Scaling Hard Vertical Surfaces with Compliant Microspine Arrays
A new approach for climbing hard vertical surfaces has been developed that allows a robot to scale concrete, stucco, brick and masonry walls without using suction or adhesives.The approach is inspired by the mechanisms observed in some climbing insects and spiders and involves arrays of microspines that catch on surface asperities. The arrays are located on the toes of the robot and consist of a tuned, multi-link compliant suspension. The fundamental issues of spine allometric scaling versus surface roughness are discussed and the interaction between spines and surfaces is modeled. The toe suspension properties needed to maximize the probability that each spine will find a useable surface irregularity and to distribute climbing loads among many spines are detailed. The principles are demonstrated with a new climbing robot, SpinybotII, that can scale a wide range of flat exterior walls, carry a payload equal to its own weight, and cling without consuming power. The paper also reports how toe parameters scale with robot mass and how spines have also been used successfully on the larger RiSE robot.
Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
Learning patterns of human behavior from sensor data is extremely important for high-level activity inference. This paper describes how to extract a person’s activities and significant places from traces of GPS data. The system uses hierarchically structured conditional random fields to generate a consistent model of a person’s activities and places. In contrast to existing techniques, this approach takes the high-level context into account in order to detect the significant places of a person. Experiments show significant improvements over existing techniques. Furthermore, they indicate that the proposed system is able to robustly estimate a person’s activities using a model that is trained from data collected by other persons.
That Which Does Not Stabilize, Will Only Make Us Stronger
Many places in the world are too rugged or enclosed for vehicles to access. Even today, material transport to such areas is limited to manual labor and beasts of burden. Modern advancements in wearable robotics may make those methods obsolete. Lower extremity exoskeletons seek to supplement the intelligence and sensory systems of a human with the significant strength and endurance of a pair of wearable robotic legs that support a payload. This article first outlines the use of Clinical Gait Analysis data as the framework for the design of such a system at UC Berkeley. This data is used to design the exoskeleton degrees of freedom and size its actuators. It will then give an overview of one of the control schemes implemented on the BLEEX. The control algorithm described here increases the system closed loop sensitivity to its wearer’s forces and torques without any measurement from the wearer (such as force, position, or electromyogram signal). The control algorithm uses the inverse dynamics of the exoskeleton, scaled by a number smaller than unity, as a positive feedback controller. This controller almost destabilizes the system since it leads to an overall loop gain slightly smaller than unity and results in a large sensitivity to all wearer’s forces and torques thereby allowing the exoskeleton to shadow its wearer.
Multi-robot Simultaneous Localization and Mapping using Particle Filters
This paper describes an on-line algorithm for multi-robot simultaneous localization and mapping (SLAM). The starting point is the single-robot Rao-Blackwellized particle filter described by H hnel et al., and three key generalizations are made. First, the particle filter is extended to handle multi-robot SLAM problems in which the initial pose of the robots is known (such as occurs when all robots start from the same location). Second, an approximation is introduced to solve the more general problem in which the initial pose of robots is not known a priori (such as occurs when the robots start from widely separated locations). In this latter case, it is assumed that pairs of robots will eventually encounter one another, thereby determining their relative pose. This relative attitude is used to initialize the filter, and subsequent observations from both robots are combined into a common map. Third and finally, a method is introduced to integrate observations collected prior to the first robot encounter, using the notion of a virtual robot travelling backwards in time. This novel approach allows one to integrate all data from all robots into a single common map.
That Which Does Not Stabilize, Will Only Make Us Stronger
Many places in the world are too rugged or enclosed for vehicles to access. Even today, material transport to such areas is limited to manual labor and beasts of burden. Modern advancements in wearable robotics may make those methods obsolete. Lower extremity exoskeletons seek to supplement the intelligence and sensory systems of a human with the significant strength and endurance of a pair of wearable robotic legs that support a payload. This article first outlines the use of Clinical Gait Analysis data as the framework for the design of such a system at UC Berkeley. This data is used to design the exoskeleton degrees of freedom and size its actuators. It will then give an overview of one of the control schemes implemented on the BLEEX. The control algorithm described here increases the system closed loop sensitivity to its wearer’s forces and torques without any measurement from the wearer (such as force, position, or electromyogram signal). The control algorithm uses the inverse dynamics of the exoskeleton, scaled by a number smaller than unity, as a positive feedback controller. This controller almost destabilizes the system since it leads to an overall loop gain slightly smaller than unity and results in a large sensitivity to all wearer’s forces and torques thereby allowing the exoskeleton to shadow its wearer.
Visually Mapping the RMS Titanic: Conservative Covariance Estimates for SLAM Information Filters
This paper describes a vision-based, large-area, simultaneous localization and mapping (SLAM) algorithm that respects the low-overlap imagery constraints typical of underwater vehicles while exploiting the inertial sensor information that is routinely available on such platforms. We present a novel strategy for efficiently accessing and maintaining consistent covariance bounds within a SLAM information filter, thereby greatly increasing the reliability of data association. The technique is based upon solving a sparse system of linear equations coupled with the application of constant-time Kalman updates. The method is shown to produce consistent covariance estimates suitable for robot planning and data association. Real-world results are reported for a vision-based, six degree of freedom SLAM implementation using data from a recent survey of the wreck of the RMS Titanic.
Multi-robot Simultaneous Localization and Mapping using Particle Filters
This paper describes an on-line algorithm for multi-robot simultaneous localization and mapping (SLAM). The starting point is the single-robot Rao-Blackwellized particle filter described by H hnel et al., and three key generalizations are made. First, the particle filter is extended to handle multi-robot SLAM problems in which the initial pose of the robots is known (such as occurs when all robots start from the same location). Second, an approximation is introduced to solve the more general problem in which the initial pose of robots is not known a priori (such as occurs when the robots start from widely separated locations). In this latter case, it is assumed that pairs of robots will eventually encounter one another, thereby determining their relative pose. This relative attitude is used to initialize the filter, and subsequent observations from both robots are combined into a common map. Third and finally, a method is introduced to integrate observations collected prior to the first robot encounter, using the notion of a virtual robot travelling backwards in time. This novel approach allows one to integrate all data from all robots into a single common map.
Scaling Hard Vertical Surfaces with Compliant Microspine Arrays
A new approach for climbing hard vertical surfaces has been developed that allows a robot to scale concrete, stucco, brick and masonry walls without using suction or adhesives.The approach is inspired by the mechanisms observed in some climbing insects and spiders and involves arrays of microspines that catch on surface asperities. The arrays are located on the toes of the robot and consist of a tuned, multi-link compliant suspension. The fundamental issues of spine allometric scaling versus surface roughness are discussed and the interaction between spines and surfaces is modeled. The toe suspension properties needed to maximize the probability that each spine will find a useable surface irregularity and to distribute climbing loads among many spines are detailed. The principles are demonstrated with a new climbing robot, SpinybotII, that can scale a wide range of flat exterior walls, carry a payload equal to its own weight, and cling without consuming power. The paper also reports how toe parameters scale with robot mass and how spines have also been used successfully on the larger RiSE robot.