Zachary B. Rubinstein

Platoon-based self-scheduling for real-time traffic signal control

In this paper, we take a self-scheduling approach to solving the traffic signal control problem, where each intersection is controlled by a self-interested agent operating with a limited (fixed horizon) view of incoming traffic. Central to the approach is a representation that aggregates incoming vehicles into critical clusters, based on the non-uniformly distributed nature of road traffic flows. Starting from a recently developed signal timing strategy based on clearing anticipated queues, we propose extended real-time decision policies that also incorporate look-ahead of approaching vehicle platoons, and thus focus attention more on keeping vehicles moving than on simply clearing queues. We present simulation results that demonstrate the benefit of our approach over simple queue clearing, both in promoting the establishment of “green waves” where vehicles move through the road network without stopping and in improving overall traffic flows.

Mixed-initiative management of dynamic business processes

Managing and participating in complex, dynamic business processes is difficult due to their inherent uncertainty, which undermines the predictability necessary for efficient planning and execution. Effective management of these processes hinges on the ability of the manager to recognize unanticipated difficulties in the process execution, determine the causes of the anomalies, and implement remedies. Current process-management approaches respond reactively to process dynamics, if they deal with them at all. In this paper, we present the ProME process-management environment, focusing on how human process managers and participants interact with a dynamic, online model of executing dynamic processes to proactively manage and operate in dynamic business processes. We show how having the best information available about a process and its future can provide managers with the time needed to detect and understand impending process anomalies and to develop and implement effective interventions. Furthermore, we show enabling managers how to update the model of executing processes and having the effects of those modifications to be pushed to the relevant participants reduces the time it takes to implement remedies, ProME was used in a commercial product for managing design processes in the automotive and aerospace industries.

Mobile manufacturing of large structures

Assembly of large structures requires large fixtures, often referred to as monuments. Their cost and massive size limit flexibility and scalability of the manufacturing process. Numerous small mobile robots can replace these large structures and, therefore, replicate the efficiency of the assembly line with far more flexibility. An assembly line made up of mobile manipulators can easily and rapidly be reconfigured to support scalability and a varied product mix, while allowing for near optimal resource assignment. The challenge to using small robots in place of monuments is making their joint behavior precise enough to accomplish the task and efficient enough to execute subtasks in a reasonable period of time. In this paper, we describe a set of techniques that we combine to achieve the necessary precision and overall efficiency to build a large structure. We describe and demonstrate these techniques in the context of a testbed we implemented for assembling a wing ladder.

Robust allocation of RF device capacity for distributed spectrum functions

Real-time awareness of radio spectrum use across frequency, geography and time is crucial to effective communications and information gathering in congested airway environments, yet acquiring this awareness presents a challenging sensing and data integration problem. A recent proposal has argued that real-time generation of spectrum usage maps might be possible through the use of existing radios in the area of interest, by exploiting their sensing capacity when they are not otherwise being used. In this paper, we assume this approach and consider the task allocation problem that it presents. We focus specifically on the development of a network-level middleware for task management, that assigns resources to prospective mapping applications based on a distributed model of device availability, and allows mapping applications (and other related RF applications) to specify what is required without worrying about how it will be accomplished. A distributed, auction-based framework is specified for task assignment and coordination, and instantiated with a family of minimum set cover algorithms for addressing “coverage” tasks. An experimental analysis is performed to investigate and quantify two types of performance benefits: (1) the basic advantage gained by exploiting knowledge of device availability, and (2) the additional advantage gained by adding redundancy in subregions where the probability of availability of assigned devices is low. To assess the effectiveness of our minimum set cover algorithms, we compute optimal solutions to a static version of the real-time coverage problem and compare performance of the algorithms to these upper bound solutions.