1,389 research outputs found
Path evaluation for a mobile robot based on a risk of collision
An odometry system that mobile robot uses for positioning has cumulative error because of wheels' slippage and uneven ground. It causes a risk of collision of obstacles. Therefore, we propose a path evaluation method for a mobile robot based on a risk of collision. To evaluate a robot's path, we define an evaluation value as an integral of a risk of collision along the path. To evaluate the risk of collision at each point, we use an estimated positioning error generated in the odometry system. Using the evaluation method, the robot can plan a path based on a risk of collision, not the shortest path. We also consider sensing points planning for position adjustment of the mobile robot, based on the same approach. Some examples of path evaluation results support a validity of the proposed method.</p
Appearance-based motion recognition of human actions
Thesis (M.S.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1996.Includes bibliographical references (leaves 52-53).by James William Davis.M.S
Adaptive Bayesian State Estimation Integrating Non-stationary DGNSS Inter-Agent Distances
Bayesian navigation filters are broadly exploited in precise state estimation for kinematic applications such as vehicular positioning and navigation. Among these, Particle Filter (PF) has been shown as a valuable solution to support hybrid positioning algorithms such as sensor fusion to Global Navigation Satellite System (GNSS) and Cooperative Positioning (CP). Despite of an increased computational complexity w.r.t. conventional Kalman Filters (KFs), an effective weighting of the input measurements generally provides an improved accuracy of the output estimate. In the framework of the Differential GNSS (DGNSS) CP, this work presents an algorithm for the automated selection of the most appropriate error models for the tight-integration of non-stationary Differential GNSS (DGNSS) collaborative inter-agent distances. A model switching technique named Automated Adaptive Likelihood Switch (AALS) is proposed for a Cognitive Particle Filter (C-PF) architecture, based on the real-time approximation of the statistics of the inter-agent distances errors. The results achieved through realistic simulations demonstrated the effectiveness of the proposed solution in terms of error model selection. Therefore, an improvement of the position estimation accuracy was observed, since the cases in which DGNSS-CP would degrade performance due to possible mismodelling of the selected likelihood function are avoided
Spatial Learning and Localization in Animals: A Computational Model and Its Implications for Mobile Robots
The ability to acquire a representation of spatial environment and the ability to localize within it are essential for successful navigation in a-priori unknown environments. The hippocampal formation is believed to play a key role in spatial learning and navigation in animals. This paper briefly reviews the relevant neurobiological and cognitive data and their relation to computational models of spatial learning and localization used in mobile robots. It also describes a hippocampal model of spatial learning and navigation and analyzes it using Kalman filter based tools for information fusion from multiple uncertain sources. The resulting model allows a robot to learn a place-based, metric representation of space in a-priori unknown environments and to localize itself in a stochastically optimal manner. The paper also describes an algorithmic implementation of the model and results of several experiments that demonstrate its capabilities
Surface Reconstruction from Noisy and Sparse Data
We introduce a set of algorithms for registering, filtering and measuring the similarity of unorganized 3d point clouds, usually obtained from multiple views.
We contribute a method for computing the similarity between point clouds that represent closed surfaces, specifically segmented tumors from CT scans. We obtain watertight surfaces and utilize volumetric overlap to determine similarity in a volumetric way. This similarity measure is used to quantify treatment variability based on target volume segmentation both prior to and following radiotherapy planning stages.
We also contribute an algorithm for the drift-free registration of thin, non- rigid scans, where drift is the build-up of error caused by sequential pairwise registration, which is the alignment of each scan to its neighbor. We construct an average scan using mutual nearest neighbors, each scan is registered to this average scan, after which we update the average scan and continue this process until convergence. The use case herein is for merging scans of plants from multiple views and registering vascular scans together.
Our final contribution is a method for filtering noisy point clouds, specif- ically those constructed from merged depth maps as obtained from a range scanner or multiple view stereo (MVS), applying techniques that have been utilized in finding outliers in clustered data, but not in MVS. We utilize ker- nel density estimation to obtain a probability density function over the space of observed points, utilizing variable bandwidths based on the nature of the neighboring points, Mahalanobis and reachability distances that is more dis- criminative than a classical Mahalanobis distance-based metric
Learning-based Uncertainty-aware Navigation in 3D Off-Road Terrains
This paper presents a safe, efficient, and agile ground vehicle navigation
algorithm for 3D off-road terrain environments. Off-road navigation is subject
to uncertain vehicle-terrain interactions caused by different terrain
conditions on top of 3D terrain topology. The existing works are limited to
adopt overly simplified vehicle-terrain models. The proposed algorithm learns
the terrain-induced uncertainties from driving data and encodes the learned
uncertainty distribution into the traversability cost for path evaluation. The
navigation path is then designed to optimize the uncertainty-aware
traversability cost, resulting in a safe and agile vehicle maneuver. Assuring
real-time execution, the algorithm is further implemented within parallel
computation architecture running on Graphics Processing Units (GPU).Comment: 6 pages, 6 figures, submitted to International Conference on Robotics
and Automation (ICRA 2023
Flexible human-robot cooperation models for assisted shop-floor tasks
The Industry 4.0 paradigm emphasizes the crucial benefits that collaborative
robots, i.e., robots able to work alongside and together with humans, could
bring to the whole production process. In this context, an enabling technology
yet unreached is the design of flexible robots able to deal at all levels with
humans' intrinsic variability, which is not only a necessary element for a
comfortable working experience for the person but also a precious capability
for efficiently dealing with unexpected events. In this paper, a sensing,
representation, planning and control architecture for flexible human-robot
cooperation, referred to as FlexHRC, is proposed. FlexHRC relies on wearable
sensors for human action recognition, AND/OR graphs for the representation of
and reasoning upon cooperation models, and a Task Priority framework to
decouple action planning from robot motion planning and control.Comment: Submitted to Mechatronics (Elsevier
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