automated networks of sensors to gather enormous volumes of data, but we do not have the human capacity to sort through all this raw data. Similar problems exist in the area of machinery condition or health monitoring - another surveillance problem. By automating data collection, processing, fusion and interpretation, one can bring the most relevant and timely information to human analysts, planners, and responders. Key technologies that enable this transformation from data to knowledge are distributed hardware and software architectures, intelligent sensors, data fusion and reasoning algorithms, and open system architectures for information exchange. Distributed hardware and software structures partition complex systems into a collection of interconnected subsystems. Intelligent sensors enable this conversion of data to information by processing massive amounts of data at the subsystem and higher levels to extract contextually relevant information. The transformation from raw sensor data to useful information requires the application of subsystem-specific signal processing and feature extraction algorithms, data fusion, and classification algorithms to combine data and features from commensurate and non-commensurate sensors or information sources. Emerging standards in open system architectures for condition based maintenance apply equally to surveillance systems and condition monitoring systems. Examples from fielded system health monitoring applications are presented along with their parallels to surveillance systems with application to homeland defense.