Weighted feature selection criteria for visual servoing of a telerobot

Because of the continually changing environment of a space station, visual feedback is a vital element of a telerobotic system. A real time visual servoing system would allow a telerobot to track and manipulate randomly moving objects. Methodologies for the automatic selection of image features to be used to visually control the relative position between an eye-in-hand telerobot and a known object are devised. A weighted criteria function with both image recognition and control components is used to select the combination of image features which provides the best control. Simulation and experimental results of a PUMA robot arm visually tracking a randomly moving carburetor gasket with a visual update time of 70 milliseconds are discussed

Seasonal trends in air temperature and precipitation in IPCC AR4 GCM output for Kansas, USA: evaluation and implications

Understanding the impacts of future climate change in Kansas is important for agricultural and other socioeconomic sectors in the region. To quantify these impacts, seasonal trends in air temperature and precipitation patterns from decadally averaged monthly output of 21 global climate models under the Special Report on Emissions Scenarios A1B scenario used in the Intergovernmental Panel of Climate Change Assessment Report 4 are examined for six grid cells representing Kansas. To ascertain the performance of the models, we compared model output to kriged meteorological data from stations in the Global Historical Climate Network for the period from 1950 to 2000. Agreement between multimodel ensemble mean output and observations is very good for temperature (r2 all more than 0.99, root mean square errors range from 0.84 to 1.48°C) and good for precipitation (r2 ranging between 0.64 and 0.89, root mean square errors range from 322 to 1144 mm). Seasonal trends for the second half of the 20th century are generally not observed except in modelled temperature trends. Linear trends for the 21st century are significant for all seasons in all grid cells for temperature and many for precipitation. Results indicate that temperatures are likely to warm in all seasons, with the largest trends being on the order of 0.04 °C/year in summer and fall. Precipitation is likely to increase slightly in winter and decrease in summer and fall. These changes have profound implications for both natural ecosystems and agricultural land uses in the region. Copyright 2009 Royal Meteorological SocietyLand Institute Climate and Energy Project (NFP #49780-720) and the National Science Foundation EPSCoR program (NSF EPS #0553722

Micro-grippers for assembly of LIGA parts

This paper describes ongoing testing of two microgrippers for assembly of LIGA (Lithographie Galvanoformung Abformung) parts. The goal is to place 100 micron outside diameter (OD) LIGA gears with a 50 micron inner diameter hole onto pins ranging from 35 to 49 microns. The first micro gripper is a vacuum gripper made of a 100 micron OD stainless steel tube. The second micro gripper is a set of tweezers fabricated using the LIGA process. Nickel, Permalloy, and copper materials are tested. The tweezers are actuated by a collet mechanism which is closed by a DC linear motor

Statistics for the Evaluation and Comparison of Models

Copyright 1985 by the American Geophysical Union.Procedures that may be used to evaluate the operational performance of a wide spectrum of geophysical models are introduced. Primarily using a complementary set of difference measures, both model accuracy and precision can be meaningfully estimated, regardless of whether the model predictions are manifesteda s scalars,d irections,o r vectors.I t is additionally suggestedth at the reliability of the accuracy and precision measures can be determined from bootstrap estimates of confidence and significance. Recommendedp roceduresa re illustrated with a comparativee valuation of two models that estimate wind velocity over the South Atlantic Bight

Lyapunov-Based Reinforcement Learning for Decentralized Multi-Agent Control

Decentralized multi-agent control has broad applications, ranging from multi-robot cooperation to distributed sensor networks. In decentralized multi-agent control, systems are complex with unknown or highly uncertain dynamics, where traditional model-based control methods can hardly be applied. Compared with model-based control in control theory, deep reinforcement learning (DRL) is promising to learn the controller/policy from data without the knowing system dynamics. However, to directly apply DRL to decentralized multi-agent control is challenging, as interactions among agents make the learning environment non-stationary. More importantly, the existing multi-agent reinforcement learning (MARL) algorithms cannot ensure the closed-loop stability of a multi-agent system from a control-theoretic perspective, so the learned control polices are highly possible to generate abnormal or dangerous behaviors in real applications. Hence, without stability guarantee, the application of the existing MARL algorithms to real multi-agent systems is of great concern, e.g., UAVs, robots, and power systems, etc. In this paper, we aim to propose a new MARL algorithm for decentralized multi-agent control with a stability guarantee. The new MARL algorithm, termed as a multi-agent soft-actor critic (MASAC), is proposed under the well-known framework of "centralized-training-with-decentralized-execution". The closed-loop stability is guaranteed by the introduction of a stability constraint during the policy improvement in our MASAC algorithm. The stability constraint is designed based on Lyapunov's method in control theory. To demonstrate the effectiveness, we present a multi-agent navigation example to show the efficiency of the proposed MASAC algorithm.Comment: Accepted to The 2nd International Conference on Distributed Artificial Intelligenc