NASA NTRS · 20150007481
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Ono, Masahiro, Balaram, J. (Bob), Kuwata, Yoshiaki. 2013-06-18. Mixed-Strategy Chance Constrained Optimal Control. https://ntrs.nasa.gov/citations/20150007481
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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.
In this talk, I describe how NASA Ames has been developing and testing robots for space exploration. In our research, we have focused on studying how human-robot teams can increase the performance, reduce the cost, and increase the success of space missions. A key tenet of our work is that humans and robots should support one another in order to compensate for limitations of manual control and autonomy. This principle has broad applicability beyond space exploration. Thus, I will conclude by discussing how we have worked with Nissan to apply our methods to self-driving cars, enabling humans to support autonomous vehicles operating in unpredictable and difficult situations.
In this talk, I will describe how NASA Ames has been studying how human-robot teams can increase the performance, reduce the cost, and increase the success of a variety of endeavors. The central premise of our work is that humans and robots should support one another in order to compensate for limitations of automation and manual control. This principle has broad applicability to a wide range of domains, environments, and situations. At the same time, however, effective human-robot teaming requires communication, coordination, and collaboration -- all of which present significant research challenges. I will discuss some of the ways that NASA Ames is addressing these challenges and present examples of our work involving planetary rovers, free-flying robots, and self-driving cars.