Search NASA⌕ Search

SEARCH · Search NASA

Results for “multi-agent”

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.

259 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Assemblers: A Modular, Reconfigurable Manipulator for Autonomous in-Space Assembly

This paper presents a modular, reconfigurable robotic manipulator for autonomous in-space assembly. On-orbit and planetary surface assembly is a challenging domain that encompasses various technological thrusts to support human exploration, science, and technology demonstration missions. The system architecture presented here addresses the need for robotic assembly of structures in locations that prohibit constant human oversight and/or teleoperation. This system is capable of scheduling, reconfiguring itself, and executing structural assembly tasks with the ability to assess construction and correct errors during the assembly as needed. An example concept of operations is presented. Details of the mechanical and electrical design of the robotic modules as well as the dynamic modeling approach, control algorithms, and sensing and perception systems are also provided.

robotics↗
NASA NTRS2021

Inertial Transfer Concept for Autonomous In-Space Assembly

Rocket payload and fairing size have placed strict mass and volume limitations on single launch in-space structures. These limitations are what motivates In-Space Assembly (ISA) and where Inertial Transfer has potential to improve or augment ISA capabilities by utilizing a multiagent autonomous system to transport untethered payloads. The Inertial Transfer concept is presented including autonomy capabilities, potential risks, and three system configurations. These configurations are based on the autonomous system’s available sensor coverage that tracks the payload’s state. Participating agents have well-defined roles, expectations, and assumptions in their physical organization and coordination of actions depending the mode of operation. The sensor information available to participating agents is considered as it affects the uncertainty in the estimated state and the actions required to capture the payload.

Multi-Agent↗
NASA NTRSDate not supplied

APIS: Honeybee Foraging Task Assignment for Use in Uncertain and Unreliable Environments

Multiagent Cyber-Physical-Human (CPH) systems in realistic environments operate under uncertain conditions. Communication among agents, aimed at reducing the uncertainty, is itself subject to uncertainty. We propose to manage uncertainties in autonomous, long-duration operations of multiagent systems via a modified Honeybee Foraging (HBF) behavioral scheme. The resulting system, Autonomous Persistent Intelligent Swarm (APIS),incorporates two new behaviors to ameliorate informational uncertainty. When “scouting”, agents are tasked based on informational quality and reliability rather than solely on priorities. When “dancing”, agents are tasked to rendezvous with other dancing agents to exchange information at close range, where successful communication is guaranteed. When coupled with uncertainty-aware modeling across agents, these behaviors improve situational awareness and resilience of the system, enabling it to function under more uncertain conditions arising during long-duration missions.

Autonomous systems↗
NASA NTRSDate not supplied

APIS: Honeybee Foraging Task Assignment for Use in Uncertain and Unreliable Environments

Multiagent Cyber-Physical-Human (CPH) systems in realistic environments operate under uncertain conditions. Communication among agents, aimed at reducing the uncertainty, is itself subject to uncertainty. We propose to manage uncertainties in autonomous, long-duration operations of multiagent systems via a modified Honeybee Foraging (HBF) behavioral scheme. The resulting system, Autonomous Persistent Intelligent Swarm (APIS),incorporates two new behaviors to ameliorate informational uncertainty. When “scouting”, agents are tasked based on informational quality and reliability rather than solely on priorities. When “dancing”, agents are tasked to rendezvous with other dancing agents to exchange information at close range, where successful communication is guaranteed. When coupled with uncertainty-aware modeling across agents, these behaviors improve situational awareness and resilience of the system, enabling it to function under more uncertain conditions arising during long-duration missions.

Autonomous systems↗