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In English
2022 International Symposium on Measurement and Control in Robotics (ISMCR)
Angol nyelvű
Megjelent: IEEE
2022
Konferencia:
2022 International Symposium on Measurement and Control in Robotics (ISMCR) 2022-09-28 [Houston (TX), Amerikai Egyesült Államok]
Azonosítók
MTMT: 33267500
ISBN:
9781665454964
Fejezetek
Bencsik Blanka et al. Efficient Neural Network Pruning Using Model-Based Reinforcement Learning. (2022) Megjelent: 2022 International Symposium on Measurement and Control in Robotics (ISMCR) pp. 1-8
Finta Barnabas et al. Control of a differentially flat 2D overhead crane using the ADRC philosophy. (2022) Megjelent: 2022 International Symposium on Measurement and Control in Robotics (ISMCR) pp. 1-6
Izso Andras et al. Development of an environment mapping robot using polygonal map representation. (2022) Megjelent: 2022 International Symposium on Measurement and Control in Robotics (ISMCR) pp. 1-8
Olasz-Szabo Sara et al. Path Planning for Data Collection Multiagent System in a Sensing Field with Obstacles. (2022) Megjelent: 2022 International Symposium on Measurement and Control in Robotics (ISMCR) pp. 1-7
Szabo Daniel et al. A proposal for an FPGA-based graphical pipeline for virtual depth image generation. (2022) Megjelent: 2022 International Symposium on Measurement and Control in Robotics (ISMCR) pp. 1-5
Szanto Matyas et al. Self-Supervised Occlusion Detection and Avoidance using Differentiable Rendering. (2022) Megjelent: 2022 International Symposium on Measurement and Control in Robotics (ISMCR) pp. 1-8
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Chicago
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Nyomtatás
2023-02-09 10:25
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Hivatkozás stílusok:
IEEE
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APA
Chicago
Harvard
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