Zobrazeno 1 - 10
of 120
pro vyhledávání: '"Frew, Eric"'
Autor:
Israelsen, Brett, Ahmed, Nisar R., Aitken, Matthew, Frew, Eric W., Lawrence, Dale A., Argrow, Brian M.
How can intelligent machines assess their competencies in completing tasks? This question has come into focus for autonomous systems that algorithmically reason and make decisions under uncertainty. It is argued here that machine self-confidence - a
Externí odkaz:
http://arxiv.org/abs/2407.19631
Autor:
Mazouz, Rayan, Skovbekk, John, Mathiesen, Frederik Baymler, Frew, Eric, Laurenti, Luca, Lahijanian, Morteza
This paper introduces a method of identifying a maximal set of safe strategies from data for stochastic systems with unknown dynamics using barrier certificates. The first step is learning the dynamics of the system via Gaussian process (GP) regressi
Externí odkaz:
http://arxiv.org/abs/2405.00136
In this work, a set of motion primitives is defined for use in an energy-aware motion planning problem. The motion primitives are defined as sequences of control inputs to a simplified four-DOF dynamics model and are used to replace the traditional c
Externí odkaz:
http://arxiv.org/abs/2311.10915
Verifying the performance of safety-critical, stochastic systems with complex noise distributions is difficult. We introduce a general procedure for the finite abstraction of nonlinear stochastic systems with non-standard (e.g., non-affine, non-symme
Externí odkaz:
http://arxiv.org/abs/2309.10702
Autor:
Biggie, Harel, Rush, Eugene R., Riley, Danny G., Ahmad, Shakeeb, Ohradzansky, Michael T., Harlow, Kyle, Miles, Michael J., Torres, Daniel, McGuire, Steve, Frew, Eric W., Heckman, Christoffer, Humbert, J. Sean
While the capabilities of autonomous systems have been steadily improving in recent years, these systems still struggle to rapidly explore previously unknown environments without the aid of GPS-assisted navigation. The DARPA Subterranean (SubT) Chall
Externí odkaz:
http://arxiv.org/abs/2301.00771
Leveraging autonomous systems in safety-critical scenarios requires verifying their behaviors in the presence of uncertainties and black-box components that influence the system dynamics. In this work, we develop a framework for verifying discrete-ti
Externí odkaz:
http://arxiv.org/abs/2201.00655
Autonomous systems often have complex and possibly unknown dynamics due to, e.g., black-box components. This leads to unpredictable behaviors and makes control design with performance guarantees a major challenge. This paper presents a data-driven co
Externí odkaz:
http://arxiv.org/abs/2110.05525
Autor:
Ohradzansky, Michael T., Rush, Eugene R., Riley, Danny G., Mills, Andrew B., Ahmad, Shakeeb, McGuire, Steve, Biggie, Harel, Harlow, Kyle, Miles, Michael J., Frew, Eric W., Heckman, Christoffer, Humbert, J. Sean
Artificial intelligence has undergone immense growth and maturation in recent years, though autonomous systems have traditionally struggled when fielded in diverse and previously unknown environments. DARPA is seeking to change that with the Subterra
Externí odkaz:
http://arxiv.org/abs/2110.04390
The paper proposes a reliable and robust planning solution to the long range robotic navigation problem in extremely cluttered environments. A two-layer planning architecture is proposed that leverages both the environment map and the direct depth se
Externí odkaz:
http://arxiv.org/abs/2108.00380
We present a data-driven framework for strategy synthesis for partially-known switched stochastic systems. The properties of the system are specified using linear temporal logic (LTL) over finite traces (LTLf), which is as expressive as LTL and enabl
Externí odkaz:
http://arxiv.org/abs/2104.02172