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pro vyhledávání: '"Vahs, Matti"'
Control invariant sets play an important role in safety-critical control and find broad application in numerous fields such as obstacle avoidance for mobile robots. However, finding valid control invariant sets of dynamical systems under input limita
Externí odkaz:
http://arxiv.org/abs/2411.04833
Useful robot control algorithms should not only achieve performance objectives but also adhere to hard safety constraints. Control Barrier Functions (CBFs) have been developed to provably ensure system safety through forward invariance. However, they
Externí odkaz:
http://arxiv.org/abs/2407.12624
Autor:
Vahs, Matti, Tumova, Jana
This paper addresses the problem of safety-critical control of autonomous robots, considering the ubiquitous uncertainties arising from unmodeled dynamics and noisy sensors. To take into account these uncertainties, probabilistic state estimators are
Externí odkaz:
http://arxiv.org/abs/2309.12857
Ensuring safety in real-world robotic systems is often challenging due to unmodeled disturbances and noisy sensor measurements. To account for such stochastic uncertainties, many robotic systems leverage probabilistic state estimators such as Kalman
Externí odkaz:
http://arxiv.org/abs/2309.06499
Autor:
Vahs, Matti, Tumova, Jana
Uncertainties arising in various control systems, such as robots that are subject to unknown disturbances or environmental variations, pose significant challenges for ensuring system safety, such as collision avoidance. At the same time, safety speci
Externí odkaz:
http://arxiv.org/abs/2309.06494
In many real-world robotic scenarios, we cannot assume exact knowledge about a robot’s state due to unmodeled dynamics or noisy sensors. Planning in belief space addresses this problem by tightly coupling perception and planning modules to obtain t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______260::6bb9d0a793867fffaff33f35d76dafc4
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-324917
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-324917
In many real-world robotic scenarios, exact knowledge about a robot’s state cannot be assumed due to unmodeled dynamics or noisy sensors. Planning in belief space provides an approach that addresses this problem by tightly coupling perception and p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______260::f2759a0c9a7eb35b215d74735cb8f466
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-324916
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-324916