Brian Stephen Smith

Automatic generation of persistent formations for multi-agent networks under range constraints

In this paper we present a collection of graph-based methods for determining if a team of mobile robots, subjected to sensor and communication range constraints, can persistently achieve a specified formation. What we mean by this is that the formation, once achieved, will be preserved by the direct maintenance of the smallest subset of all possible pairwise inter-agent distances. In this context, formations are defined by sets of points separated by distances corresponding to desired inter-agent distances. Further, we provide graph operations to describe agent interactions that implement a given formation, as well as an algorithm that, given a persistent formation, automatically generates a sequence of such operations. Experimental results are presented that illustrate the operation of the proposed methods on real robot platforms.

Multi-robot deployment and coordination with Embedded Graph Grammars

This paper presents a framework for going from specifications to implementations of decentralized control strategies for multi-robot systems. In particular, we show how the use of Embedded Graph Grammars (EGGs) provides a tool for characterizing local interaction and control laws. This paper highlights some key implementation aspects of the EGG formalism, and develops and discusses experimental results for a hexapod-based multi-robot system, as well as a multi-robot system of wheeled robots.

Yield analysis and process modeling of low cost, high throughput flip chip assembly based on no-flow underfill materials

As a concept to achieve low-cost, high-throughput flip chip on board (FCOB) assembly, a new process has been developed implementing next generation flip chip processing based no-flow fluxing underfill materials. The low-cost, high throughput flip chip process implements large area underfill printing, integrated chip placement and underfill flow and simultaneous solder interconnect reflow and underfill cure. The goals of this study are to demonstrate feasibility of no flow underfill materials and the high throughput flip chip process over a range of flip chip configurations, identify the critical process variables affecting yield, analyze the yield of the high throughput flip chip process, and determine the impact of no-flow underfill materials on key process elements. Reported in this work is the assembly of a series of test vehicles to assess process yield and process defects. The test vehicles are assembled by depositing a controlled mass of underfill material on the chip site, aligning chip to the substrate pads, and placing the chip inducing a compression type underfill flow. The assemblies are reflowed in a commercial reflow furnace in an air atmosphere to simultaneously form the solder interconnects and cure the underfill. A series of designed experiments identify the critical process variables including underfill mass, reflow profile, placement velocity, placement force, and underfill material system. Of particular interest is the fact that the no-flow underfill materials studied exhibit an affinity for unique reflow profiles to minimize process defects.

QCA channel routing with wire crossing minimization

Quantum-dot Cellular Automata (QCA) is a novel computing mechanism that can represent binary information based on spatial distribution of electron charge configuration in chemical molecules. QCA layout is currently restricted to a single layer with very limited number of wire crossing permitted. Thus, wire crossing minimization is crucial in improving the manufacturability of QCA circuits. In this article, we present the first QCA channel routing algorithm for wire crossing minimization. Our channel routing algorithm is able to reduce crossings, where Left-Edge First, Yoshimura and Kuh, and topologically-based algorithms fail to do so.

A reliability and failure mode analysis of no flow underfill materials for low cost flip chip assembly

Development of new material systems will increase flip chip market growth provided they reduce manufacturing time and enhance reliability. Among these new materials are no flow underfills. Properly formulated, these underfills can significantly decrease manufacturing cost by eliminating the fluxing process, the underfill flow process, and the underfill cure process. This work evaluates the reliability of no-flow underfill materials and performs a critical failure mode analysis of flip chip structures using no flow underfills. Six test vehicles and four reliability tests were used to evaluate and analyze the reliability performance of several commercial no-flow underfill materials. Different test vehicles were used to evaluate the effect of varying chip size, interconnect density, pad surface finish metallization, and soldermask opening design. Accelerated reliability tests performed included liquid/liquid thermal shock (LLTS), air/air thermal cycling (AATC), moisture sensitivity preconditioning, and temperature humidity aging (TH). Materials tested in this work demonstrated the ability to survive 1000 cycles in LLTS and AATC without failure, 1000 hours of TH and level three preconditioning. A number of unique failure modes are identified including bulk underfill cracking, fillet cracking, solder interconnect fatigue cracking and underfill interfacial delamination.

QCA physical design with crossing minimization

Quantum-dot Cellular Automata (QCA) is a novel computing mechanism that can represent binary information based on spatial distribution of electron charge configuration in chemical molecules. QCA circuit layout is currently restricted to a single layer with very limited number of wire crossing permitted. Thus, wire crossing minimization is crucial in improving the manufacturability of QCA circuits. The focus of this paper is to develop the first QCA physical design algorithms for wire crossing minimization.

Automatic formation deployment of decentralized heterogeneous multi-robot networks with limited sensing capabilities

Heterogeneous multi-robot networks require novel tools for applications that require achieving and maintaining formations. This is the case for distributing sensing devices with heterogeneous mobile sensor networks. Here, we consider a heterogeneous multi-robot network of mobile robots. The robots have a limited range in which they can estimate the relative position of other network members. The network is also heterogeneous in that only a subset of robots have localization ability. We develop a method for automatically configuring the heterogeneous network to deploy a desired formation at a desired location. This method guarantees that network members without localization are deployed to the correct location in the environment for the sensor placement.

Automatic deployment and formation control of decentralized multi-agent networks

Novel tools are needed to deploy multi-agent networks in applications that require a high degree of accuracy in the achievement and maintenance of geometric formations. This is the case when deploying distributed sensing devices across large spatial domains. Through so-called embedded graph grammars (EGGs), this paper develops a method for automatically generating control programs that ensure that a multi-robot network is deployed according to the desired configuration. This paper presents a communication protocol needed for implementing and executing the control programs in an accurate and deadlock-free manner.

Persistent formation control of multi-robot networks

This paper presents a method for controlling formations of mobile robots. In particular, the problem of maintaining so-called ¿persistent formations¿ while moving the formation from one location to another is defined and investigated. A method for accomplishing such persistent formation motions is presented, and the method is demonstrated in simulation and with a prototype network of robots.

A Learning Approach to Enable Locomotion of Multiple Robotic Agents Operating in Natural Terrain Environments

Abstract This paper presents a methodology that utilizes soft computing approaches to enable locomotion of multiple legged robotic agents operating in natural terrain environrnents. For individual robotic control, the locomotion strategy consists of a hybrid FSM-GA approach that couples leg orientation states with a genetic algorithm to leazn necessary leg movement sequences. To achieve multi-agent formations, locomotion behavior is driven by using a trained neural network to extract relevant distance metrics necessary to realize desired robotic formations while operating in the field. These distance metrics aze then fed into local controllers for realizing lineaz and rotational velocity values for each robotic agent. Details of the methodology are discussed, and experimental results with a team of mobile robots aze presented.