Encoderless position estimation and error correction techniques for miniature mobile robots

This paper presents an encoderless position estimation technique for miniature-sized mobile robots. Odometry techniques, which are based on the hardware components, are commonly used for calculating the geometric location of mobile robots. Therefore, the robot must be equipped with an appropriate sensor to measure the motion. However, due to the hardware limitations of some robots, employing extra hardware is impossible. On the other hand, in swarm robotic research, which uses a large number of mobile robots, equipping the robots with motion sensors might be costly. In this study, the trajectory of the robot is divided into several small displacements over short spans of time. Therefore, the position of the robot is calculated within a short period, using the speed equations of the robot's wheel. In addition, an error correction function is proposed that estimates the errors of the motion using a current monitoring technique. The experiments illustrate the feasibility of the proposed position estimation and error correction techniques to be used in miniature-sized mobile robots without requiring an additional sensor

K-nearest Neighbor Search by Random Projection Forests

K-nearest neighbor (kNN) search has wide applications in many areas, including data mining, machine learning, statistics and many applied domains. Inspired by the success of ensemble methods and the flexibility of tree-based methodology, we propose random projection forests (rpForests), for kNN search. rpForests finds kNNs by aggregating results from an ensemble of random projection trees with each constructed recursively through a series of carefully chosen random projections. rpForests achieves a remarkable accuracy in terms of fast decay in the missing rate of kNNs and that of discrepancy in the kNN distances. rpForests has a very low computational complexity. The ensemble nature of rpForests makes it easily run in parallel on multicore or clustered computers; the running time is expected to be nearly inversely proportional to the number of cores or machines. We give theoretical insights by showing the exponential decay of the probability that neighboring points would be separated by ensemble random projection trees when the ensemble size increases. Our theory can be used to refine the choice of random projections in the growth of trees, and experiments show that the effect is remarkable.Comment: 15 pages, 4 figures, 2018 IEEE Big Data Conferenc

Overcoming the penetration depth limit in optical microscopy: Adaptive optics and wavefront shaping

Despite the unique advantages of optical microscopy for molecular specific high resolution imaging of living structure in both space and time, current applications are mostly limited to research settings. This is due to the aberrations and multiple scattering that is induced by the inhomogeneous refractive boundaries that are inherent to biological systems. However, recent developments in adaptive optics and wavefront shaping have shown that high resolution optical imaging is not fundamentally limited only to the observation of single cells, but can be significantly enhanced to realize deep tissue imaging. To provide insight into how these two closely related fields can expand the limits of bio imaging, we review the recent progresses in their performance and applicable range of studies as well as potential future research directions to push the limits of deep tissue imaging

Extracting Visibility Information by Following Walls

This paper presents an analysis of a simple robot model, called Bitbot. The Bitbot has limited capabilities; it can reliably follow walls and sense a contact with a wall. Although the Bitbot does not have a range sensor or a camera, it is able to acquire visibility information from the environment, which is then used to solve a pursuit-evasion task. Our developments are centered on the characterization of the information the Bitbot acquires. At any given moment, due to the sensing uncertainty, the robot does not know the current state. In general, uncertainty in the state is one of the central issues in robotics; the Bitbot model serves as an example of how the notion of information space naturally handles uncertainty. We show that state estimation with the Bitbot is a challenging problem, related to the well-known open problem of characterizing visibility graphs in computational geometry. However, state estimation becomes unnecessary to the achievement of the Bitbot\u27s visibility tasks. We show how pursuit-evasion strategy is derived from a careful manipulation with histories of observations, and present analysis of the algorithm and experimental results