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19. Multi-segmented Adaptive Feet for Versatile Legged Locomotion in Natural Terrain

Author

Chatterjee, Abhishek, Mo, An, Kiss, Bernadett, Gönen, Emre Cemal, and Badri-Spröwitz, Alexander

Subjects
FOS: Computer and information sciences, Computer Science - Robotics, Robotics (cs.RO)
Abstract

Most legged robots are built with leg structures from serially mounted links and actuators and are controlled through complex controllers and sensor feedback. In comparison, animals developed multi-segment legs, mechanical coupling between joints, and multi-segmented feet. They run agile over all terrains, arguably with simpler locomotion control. Here we focus on developing foot mechanisms that resist slipping and sinking also in natural terrain. We present first results of multi-segment feet mounted to a bird-inspired robot leg with multi-joint mechanical tendon coupling. Our one- and two-segment, mechanically adaptive feet show increased viable horizontal forces on multiple soft and hard substrates before starting to slip. We also observe that segmented feet reduce sinking on soft substrates compared to ball-feet and cylinder feet. We report how multi-segmented feet provide a large range of viable centre of pressure points well suited for bipedal robots, but also for quadruped robots on slopes and natural terrain. Our results also offer a functional understanding of segmented feet in animals like ratite birds. 10.48550/arXiv.2209.08499 ispartof: Proceedings of ICRA 2023 ispartof: 2023 IEEE International Conference on Robotics and Automation location:London, UK date:30 May - 2 Jun 2023 status: accepted

Published
2023

39. To Offload or Not? An Analysis of Big Data Offloading Strategies from Edge to Cloud

Author

Raghubir Singh, Jozsef Kovacs, and Tamas Kiss

Subjects
Computational complexity, WLAN, Server workload, Big Data analytics, Application-level orchestration, Cloud-to-Edge continuum, WAN
Abstract

Major research efforts have been recently made to develop resource orchestration solutions to flexibly link edge nodes with centralised cloud resources so as to maximise the efficiency with which such a continuum of resources can be accessed by users. In this context, we consider the case of Big Data analytics in which total task completion time reductions can be achieved by routing tasks initially to edge servers and subsequently to cloud resources. We demonstrate that the task complexity of the computational jobs, the Wide Area Network (WAN) speed and the potential overload of edge servers (as reflected by CPU workloads) are crucial for achieving total task completion time reductions by offloading from edge to cloud resources. The edge-cloud orchestrators are situated in the edge nodes and, therefore, require continuous access to the parameters of WAN speeds (and their fluctuations), edge server CPU workload and the task complexities in Big Data analytics requirements to make accurate edge-to-cloud offloading decisions. With favourable values for these three parameters, large reductions in completion times can result from transfer of large-scale data from edge nodes to cloud resources, which can reduce the completion times by up to 97% and meet client deadlines for computational tasks with responsive and agile solutions.

Published
2022

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