IRIS: High-fidelity Perception Sensor Modeling for Closed-Loop Planetary Simulations
No abstract provided
Engineering topics
Publications and source records attributed to Gaut, Aaron.
No abstract provided
Perception plays a key role in autonomous and semi-autonomous planetary exploration vehicles. For instance, landers can use computer vision techniques for identifying safe landing locations, aerial vehicles use cameras as navigation sensors, and planetary rovers use them for localization and hazard detection. Engineering simulations of such systems requires the accurate modeling of perception and vision sensors for simulating autonomy scenarios. In addition, the modeling of sensors for landers, aerial and ground vehicles requires the ability to handle large and high-resolution terrains, the accurate modeling of illumination, hi-fidelity rendering via ray/path tracing and the inclusion of sensor characteristics. Vision sensor models strive to simulate sensor reality by using physics principles to model the interaction of light and objects. Furthermore, high frame rate performance is highly desirable for in-the-loop simulations involving vehicle dynamics and control software. In this paper we describe a new sensor modeling capability called Inter-planetary Rendering for Imaging and Sensors (IRIS) that meets these requirements for the real-time and high-fidelity simulation of vision sensors for planetary aerospace and robotics applications.
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DARTS is a rigid/flexible multibody dynamics toolkit for themodeling and simulation of aerospace and robotic vehicles forengineering applications. In this paper we describe an on-line,browser-based environment using Jupyter notebooks to supporttraining needs for the DARTS software. The suite of curated tutorial notebooks is organized into different topic areas, and intomultiple themes within each topic area. The notebooks within atheme use a progression of examples for users to expand theirunderstanding of the software. The topic areas include one onthe DARTS multibody dynamics software and another one on thetheory underlying the multibody dynamics formulation. We alsodescribe a number of Jupyter extensions that were used - andsome developed in house - to enhance the notebook interface foruse with the dynamics simulation software. One significant extension we implemented allows the embedding of live 3D visualizations within simulation notebooks.
This paper presents ARIEL (Autonomous Ranking and Interrogation of Excavation Location), an autonomy system for selecting an excavation site on-board for NASA’s Europa Lander Mission Concept. Historically, excavation site selection has been performed by a lengthy ground-in-the-loop (GITL) process involving manual inspections, assessments, and decision making in past missions. However, as Europa Lander would have approximately 20 days of lifetime after the landing, many surface activities, including excavation site selection, must be autonomously performed on-board. This paper describes the overall system of ARIEL as well as its two major algorithmic components: vision-based candidate selection and smart interrogation, which estimates the physical properties of the icy surface through physical contact with the robotic arm’s endeffector. Preliminary results are presented using images from Earth analogue sites. The Europa Lander mission returned to the formulation phases in early 2019 while ARIEL was at an early stage of development. Described in this paper is a snapshot of ARIEL as of the project suspension. This paper also describes the remaining challenges to be solved, should the mission resume in the future.