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Abstract-For certain applications, field robotic systems require small size for cost, weight, access, stealth or other reasons. Small size results in constraints on critical resources such as power, space (for sensors and actuators), and computing cycles, but these robots still must perform many of the challenging tasks of their larger brethren. The need for advanced capabilities such as machine vision, applicationspecific sensing, path planning, self localization, etc. is not reduced by small-scale applications, but needs may vary with the task. As a result, when resources are constrained, it is prudent to configure the robot for the task at hand; both hardware and software. We are developing a reconfigurable computing subsystem for resource-constrained robots that allows rapid deployment of statically configured hardware and software for a specific task. The use of a Field Programmable Gate Array (FPGA) provides flexibility in hardware for both sensor interfacing and hardware-accelerated computation. In this paper, we describe a static reconfiguration architecture we call the Morphing Bus that allows the rapid assembly of sensors and dedicated computation through reusable hardware and software modules. It is a novel sensor bus in the fact that no bus interface circuitry is required on the sensor side -the bus "morphs" to accommodate the signals of the sensor.

Much work has been undertaken recently toward the development of low-power, high-performance sensor networks. There are many static remote sensing applications for which this is appropriate. The focus of this development effort is applications that require higher performance computation, but still involve severe constraints on power and other resources. Toward that end, we are developing a reconfigurable computing platform for miniature robotic and humandeployed sensor systems composed of several mobile nodes. The system provides static and dynamic reconfigurability for both software and hardware by the combination of CPU (central processing unit) and FPGA (field-programmable gate array) allowing on-the-fly reprogrammability. Static reconfigurability of the hardware manifests itself in the form of a "morphing bus" architecture that permits the modular connection of various sensors with no bus interface logic. Dynamic hardware reconfigurability provides for the reallocation of hardware resources at run-time as the mobile, resource-constrained nodes encounter unknown environmental conditions that render various sensors ineffective.This computing platform will be described in the context of work on chemical/biological/radiological plume tracking using a distributed team of mobile sensors. The objective for a dispersed team of ground and/or aerial autonomous vehicles (or hand-carried sensors) is to acquire measurements of the concentration of the chemical agent from optimal locations and estimate its source and spread. This requires appropriate distribution, coordination and communication within the team members across a potentially unknown environment. The key problem is to determine the parameters of the distribution of the harmful agent so as to use these values for determining its source and predicting its spread. The accuracy and convergence rate of this estimation process depend not only on the number and accuracy of the sensor measurements but also on their spatial distribution over time (the sampling strategy). For the safety of a humandeployed distribution of sensors, optimized trajectories to minimize human exposure are also of importance.The systems described in this paper are currently being developed. Parts of the system are already in existence and some results from these are described.

Abstract-Small robots can be beneficial to important applications such as civilian search and rescue and military surveillance, but their limited resources constrain their functionality and performance. To address this, a reconfigurable technique based on field-programmable gate arrays (FPGAs) may be applied, which has the potential for greater functionality and higher performance, but with smaller volume and lower power dissipation. This project investigates an FPGA-based PID motion control system for small, self-adaptive systems. For one channel control, parallel and serial architectures for the PID control algorithm are designed and implemented. Based on these onechannel designs, four architectures for multiple-channel control are proposed and two channel-level serial (CLS) architectures are designed and implemented. Functional correctness of all the designs was verified in motor control experiments, and area, speed, and power consumption were analyzed. The tradeoffs between the different designs are discussed in terms of area, power consumption, and execution time with respect to number of channels, sampling rate, and control clock frequency. The data gathered in this paper will be leveraged in future work to dynamically adapt the robot at run time.

This study suggested combined filtering model to eliminate outlier travel time data in transportation information system, and it was based on Median Absolute Deviation and Voting Rule. This model applied Median Absolute Deviation (MAD) method to follow normal distribution as first filtering process. After that, Voting rule is applied to eliminate remaining outlier travel time data after Median Absolute Deviation. In Voting Rule, travel time samples are judged as outliers according to travel-time difference between sample data and mean data. Elimination or not of outliers are determined using a majority rule. In case study of national highway No. 3, combined filtering model selectively eliminated outliers only and could improve accuracy of estimated travel time.