Robot Builders

Markus Waibel writes, “The latest Talking Robots podcast episode interviews Jun Tani, who is a team leader at the RIKEN Brain Science Institute, about his research in robot cognition and robot consciousness. He talks about robot, animal and human brains, meta-level cognition, and on his interest in building schizophrenic robots.”

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.

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.

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Haptics, like the fields of robotics and motion control, relies on high stiffness position control of electric motors. Traditionally, DC motors are driven by current amplifiers designed to hide their electrical dynamics. Meanwhile encoder-based position feedback creates virtual springs. Unfortunately this cancellation-replacement approach experiences performance limits due to sensor quantization, discretization, and amplifier bandwidths.

An alternate approach is presented, noting the inherent inductor-resistor dynamics of the motor are beneficial to the haptic task. Two main insights are followed, which may be utilized independently or preferably in combination. First, the electrical inductance L can serve as a stiffness, providing a natural sensorless coupling between the virtual environment and the user. Second, the electrical resistance R can create a natural wave transformation, providing a robust computer interface between the discrete and continuous time domains. The resulting analog circuit implements a simple voltage drive and can achieve higher stiffness than traditional approaches, especially in the frequency range where human users are most sensitive. A prototype 1-DOF system has been implemented and confirms the promise of this novel paradigm.

In the development of humanoids, both the appearance and behavior of the robots are significant issues. However, designing the robot’s appearance, especially to give it a humanoid one, was always a role of industrial designers. To tackle the problem of appearance and behavior, two approaches are necessary: one from robotics and the other from cognitive science. The approach from robotics tries to build very humanlike robots based on knowledge from cognitive science. The approach from cognitive science uses the robot to verify hypotheses for understanding humans. This cross-interdisciplinary framework is called android science. This conceptual paper introduces developed androids and states key issues in android science.

Researchers at the University of Illinois at Urbana-Champaign (UIUC) have created the world’s smallest chain-mail fabric. This fabric looks like the chain-mail armor worn by medieval knights, but can embed much more recent sensors to create some smart textiles. This fabric, which consists of “a network of small rings about 500 microns in diameter and even smaller links about 400 microns long,” has unique electrical properties. For example, such a smart fabric could detect movement or damage, and even generate electricity to power the sensors embedded into it. But don’t expect to wear a dress or a jacket made with it anytime soon. read story

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.

LinuxDevices reports that Atmel is now shipping a very inexpensive single board computer (SBC) that runs Linux. The ATNGW 100 board, based on Atmel’s AVR32 architecture, can be had for only $69. While it’s aimed at network gateway use, robot experimenters will be interested too because of the pricing. The board includes a lot of stuff for $69 such as a 140MHz AT32AP7000 MCU/DSP, 32MB SDRAM, 16MB flash, an SD/MMC slot, an ATtiny24 board controller interface, 16-bit stereo audio DAC, LCD controller, USB 2.0, two 10/1000 Ethernet ports, RS232, USART, TWI/I2C, I2S, JTAG, timer/PWM outputs, and GPIO pins. More details can be found on the AVR Freaks NGW page.

Haptics, like the fields of robotics and motion control, relies on high stiffness position control of electric motors. Traditionally, DC motors are driven by current amplifiers designed to hide their electrical dynamics. Meanwhile encoder-based position feedback creates virtual springs. Unfortunately this cancellation-replacement approach experiences performance limits due to sensor quantization, discretization, and amplifier bandwidths.

An alternate approach is presented, noting the inherent inductor-resistor dynamics of the motor are beneficial to the haptic task. Two main insights are followed, which may be utilized independently or preferably in combination. First, the electrical inductance L can serve as a stiffness, providing a natural sensorless coupling between the virtual environment and the user. Second, the electrical resistance R can create a natural wave transformation, providing a robust computer interface between the discrete and continuous time domains. The resulting analog circuit implements a simple voltage drive and can achieve higher stiffness than traditional approaches, especially in the frequency range where human users are most sensitive. A prototype 1-DOF system has been implemented and confirms the promise of this novel paradigm.

read story

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.