Heterogeneous Self-Reconfiguring Robotics: Ph.D. Thesis Proposal

Self-reconfiguring robots are modular systems that can change shape, or reconfigure, to match structure to task. They comprise many small, discrete, often identical modules that connect together and that are minimally actuated. Global shape transformation is achieved by composing local motions. Systems with a single module type, known as homogeneous systems, gain fault tolerance, robustness and low production cost from module interchangeability. However, we are interested in heterogeneous systems, which include multiple types of modules such as those with sensors, batteries or wheels. We believe that heterogeneous systems offer the same benefits as homogeneous systems with the added ability to match not only structure to task, but also capability to task. Although significant results have been achieved in understanding homogeneous systems, research in heterogeneous systems is challenging as key algorithmic issues remain unexplored. We propose in this thesis to investigate questions in four main areas: 1) how to classify heterogeneous systems, 2) how to develop efficient heterogeneous reconfiguration algorithms with desired characteristics, 3) how to characterize the complexity of key algorithmic problems, and 4) how to apply these heterogeneous algorithms to perform useful new tasks in simulation and in the physical world. Our goal is to develop an algorithmic basis for heterogeneous systems. This has theoretical significance in that it addresses a major open problem in the field, and practical significance in providing self-reconfiguring robots with increased capabilities

Parallel Architectures for Planetary Exploration Requirements (PAPER)

The Parallel Architectures for Planetary Exploration Requirements (PAPER) project is essentially research oriented towards technology insertion issues for NASA's unmanned planetary probes. It was initiated to complement and augment the long-term efforts for space exploration with particular reference to NASA/LaRC's (NASA Langley Research Center) research needs for planetary exploration missions of the mid and late 1990s. The requirements for space missions as given in the somewhat dated Advanced Information Processing Systems (AIPS) requirements document are contrasted with the new requirements from JPL/Caltech involving sensor data capture and scene analysis. It is shown that more stringent requirements have arisen as a result of technological advancements. Two possible architectures, the AIPS Proof of Concept (POC) configuration and the MAX Fault-tolerant dataflow multiprocessor, were evaluated. The main observation was that the AIPS design is biased towards fault tolerance and may not be an ideal architecture for planetary and deep space probes due to high cost and complexity. The MAX concepts appears to be a promising candidate, except that more detailed information is required. The feasibility for adding neural computation capability to this architecture needs to be studied. Key impact issues for architectural design of computing systems meant for planetary missions were also identified

Evaluating a computational support tool for set-based configuration of production systems:Results from an industrial case

This paper describes research conducted in the context of an industrial case dealing with the design of re configurable cellular manufacturing systems. Reconfiguring such systems represents a complex task due to the interdependences between the constituent subsystems. A novel computational tool was developed to support the production engineers in (sub) system configuration by enabling to consider multiple alternative configurations simultaneously. The tool was tested by applying it in two realistic system engineering problems and conducting interviews to evaluate its effects. The prototype was found to be an effective and efficient approach to support exploring evaluating and selecting sets of system configurations. The findings suggest that the approach is applicable in practice and represents a means to strategically leverage the flexibility in production system design as well as to improve the efficiency of the engineering process. Hence further research could examine if the approach is useful in additional systems engineering domains

Optimizing for confidence - Costs and opportunities at the frontier between abstraction and reality

Is there a relationship between computing costs and the confidence people place in the behavior of computing systems? What are the tuning knobs one can use to optimize systems for human confidence instead of correctness in purely abstract models? This report explores these questions by reviewing the mechanisms by which people build confidence in the match between the physical world behavior of machines and their abstract intuition of this behavior according to models or programming language semantics. We highlight in particular that a bottom-up approach relies on arbitrary trust in the accuracy of I/O devices, and that there exists clear cost trade-offs in the use of I/O devices in computing systems. We also show various methods which alleviate the need to trust I/O devices arbitrarily and instead build confidence incrementally "from the outside" by considering systems as black box entities. We highlight cases where these approaches can reach a given confidence level at a lower cost than bottom-up approaches.Comment: 11 pages, 1 figur

Department of Computer Science Activity 1998-2004

This report summarizes much of the research and teaching activity of the Department of Computer Science at Dartmouth College between late 1998 and late 2004. The material for this report was collected as part of the final report for NSF Institutional Infrastructure award EIA-9802068, which funded equipment and technical staff during that six-year period. This equipment and staff supported essentially all of the department\u27s research activity during that period