Safe navigation on hazardous terrain
This paper presents a new strategy for autonomous navigation of field mobile robots on hazardous natural terrain using a fuzzy logic approach and a novel measure of terrain transversability.
SEARCH · Engineering Papers
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
This paper presents a new strategy for autonomous navigation of field mobile robots on hazardous natural terrain using a fuzzy logic approach and a novel measure of terrain transversability.
This paper is about vision-based navigation of Micro Aerial Vehicles (MAVs) at night. Despite it being dark almost half of the time, most of the work to date has addressed only daytime operations. Enabling autonomous night-time operation of MAVs with low SWaP on-board sensing capabilities is still an open problem in current robotics research. In this work, we take a step in this direction and study the problem in the context of vision-based autonomous landing at night using thermal-infrared cameras.
The OASSIS Year 3 project continues to innovate with three goals: 1) transition to a generic configuration compatible with GNC flight software, 2) implement a new, computationally-efficient TRN algorithm for lunar landing, and 3) integrate with the a HWIL testbed to validate lunar landing GNC systems. This project enables lunar lander GNC flight software to be tested dynamically without the need of a costly flight campaign and without the risk of catastrophic hardware loss. Additionally, the TRN algorithm development and testing enhances the state-of-the-art in pinpoint landing navigation, ultimately improving the overall landing accuracy, safety, and reliability of a crewed lunar landing mission.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
USC’s Space Engineering Research Center (SERC) and the Jet Propulsion Laboratory (JPL) have created a unique “octopus” tentacle end effector for robotic systems that uses formable electro-adhesion (EA) and Gecko adhesion capture cloth material. REACCH, or Reactive Electro-Adhesive Capture ClotH, supports soft capture of objects of any size, shape, surface finish or material on-orbit. To-date SERC/JPL have developed initial tentacles and backing spines that can control the EA/G material. Prototypes with two tentacles have been demonstrated and tested on a 3-DOF air bearing device in 1g in one plane of grip. The results show promise to further develop this new type of grappling mechanism able to make first soft contact with an object, with a technology that merges compliance and control elements for future on-orbit servicing and assembly missions. This paper presents the initial design and test results on this type of system.
No abstract provided
Explore the source record for details and available documents.
Explore the source record for details and available documents.
No abstract provided
The emergence and development of advanced technologies and vehicle types have created a growing demand for new forms of flight operations. These new and increasingly complex operational paradigms, such as Advanced and Urban Air Mobility (AAM/UAM), present regulatory authorities and the aviation community with several design-and-implementation challenges – particularly for highly autonomous vehicles. An overarching and daunting task is to develop protocols that can integrate these operations without compromising safety or disrupting traditional airspace operations. A shift toward a more predictive, autonomous, risk mitigation capability becomes critical to meet this challenge. This paper proposes and evaluates a computationally-efficient path planning approach to perform pre-flight planning and autonomous in-flight re-routing to minimize exposures to selected hazards. In our evaluation, hazards associated with degraded and missing critical GPS navigation data are considered. In this paper, we first present a high-performance computing path planning approach based on an adapted Bellman-Ford algorithm, developed in the CUDA programming language. Using the adapted path planning algorithm, we test this algorithm when encountering issues with GPS quality, and deliver an implementation that can produce flight paths that minimize exposure to risks, while maintaining a low computational burden. In our evaluation, the computation of periodic and aperiodic path updates are evaluated, prioritizing specific events as triggers for updates, based on changes to satellite availability. These critical events can lead to significant exposure to navigational hazards if not dealt with correctly.