Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Bueskens, Christof"'
Deep reinforcement learning (DRL) allows a system to interact with its environment and take actions by training an efficient policy that maximizes self-defined rewards. In autonomous driving, it can be used as a strategy for high-level decision makin
Externí odkaz:
http://arxiv.org/abs/2407.01216
Global navigation satellite systems readily provide accurate position information when localizing a robot outdoors. However, an analogous standard solution does not exist yet for mobile robots operating indoors. This paper presents an integrated fram
Externí odkaz:
http://arxiv.org/abs/2310.05198
Akademický článek
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The development of driving functions for autonomous vehicles in urban environments is still a challenging task. In comparison with driving on motorways, a wide variety of moving road users, such as pedestrians or cyclists, but also the strongly varyi
Externí odkaz:
http://arxiv.org/abs/1911.03139
We present a control approach for autonomous vehicles based on deep reinforcement learning. A neural network agent is trained to map its estimated state to acceleration and steering commands given the objective of reaching a specific target state whi
Externí odkaz:
http://arxiv.org/abs/1909.12153
Publikováno v:
2018 Annual American Control Conference (ACC), Milwaukee, WI, 2018, pp. 3756-3762
We present an optimization-based approach for trajectory planning and control of a maneuverable melting probe with a high number of binary control variables. The dynamics of the system are modeled by a set of ordinary differential equations with a pr
Externí odkaz:
http://arxiv.org/abs/1804.06299
Publikováno v:
In IFAC PapersOnLine 2022 55(16):172-177
Publikováno v:
In IFAC PapersOnLine 2021 54(14):138-143
Autor:
Knauer, Matthias, Büskens, Christof
Publikováno v:
In IFAC PapersOnLine 2020 53(2):17572-17577
Publikováno v:
In IFAC PapersOnLine 2020 53(2):15077-15083