Concurrent-scene/alternate-pattern analysis for robust video-based docking systems

A typical docking target employs a three-point design of retroreflective tape, one at each endpoint of the center-line, and one on the tip of the central post. Scenes, sensed via laser diode illumination, produce pictures with spots corresponding to desired reflection from the retroreflectors and other reflections. Control corrections for each axis of the vehicle can then be properly applied if the desired spots are accurately tracked. However, initial acquisition of these three spots (detection and identification problem) are non-trivial under a severe noise environment. Signal-to-noise enhancement, accomplished by subtracting the non-illuminated scene from the target scene illuminated by laser diodes, can not eliminate every false spot. Hence, minimization of docking failures due to target mistracking would suggest needed inclusion of added processing features pertaining to target locations. In this paper, we present a concurrent processing scheme for a modified docking target scene which could lead to a perfect docking system. Since the non-illuminated target scene is already available, adding another feature to the three-point design by marking two non-reflective lines, one between the two end-points and one from the tip of the central post to the center-line, would allow this line feature to be picked-up only when capturing the background scene (sensor data without laser illumination). Therefore, instead of performing the image subtraction to generate a picture with a high signal-to-noise ratio, a processed line-image based on the robust line detection technique (Hough transform) can be used to fuse with the actively sensed three-point target image to deduce the true locations of the docking target. This dual-channel confirmation scheme is necessary if a fail-safe system is to be realized from both the sensing and processing point-of-views. Detailed algorithms and preliminary results are presented

JPL Robotics Technology Applicable to Agriculture

This slide presentation describes several technologies that are developed for robotics that are applicable for agriculture. The technologies discussed are detection of humans to allow safe operations of autonomous vehicles, and vision guided robotic techniques for shoot selection, separation and transfer to growth media

Mars base technology program overview

In this paper, we present an overview of the current technology portfolio for Mars Base Technology Program. Brief descriptions of the awarded technologies and the high-priority areas in both NRAs are provided to show the current focus of MTP. We also present the approach that MTP uses to evaluate technology maturity for each of the technology tasks

Tera-Ops Processing for ATR

A three-dimensional microelectronic device (3DANN-R) capable of performing general image convolution at the speed of 1012 operations/second (ops) in a volume of less than 1.5 cubic centimeter has been successfully built under the BMDO/JPL VIGILANTE program. 3DANN-R was developed in partnership with Irvine Sensors Corp., Costa Mesa, California. 3DANN-R is a sugar-cube-sized, low power image convolution engine that in its core computation circuitry is capable of performing 64 image convolutions with large (64x64) windows at video frame rates. This paper explores potential applications of 3DANN-R such as target recognition, SAR and hyperspectral data processing, and general machine vision using real data and discuss technical challenges for providing deployable systems for BMDO surveillance and interceptor programs