Cyrus the Tux Droid and his friend, Fux
According to a LinuxDevices story, the latest robot to come out of Europe is the Belgian Tux Droid, named Cyrus. Despite the name, the Tux droid doesn’t run Linux. Inside is an Atmel AVR microcontroller. The robot does include a Python based API called tuxdaemon that allows it to be controlled from a Linux box using a fishy USB dongle called “Fux”. The Tux droid communicates with Fux using a 2.4GHz full-duplex RF link. So what’s the robot actually do? It can move its beak and LED eyes. It can flap its wings, and spin. The wings and head have pressure sensors that can trigger behaviors. It also has a speaker and microphone, allowing it to reproduce sounds or even act as VOIP phone. Suggested uses include dancing when you receive an email or acting as an alarm clock. Among the other features, it includes an IrDA transceiver and an I2C bus connector that might allow some interesting new sensors or actuators. Find out more on the Tux Droid community portal.
A Generative Model of Terrain for Autonomous Navigation in Vegetation
Current approaches to off-road autonomous navigation are often limited by their ability to build a terrain model from sensor data. Available sensors make very indirect measurements of quantities of interest such as the supporting ground height and the location of obstacles, especially in domains where vegetation may hide the ground surface or partially obscure obstacles. A generative, probabilistic terrain model is introduced that exploits natural structure found in off-road environments to constrain the problem and use ambiguous sensor data more effectively. The model includes two Markov random fields that encode the assumptions that ground heights smoothly vary and terrain classes tend to cluster. The model also includes a latent variable that encodes the assumption that vegetation of a single type has a similar height. The model parameters can be trained by simply driving through representative terrain. Results from a number of challenging test scenarios in an agricultural domain reveal that exploiting the 3D structure inherent in outdoor domains significantly improves ground estimates and obstacle detection accuracy, and allows the system to infer the supporting ground surface even when it is hidden under dense vegetation.
Teaching nanoscience to the blind
Nanoscale objects are much too small for us to see them. So, according to educators at the University of Wisconsin-Madison, nanotechnology is a research field where blind students and sighted ones are equal. After all, “we’re all blind at the nanoscale,” says a member of the educational team. They’ve built 3-D models of nano-surfaces which can be explored with the hands. These plaster models, which are several inches long — even if the structures they represent are millions times smaller — replicate an earlier version of ‘NanoBucky,’ a nanoscale version of the UW-Madison mascot, Bucky Badger. The goal of this project is to encourage blind and visually impaired students to pursue science, technology and engineering. read story
Tradeoffs Between Directed and Autonomous Driving on the Mars Exploration Rovers
NASA’s Mars Exploration Rovers (MER) have collected a great diversity of geological science results, thanks in large part to their surface mobility capabilities. The six wheel rocker/bogie mobility system provides driving capabilities in a range of terrain types, while the onboard IMU measures actual rover attitude changes (roll, pitch and yaw, but not position) quickly and accurately. Four stereo camera pairs provide accurate position knowledge and/or terrain assessment. Solar panels generally provide enough energy to drive the vehicle for at most four hours each day, but drive time is often restricted by other planned activities. Driving along slopes in nonhomogeneous terrain injects unpredictable amounts of slip into each drive. These restrictions led to the creation of driving strategies that alternately use more or less onboard autonomy, to maximize drive speed and distance at the cost of increased complexity in the sequences of commands built by human Rover Planners each day.
Commands to the MER vehicles are typically transmitted at most once per day, so mobility operations are encoded as event-driven sequences of individual motion commands. Motions may be commanded using quickly-executing Directed commands which perform only reactive motion safety checks (e.g., real-time current limits, maximum instantaneous vehicle tilt limit), slowly-executing position measuring Visual Odometry (VisOdom) commands, which use images to accurately update the onboard position estimate, or slow-to-medium speed Autonomous Navigation (AutoNav) commands, which use onboard image processing to perform predictive terrain safety checks and optional autonomous Path Selection.
In total, the MER rovers have driven more than 10 kilometers over Martian terrain during their first 21 months of operation using these basic modes. In this paper we describe the strategies adopted for selecting between human-planned Directed drives versus rover-adaptive Autonomous Navigation, Visual Odometry and Path Selection drives.
Predicting the Performance of Cooperative Simultaneous Localization and Mapping (C-SLAM)
In this paper we study the time evolution of the position estimates’ covariance in Cooperative Simultaneous Localization and Mapping (C-SLAM), and obtain analytical upper bounds for the positioning uncertainty. The derived bounds provide descriptions of the asymptotic positioning performance of a team of robots in a mapping task, as a function of the characteristics of the proprioceptive and exteroceptive sensors of the robots, and of the graph of relative position measurements recorded by the robots. A study of the properties of the Riccati recursion, which describes the propagation of uncertainty through time, yields (i) the guaranteed accuracy for a robot team in a given C-SLAM application, as well as (ii) the maximum expected steady-state uncertainty of the robots and landmarks, when the spatial distribution of features in the environment can be modeled by a known distribution. The theoretical results are validated both in simulation and experimentally.
A Generative Model of Terrain for Autonomous Navigation in Vegetation
Current approaches to off-road autonomous navigation are often limited by their ability to build a terrain model from sensor data. Available sensors make very indirect measurements of quantities of interest such as the supporting ground height and the location of obstacles, especially in domains where vegetation may hide the ground surface or partially obscure obstacles. A generative, probabilistic terrain model is introduced that exploits natural structure found in off-road environments to constrain the problem and use ambiguous sensor data more effectively. The model includes two Markov random fields that encode the assumptions that ground heights smoothly vary and terrain classes tend to cluster. The model also includes a latent variable that encodes the assumption that vegetation of a single type has a similar height. The model parameters can be trained by simply driving through representative terrain. Results from a number of challenging test scenarios in an agricultural domain reveal that exploiting the 3D structure inherent in outdoor domains significantly improves ground estimates and obstacle detection accuracy, and allows the system to infer the supporting ground surface even when it is hidden under dense vegetation.
A Unified Passivity-based Control Framework for Position, Torque and Impedance Control of Flexible Joint Robots
This paper describes a general passivity-based framework for the control of flexible joint robots. Recent results on torque, position, as well as impedance control of flexible joint robots are summarized, and the relations between the individual contributions are highlighted. It is shown that an inner torque feedback loop can be incorporated into a passivity-based analysis by interpreting torque feedback in terms of shaping of the motor inertia. This result, which implicitly was already included in earlier work on torque and position control, can also be used for the design of impedance controllers. For impedance control, furthermore, potential energy shaping is of special interest. It is shown how, based only on the motor angles, a potential function can be designed which simultaneously incorporates gravity compensation and a desired Cartesian stiffness relation for the link angles. All the presented controllers were experimentally evaluated on DLR lightweight robots and their performance and robustness shown with respect to uncertain model parameters. Experimental results with position controllers as well as an impact experiment are presented briefly, and an overview of several applications is given in which the controllers have been applied.
A Generative Model of Terrain for Autonomous Navigation in Vegetation
Current approaches to off-road autonomous navigation are often limited by their ability to build a terrain model from sensor data. Available sensors make very indirect measurements of quantities of interest such as the supporting ground height and the location of obstacles, especially in domains where vegetation may hide the ground surface or partially obscure obstacles. A generative, probabilistic terrain model is introduced that exploits natural structure found in off-road environments to constrain the problem and use ambiguous sensor data more effectively. The model includes two Markov random fields that encode the assumptions that ground heights smoothly vary and terrain classes tend to cluster. The model also includes a latent variable that encodes the assumption that vegetation of a single type has a similar height. The model parameters can be trained by simply driving through representative terrain. Results from a number of challenging test scenarios in an agricultural domain reveal that exploiting the 3D structure inherent in outdoor domains significantly improves ground estimates and obstacle detection accuracy, and allows the system to infer the supporting ground surface even when it is hidden under dense vegetation.
Replace smileys with your face
According to Technology Review in “The New Face of Emoticons,” computer scientists from U.S. and Taiwan have found a new way to personalize your messages. You just need a picture of you — preferably with a neutral expression — and their software will show your mood to your correspondent and tell them if you’re happy, sad, or angry. The real innovation is that you will not have to transmit the whole image each time. Instead, your original picture will already have been stored on the recipient’s device, and the desired expression will be automatically reconstructed when opening your message. Clever idea, which mixes emoticons and avatars, but will it work? read story
A Unified Passivity-based Control Framework for Position, Torque and Impedance Control of Flexible Joint Robots
This paper describes a general passivity-based framework for the control of flexible joint robots. Recent results on torque, position, as well as impedance control of flexible joint robots are summarized, and the relations between the individual contributions are highlighted. It is shown that an inner torque feedback loop can be incorporated into a passivity-based analysis by interpreting torque feedback in terms of shaping of the motor inertia. This result, which implicitly was already included in earlier work on torque and position control, can also be used for the design of impedance controllers. For impedance control, furthermore, potential energy shaping is of special interest. It is shown how, based only on the motor angles, a potential function can be designed which simultaneously incorporates gravity compensation and a desired Cartesian stiffness relation for the link angles. All the presented controllers were experimentally evaluated on DLR lightweight robots and their performance and robustness shown with respect to uncertain model parameters. Experimental results with position controllers as well as an impact experiment are presented briefly, and an overview of several applications is given in which the controllers have been applied.