3,234 research outputs found
Multi-criteria Evolution of Neural Network Topologies: Balancing Experience and Performance in Autonomous Systems
Majority of Artificial Neural Network (ANN) implementations in autonomous
systems use a fixed/user-prescribed network topology, leading to sub-optimal
performance and low portability. The existing neuro-evolution of augmenting
topology or NEAT paradigm offers a powerful alternative by allowing the network
topology and the connection weights to be simultaneously optimized through an
evolutionary process. However, most NEAT implementations allow the
consideration of only a single objective. There also persists the question of
how to tractably introduce topological diversification that mitigates
overfitting to training scenarios. To address these gaps, this paper develops a
multi-objective neuro-evolution algorithm. While adopting the basic elements of
NEAT, important modifications are made to the selection, speciation, and
mutation processes. With the backdrop of small-robot path-planning
applications, an experience-gain criterion is derived to encapsulate the amount
of diverse local environment encountered by the system. This criterion
facilitates the evolution of genes that support exploration, thereby seeking to
generalize from a smaller set of mission scenarios than possible with
performance maximization alone. The effectiveness of the single-objective
(optimizing performance) and the multi-objective (optimizing performance and
experience-gain) neuro-evolution approaches are evaluated on two different
small-robot cases, with ANNs obtained by the multi-objective optimization
observed to provide superior performance in unseen scenarios
Instance Segmentation for Feature Recognition on Noncooperative Resident Space Objects
Active debris removal and unmanned on-orbit servicing missions have gained interest in the last few years, along with the possibility to perform them through the use of an autonomous chasing spacecraft. In this work, new resources are proposed to aid the implementation of guidance, navigation, and control algorithms for satellites devoted to the inspection of noncooperative targets before any proximity operation is initiated. In particular, the use of convolutional neural networks (CNN) performing object detection and instance segmentation is proposed, and its effectiveness in recognizing the components and parts of the target satellite is evaluated. Yet, no reliable training images dataset of this kind exists to date. A tailored and publicly available software has been developed to overcome this limitation by generating synthetic images. Computer-aided design models of existing satellites are loaded on a three-dimensional animation software and used to programmatically render images of the objects from different points of view and in different lighting conditions, together with the necessary ground truth labels and masks for each image. The results show how a relatively low number of iterations is sufficient for a CNN trained on such datasets to reach a mean average precision value in line with state-of-the-art performances achieved by CNN in common datasets. An assessment of the performance of the neural network when trained on different conditions is provided. To conclude, the method is tested on real images from the Mission Extension Vehicle-1 on-orbit servicing mission, showing that using only artificially generated images to train the model does not compromise the learning process
NASA Automated Rendezvous and Capture Review. Executive summary
In support of the Cargo Transfer Vehicle (CTV) Definition Studies in FY-92, the Advanced Program Development division of the Office of Space Flight at NASA Headquarters conducted an evaluation and review of the United States capabilities and state-of-the-art in Automated Rendezvous and Capture (AR&C). This review was held in Williamsburg, Virginia on 19-21 Nov. 1991 and included over 120 attendees from U.S. government organizations, industries, and universities. One hundred abstracts were submitted to the organizing committee for consideration. Forty-two were selected for presentation. The review was structured to include five technical sessions. Forty-two papers addressed topics in the five categories below: (1) hardware systems and components; (2) software systems; (3) integrated systems; (4) operations; and (5) supporting infrastructure
Simulation models for autonomous rendezvous and capture
Autonomous rendezvous and capture (AR&C) is a critical space technology with significant application to a variety of missions. Martin Marietta Astronautics Group (MMAG) has been developing AR&C technical capability in support of several recent NASA contracts. The use of AR&C for the Mars Rover/Sample Return (MRSR) mission was studied through a contract with JSC. Incorporation of AR&C in the Space Transportation Vehicle (STV) lunar mission was studied through a contract with MSFC. The MMAG has also been developing AR&C simulation capability under independent research and development studies. Simulation development was driven by two goals: comprehensive software simulation of the autonomous rendezvous and capture mission from launch to final capture; and integration of the overall software and hardware simulation to support an AR&C flight demonstration. This presentation will highlight the AR&C software simulation tools and analyze results from their application to the STV lunar mission. Plans for an integrated software and hardware simulation will also be summarized
Performance Study of YOLOv5 and Faster R-CNN for Autonomous Navigation around Non-Cooperative Targets
Autonomous navigation and path-planning around non-cooperative space objects
is an enabling technology for on-orbit servicing and space debris removal
systems. The navigation task includes the determination of target object
motion, the identification of target object features suitable for grasping, and
the identification of collision hazards and other keep-out zones. Given this
knowledge, chaser spacecraft can be guided towards capture locations without
damaging the target object or without unduly the operations of a servicing
target by covering up solar arrays or communication antennas. One way to
autonomously achieve target identification, characterization and feature
recognition is by use of artificial intelligence algorithms. This paper
discusses how the combination of cameras and machine learning algorithms can
achieve the relative navigation task. The performance of two deep
learning-based object detection algorithms, Faster Region-based Convolutional
Neural Networks (R-CNN) and You Only Look Once (YOLOv5), is tested using
experimental data obtained in formation flight simulations in the ORION Lab at
Florida Institute of Technology. The simulation scenarios vary the yaw motion
of the target object, the chaser approach trajectory, and the lighting
conditions in order to test the algorithms in a wide range of realistic and
performance limiting situations. The data analyzed include the mean average
precision metrics in order to compare the performance of the object detectors.
The paper discusses the path to implementing the feature recognition algorithms
and towards integrating them into the spacecraft Guidance Navigation and
Control system.Comment: 12 pages, 10 figures, 9 tables, IEEE Aerospace Conference 202
NASA Automated Rendezvous and Capture Review. A compilation of the abstracts
This document presents a compilation of abstracts of papers solicited for presentation at the NASA Automated Rendezvous and Capture Review held in Williamsburg, VA on November 19-21, 1991. Due to limitations on time and other considerations, not all abstracts could be presented during the review. The organizing committee determined however, that all abstracts merited availability to all participants and represented data and information reflecting state-of-the-art of this technology which should be captured in one document for future use and reference. The organizing committee appreciates the interest shown in the review and the response by the authors in submitting these abstracts
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