An official website of the United States government
Abstract The deformability of soft material robots provides them with the ability to transform between complex shapes and forms. This unique ability facilitates Modular Soft Robots (MSoRos) to assemble and reconfigure into different configurations, e.g., planar and spherical. These topologies display widely different locomotion modes that are desirable to navigate different environments, e.g., crawling or rolling for these cases. This research presents topology design and optimization methodology of MSoRos capable of both homogeneous and heterogeneous reconfiguration in spherical and planar configurations. Homogeneous reconfiguration refers to the scenario when all the modules are identical, while the heterogeneous contains nonidentical modules. The sequential design approach uses a polyhedron (Archimedean or Platonic) as the base solid to define module characteristics. As the design processes involve nonlinear projections, the base polyhedron also dictates the type of reconfiguration—heterogeneous (Archimedean) or homogeneous (Platonic). Thereafter, it applies the polyhedron vertex alignment principle to ensure geometric alignment of the modules during reconfiguration. Planar and spherical distortion metrics are defined to quantify distortions due to reconfiguration. Subsequently, the optimal topology is obtained by minimizing a cost function that is a weighted sum of the two distortion metrics. The result is a set of MSoRos capable of distinct 1D and 2D planar configurations (both heterogeneous and homogeneous) and multiple 3D spherical configurations of varying radii (both heterogeneous and homogeneous). The methodology is validated on a MSoRo system based on the combination of a cuboctahedron (Archimedean solid) and a cube and an octahedron (Platonic solids).
more »
« less
A Universal In-Place Reconfiguration Algorithm for Sliding Cube-Shaped Robots in a Quadratic Number of Moves
In the modular robot reconfiguration problem, we are given n cube-shaped modules (or robots) as well as two configurations, i.e., placements of the n modules so that their union is face-connected. The goal is to find a sequence of moves that reconfigures the modules from one configuration to the other using "sliding moves," in which a module slides over the face or edge of a neighboring module, maintaining connectivity of the configuration at all times. For many years it has been known that certain module configurations in this model require at least Ω(n²) moves to reconfigure between them. In this paper, we introduce the first universal reconfiguration algorithm - i.e., we show that any n-module configuration can reconfigure itself into any specified n-module configuration using just sliding moves. Our algorithm achieves reconfiguration in O(n²) moves, making it asymptotically tight. We also present a variation that reconfigures in-place, it ensures that throughout the reconfiguration process, all modules, except for one, will be contained in the union of the bounding boxes of the start and end configuration.
more »
« less
- Award ID(s):
- 2348067
- PAR ID:
- 10563320
- Editor(s):
- Mulzer, Wolfgang; Phillips, Jeff M
- Publisher / Repository:
- Schloss Dagstuhl – Leibniz-Zentrum für Informatik
- Date Published:
- Volume:
- 293
- ISSN:
- 1868-8969
- ISBN:
- 978-3-95977-316-4
- Page Range / eLocation ID:
- 293-293
- Subject(s) / Keyword(s):
- modular reconfigurable robots sliding cube model reconfiguration Theory of computation → Computational geometry
- Format(s):
- Medium: X Size: 14 pages; 829932 bytes Other: application/pdf
- Size(s):
- 14 pages 829932 bytes
- Right(s):
- Creative Commons Attribution 4.0 International license; info:eu-repo/semantics/openAccess
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Thermoelectric generation (TEG) has increasingly drawn attention for being environmentally friendly. A few researches have focused on improving TEG efficiency at system level on vehicle radiators. The most recent reconfiguration algorithm shows improvement on performance but suffers from major drawback on computational time and energy overhead, and non-scalability in terms of array size and processing frequency. In this paper, we propose a novel TEG array reconfiguration algorithm that determines near-optimal configuration with an acceptable computational time. More precisely, with O(N) time complexity, our prediction-based fast TEG reconfiguration algorithm enables all modules to work at or near their maximum power points (MPP). Additionally, we incorporate prediction methods to further reduce the runtime and switching overhead during the reconfiguration process. Experimental results present 30% performance improvement, almost 100 χ reduction on switching overhead and 13 χ enhancement on computational speed compared to the baseline and prior work. The scalability of our algorithm makes it applicable to larger scale systems such as industrial boilers and heat exchangers.more » « less
-
We consider the problem of resource provisioning for real-time cyber-physical applications in an open system environment where there does not exist a global resource scheduler that has complete knowledge of the real-time performance requirements of each individual application that shares the resources with the other applications. Regularity-based Resource Partition (RRP) model is an effective strategy to hierarchically partition and assign various resource slices among such applications. However, previous work on RRP model only discusses uniform resource environment, where resources are implicitly assumed to be synchronized and clocked at the same frequency. The challenge is that a task utilizing multiple resources may experience unexpected delays in non-uniform environments, where resources are clocked at different frequencies. This paper extends the RRP model to non-uniform multi-resource open system environments to tackle this problem. It first introduces a novel composite resource partition abstraction and then proposes algorithms to construct and reconfigure the composite resource partitions. Specifically, theAcyclic Regular Composite Resource Partition Scheduling (ARCRP-S)algorithm constructs regular composite resource partitions and theAcyclic Regular Composite Resource Partition Dynamic Reconfiguration (ARCRP-DR)algorithm reconfigures the composite resource partitions in the run time upon requests of partition configuration changes. Our experimental results show that compared with state-of-the-art methods, ARCRP-S can prevent unexpected resource supply shortfall and improve the schedulability up to 50%. On the other hand, ARCRP-DR can guarantee the resource supply during the reconfiguration with moderate computational overhead.more » « less
-
Advances in vision processing have ignited a proliferation of mobile vision applications, including augmented reality. However, limited by the inability to rapidly reconfigure sensor operation for performance-efficiency tradeoffs, high power consumption causes vision applications to drain the device's battery. To explore the potential impact of enabling rapid reconfiguration, we use a case study around marker-based pose estimation to understand the relationship between image frame resolution, task accuracy, and energy efficiency. Our case study motivates that to balance energy efficiency and task accuracy, the application needs to dynamically and frequently reconfigure sensor resolution. To explore the latency bottlenecks to sensor resolution reconfiguration, we define and profile the end-to-end reconfiguration latency and frame-to-frame latency of changing capture resolution on a Google LG Nexus 5X device. We identify three major sources of sensor resolution reconfiguration latency in current Android systems: (i) sequential configuration patterns, (ii) expensive system calls, and (iii) imaging pipeline delay. Based on our intuitions, we propose a redesign of the Android camera system to mitigate the sources of latency. Enabling smooth transitions between sensor configurations will unlock new classes of adaptive-resolution vision applications.more » « less
-
Mobile vision systems would benefit from the ability to situationally sacrifice image resolution to save system energy when imaging detail is unnecessary. Unfortunately, any change in sensor resolution leads to a substantial pause in frame delivery -- as much as 280 ms. Frame delivery is bottlenecked by a sequence of reconfiguration procedures and memory management in current operating systems before it resumes at the new resolution. This latency from reconfiguration impedes the adoption of otherwise beneficial resolution-energy tradeoff mechanisms. We propose Banner as a media framework that provides a rapid sensor resolution reconfiguration service as a modification to common media frameworks, e.g., V4L2. Banner completely eliminates the frame-to-frame reconfiguration latency (226 ms to 33 ms), i.e., removing the frame drop during sensor resolution reconfiguration. Banner also halves the end-to-end resolution reconfiguration latency (226 ms to 105 ms). This enables a more than 49% reduction of system power consumption by allowing continuous vision applications to reconfigure the sensor resolution to 480p compared with downsampling from 1080p to 480p, as measured in a cloud-based offloading workload running on a Jetson TX2 board. As a result, Banner unlocks unprecedented capabilities for mobile vision applications to dynamically reconfigure sensor resolutions to balance the energy efficiency and task accuracy tradeoff.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.4230/LIPIcs.SoCG.2024.1
