Imaging the center of the Earth
Scientists from several U.S. universities had a bright idea. They’ve adapted the industry tools developed for oil and gas exploration to use the data provided by more than 1,000 seismic observatories to image the Earth’s mantle 2,900 kilometers beneath Central and North America. The team wants now to image the whole globe. It also thinks that these new imaging technologies could improve how we look for oil in or beneath geologically complex structures such as the Gulf of Mexico salt domes, but do we really need this? read story
Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing
Solving the SLAM (simultaneous localization and mapping) problem is one way to enable a robot to explore, map, and navigate in a previously unknown environment. Smoothing approaches have been investigated as a viable alternative to extended Kalman filter (EKF)-based solutions to the problem. In particular, approaches have been looked at that factorize either the associated information matrix or the measurement Jacobian into square root form. Such techniques have several significant advantages over the EKF: they are faster yet exact; they can be used in either batch or incremental mode; are better equipped to deal with non-linear process and measurement models; and yield the entire robot trajectory, at lower cost for a large class of SLAM problems. In addition, in an indirect but dramatic way, column ordering heuristics automatically exploit the locality inherent in the geographic nature of the SLAM problem. This paper presents the theory underlying these methods, along with an interpretation of factorization in terms of the graphical model associated with the SLAM problem. Both simulation results and actual SLAM experiments in large-scale environments are presented that underscore the potential of these methods as an alternative to EKF-based approaches.
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.
Coordinate-free Coverage in Sensor Networks with Controlled Boundaries via Homology
Tools from computational homology are introduced to verify coverage in an idealized sensor network. These methods are unique in that, while they are coordinate-free and assume no localization or orientation capabilities for the nodes, there are also no probabilistic assumptions. The key ingredient is the theory of homology from algebraic topology. The robustness of these tools is demonstrated by adapting them to a variety of settings, including static planar coverage, 3-D barrier coverage, and time-dependent sweeping coverage. Results are also given on hole repair, error tolerance, optimal coverage, and variable radii. An overview of implementation is given.
Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing
Solving the SLAM (simultaneous localization and mapping) problem is one way to enable a robot to explore, map, and navigate in a previously unknown environment. Smoothing approaches have been investigated as a viable alternative to extended Kalman filter (EKF)-based solutions to the problem. In particular, approaches have been looked at that factorize either the associated information matrix or the measurement Jacobian into square root form. Such techniques have several significant advantages over the EKF: they are faster yet exact; they can be used in either batch or incremental mode; are better equipped to deal with non-linear process and measurement models; and yield the entire robot trajectory, at lower cost for a large class of SLAM problems. In addition, in an indirect but dramatic way, column ordering heuristics automatically exploit the locality inherent in the geographic nature of the SLAM problem. This paper presents the theory underlying these methods, along with an interpretation of factorization in terms of the graphical model associated with the SLAM problem. Both simulation results and actual SLAM experiments in large-scale environments are presented that underscore the potential of these methods as an alternative to EKF-based approaches.
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.
Coordinate-free Coverage in Sensor Networks with Controlled Boundaries via Homology
Tools from computational homology are introduced to verify coverage in an idealized sensor network. These methods are unique in that, while they are coordinate-free and assume no localization or orientation capabilities for the nodes, there are also no probabilistic assumptions. The key ingredient is the theory of homology from algebraic topology. The robustness of these tools is demonstrated by adapting them to a variety of settings, including static planar coverage, 3-D barrier coverage, and time-dependent sweeping coverage. Results are also given on hole repair, error tolerance, optimal coverage, and variable radii. An overview of implementation is given.