Computer Science 2019 APR Self-Study & Documents

UNM Computer Science APR self-study report and review team report for Spring 2019, fulfilling requirements of the Higher Learning Commission

Improving the SLAM Convergence with a Relative Map Filter

This paper presents an approach to solve the SLAM problem in the stochastic map framework based on the concept of the relative map. The idea consists in introducing a map state, which only contains quantities invariant under translation and rotation. This is the only way in order to have a decoupling between the robot motion and the landmark estimation and therefore not to rely the landmark estimation on the unmodeled error sources of the robot motion. The approach is general and can be applied for several kind of landmark. However, only the case of point landmark is considered here. For this special case, the structure of the proposed filter is deeply examined and a comparison with the joint vehicle-landmark approach (absolute map filter) is carried out theoretically and through accurate simulations. The main result shown about this new approach is the map convergence in large environment even when the odometry is affected by undetected systematic errors or by large or unmodeled non-systematic errors

Stochastic Plume Estimation: Measurement Sampling for a Supermartingale Support

In this paper we use a simple a model for a stochastically moving plume center and determine sufficient measurement schemes, for three cases of measurement noise, that reduce the support of the plume center\u27s probability distribution. We assume a multivariate gaussian plume that moves according to a stochastic discrete-time stochastic linear time-invariant model. We also assume a measurement function that is a function of proximity to the center of the plume distribution. Using both knowledge of the dynamics and the behaviour of this measurement function a recursive probability distribution was formulated. We then found sufficient measurement schemes that reduce the support of this recursive probability distribution such that the area of the support behaves like a supermartingale

Cooperative Localization and SLAM Based on the Extended Information Filter

Virtual realit

Dynamic Parameter Identification of a 6 DOF Industrial Robot using Power Model

International audienceOff-line dynamic identification requires the use of a model linear in relation to the robot dynamic parameters and the use of linear least squares technique to calculate the parameters. Most of time, the used model is the Inverse Dynamic Identification Model (IDIM). However, the computation of its symbolic expressions is extremely tedious. In order to simplify the procedure, the use of the Power Identification Model (PIM), which is dramatically simpler to obtain and that contains exactly the same dynamic parameters as the IDIM, was previously proposed. However, even if the identification of the PIM parameters for a 2 degrees-of-freedom (DOF) planar serial robot was successful, its fails to work for 6 DOF industrial robots. This paper discloses the reasons of this failure and presents a methodology for the identification of the robot dynamic parameters using the PIM. The method is experimentally validated on an industrial 6 DOF Stäubli TX-40 robot

Toward the vision based supervision of microfactories through images mosaicing.

International audienceThe microfactory paradigm means the miniaturisation of manufacturing systems according to the miniaturisation of products. Some benefits are the saving of material, energy and place. A vision based solution to the problem of supervision of microfactories is proposed. It consists in synthetising a high resolution global view of the work field and real time inlay of local image in this background. The result can be used for micromanipulation monitoring, assistance to the operator, alarms and others useful informations displaying