Engineering topics
Publications and source records attributed to Nesnas, Issa.
We present a novel Shape from Silhouette (SfS) algorithm to estimate the physical and dynamical properties of a small body - such as an asteroid or comet - from periodic images taken from a distant approaching spacecraft.
This paper presents a probabilistic guidance algorithm for a swarm of assets deployed from the back shell of the Mars spacecraft. Such a swarm could provide valuable science data, with large spatiotemporal variation, from the Martian surface. Our probabilistic swarm guidance algorithm maximizes the coverage area of the swarm while uniformly distributing the assets on the Martian surface and guaranteeing strong connectivity of the swarm’s communication network topology. Numerical simulations demonstrate the effectiveness and versatility of our probabilistic swarm guidance algorithm.
Current methods for pose and shape estimation of small bodies, such as comets and asteroids, rely on extensive ground support and significant use of radiometric measurements using the Deep Space Network. The Stereo-Photoclinometry (SPC) technique is currently used to provide detailed topological information about a small body as well as its absolute orientation and position. While this technique has produced very accurate estimates, the core algorithm cannot be run in real-time and requires a team of scientists on the ground who must communicate with the spacecraft in order to oversee SPC operations. Autonomous onboard navigation addresses these limitations by eliminating the need for human oversight. In this paper, we present an optimization-based estimation algorithm for navigation that allows the spacecraft to autonomously approach and maneuver around an unknown small body by mapping its geometric shape, estimating its orientation, and simultaneously determining the trajectory of the center of mass of the small body. We show the effectiveness of the proposed algorithm using simulated data from a previous flight mission to Comet 67P.
The Axel rappelling rover was designed to enable access to intriguing and important science sites that lie in difficult terrains that are inaccessible to conventional rovers. Extended autonomous rappelling calls for careful control of tethertension,precisemanagementoftetherspooling,andsome measure of shock tolerance. This paper covers the design and testing of a first-generation tether management system (TMS) for Axel. The system uses a double bull-wheel capstan driven by a low-stiffness series elastic actuator (SEA) to provide tension control and decouple internal spooling tension from external tether tension. The benefit of the series elastic actuator is two-fold. First, it permits closed-loop tether tension control with lower mass and power consumption than a comparable torque motor. Second, it improves the shock/drop tolerance of the rappelling system both while moving and when the system is inactive with the motors locked. Experiments on the new TMS show that this design performs well in keeping nearly constant spooling tension while rejecting large dynamic disturbances at the output. While the SEA is very effective at maintaining a given tension contribution, the additional effects of friction and the unique mechanical properties of the tether result in substantial errors in the measured output tension. Upcoming field trials will be used to evaluate the effectiveness and sufficiency of this system when integrated in Axel.
Luna has extended the length of its fiber optic shape sensing technology out to a continuous, high-fidelity measurement of a 100-meter sensor, creating a novel concept for a self-monitoring tether for robotic exploration. This tether is capable of measuring its own tension, curvature, and full 3-D shape along its entire length as it is deployed by a robotic rover. Using Optical Frequency Domain Reflectometry (OFDR) and the Rayleigh scatter signature to sense the state of a customized multicore optical fiber, the new technology is lightweight, compact, and immune to electromagnetic interference. Results are presented from tests with a 50-meter tether on the Axel Rover at JPL’s Mars Yard testing ground.
Achieving consistently high levels of productivity for surface exploration missions has been a challenge for Mars missions. While the rovers have made major discoveries and accomplished a large number of objectives, they often require a great deal of effort from the operations teams and achievingobjectives can take longer than anticipated. This paper describes the early stages of a multi-year project to investigate solutions for enhancing surface mission productivity. A primary focus of this early stage is to conduct in-depth studies of Mars Science Laboratory science campaigns to gain a deeper understanding of the factors that impact productivity, and to use this understanding to identify potential changes to flight software and ground operations practices to increase productivity. We present the science campaigns we have selected along with a conceptual model of how surface missions achieve objectives that is used to guide the study. We also provide some early thoughts on the technologies, and their interactions, which we believe will play an important role in addressing surface mission productivity challenges.We are in the early stages of a multi-year project to studyand address productivity challenges of future surface missions. We have identified campaigns from the MSL missionfor study which we believe will yield valuable informationabout the nature of surface mission productivity challenges.Based on preliminary analysis from the data collected weanticipate that the lessons from these case studies will helpdevelop and mature our concepts for changes to flight andground systems to address these challenges.While the focus of our work is on Mars rover missions, webelieve the concepts in the work will be applicable to a variety of in-situ explorers, including Venus, and Titan, as wellas orbital missions, such as the Europa orbiter. These missions will also benefit from the ability to adapt and respondto the latest state of the spacecraft and its environment.
Modular exploratory robotic vehicles that will be able to reconfigure themselves in the field are undergoing development. Proposed for use in exploration of the surfaces of Mars and other remote planets, these vehicles and others of similar design could also be useful for exploring hostile terrain on Earth.
This paper presents an architecture and a set of technology for performing autonomous science and commanding for a planetary rover. The MER rovers have outperformed all expectations by lasting over 1100 sols (or Martian days), which is an order of magnitude longer than their original mission goal. The longevity of these vehicles will have significant effects on future mission goals, such as objectives for the Mars Science Laboratory rover mission (scheduled to fly in 2009) and the Astrobiology Field Lab rover mission (scheduled to potentially fly in 2016). Common objectives for future rover missions to Mars include the handling of opportunistic science, long-range or multi-sol driving, and onboard fault diagnosis and recovery. To handle these goals, a number of new technologies have been developed and integrated as part of the CLARAty architecture. CLARAty is a unified and reusable robotic architecture that was designed to simplify the integration, testing and maturation of robotic technologies for future missions. This paper focuses on technology comprising the CLARAty Decision Layer, which was designed to support and validate high-level autonomy technologies, such as automated planning and scheduling and onboard data analysis.
We are developing technologies to increase the autonomous capabilities of future rover missions. Our objectives are to make rovers easier to command and to enable them to make more effective use of rover resources when problems arise or when things go better than expected. We will demonstrate OASIS (Onboard Analysis Science Investigation System) which combined planning and scheduling techniques with machine learning to enable rovers to perform robust and opportunistic science operations.
The Mars Exploration Rovers (MER'03), Spirit and Opportunity, represent the state of the art in rover operations on Mars. This paper presents validation experiments of different visual tracking algorithms using the rover's navigation camera.
This paper describes how dynamic planning and scheduling techniques can be used onboard a rover to autonomously adjust rover activities in support of science goals. These goals could be identified by scientists on the ground or could be identified by onboard data-analysis software. Several different types of dynamic decisions are described, including the handling of opportunistic science goals identified during rover traverses, preserving high priority science targets when resources, such as power, are unexpectedly over-subscribed, and dynamically adding additional, ground-specified science targets when rover actions are executed more quickly than expected. After describing our specific system approach, we discuss some of the particular challenges we have examined to support autonomous rover decision-making. These include interaction with rover navigation and path-planning software and handling large amounts of uncertainty in state and resource estimations.
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This paper addresses a high level spatio-temporal problem, namely "absolute orientation", which arises in visual-odometry (using stereo), or registering two models created by different Structure from Motion (SFM) reconstructions. We compare the very popular method due to Horn using quaternions and our own independently derive method using the orthonormal rotation matrix R. We also introduce a novel approach for outlier rejection using spectral clustering.