BigDipper: A hyperscale BFT system with short term censorship resistance

Byzantine-fault-tolerant (BFT) protocols underlie a variety of decentralized applications including payments, auctions, data feed oracles, and decentralized social networks. In most leader-based BFT protocols, an important property that has been missing is the censorship resistance of transaction in the short term. The protocol should provide inclusion guarantees in the next block height even if the current and future leaders have the intent of censoring. In this paper, we present a BFT system, BigDipper, that achieves censorship resistance while providing fast confirmation for clients and hyperscale throughput. The core idea is to decentralize inclusion of transactions by allowing every BFT replica to create their own mini-block, and then enforcing the leader on their inclusions. To achieve this, BigDipper creates a modular system made of three components. First, we provide a transaction broadcast protocol used by clients as an interface to achieve a spectrum of probabilistic inclusion guarantees. Afterwards, a distribution of BFT replicas will receive the client's transactions and prepare mini-blocks to send to the data availability (DA) component. The DA component characterizes the censorship resistant properties of the whole system. We design three censorship resistant DA (DA-CR) protocols with distinct properties captured by three parameters and demonstrate their trade-offs. The third component interleaves the DA-CR protocols into the consensus path of leader based BFT protocols, it enforces the leader to include all the data from the DA-CR into the BFT block. We demonstrate an integration with a two-phase Hotstuff-2 BFT protocol with minimal changes. BigDipper is a modular system that can switch the consensus to other leader based BFT protocol including Tendermint

Efficient Global Navigational Planning in 3D Structures based on Point Cloud Tomography

Navigation in complex 3D scenarios requires appropriate environment representation for efficient scene understanding and trajectory generation. We propose a highly efficient and extensible global navigation framework based on a tomographic understanding of the environment to navigate ground robots in multi-layer structures. Our approach generates tomogram slices using the point cloud map to encode the geometric structure as ground and ceiling elevations. Then it evaluates the scene traversability considering the robot's motion capabilities. Both the tomogram construction and the scene evaluation are accelerated through parallel computation. Our approach further alleviates the trajectory generation complexity compared with planning in 3D spaces directly. It generates 3D trajectories by searching through multiple tomogram slices and separately adjusts the robot height to avoid overhangs. We evaluate our framework in various simulation scenarios and further test it in the real world on a quadrupedal robot. Our approach reduces the scene evaluation time by 3 orders of magnitude and improves the path planning speed by 3 times compared with existing approaches, demonstrating highly efficient global navigation in various complex 3D environments. The code is available at: https://github.com/byangw/PCT_planner.Comment: 11 pages, 9 figures, submitted to IEEE/ASME Transactions on Mechatronic

Patched Line Segment Learning for Vector Road Mapping

This paper presents a novel approach to computing vector road maps from satellite remotely sensed images, building upon a well-defined Patched Line Segment (PaLiS) representation for road graphs that holds geometric significance. Unlike prevailing methods that derive road vector representations from satellite images using binary masks or keypoints, our method employs line segments. These segments not only convey road locations but also capture their orientations, making them a robust choice for representation. More precisely, given an input image, we divide it into non-overlapping patches and predict a suitable line segment within each patch. This strategy enables us to capture spatial and structural cues from these patch-based line segments, simplifying the process of constructing the road network graph without the necessity of additional neural networks for connectivity. In our experiments, we demonstrate how an effective representation of a road graph significantly enhances the performance of vector road mapping on established benchmarks, without requiring extensive modifications to the neural network architecture. Furthermore, our method achieves state-of-the-art performance with just 6 GPU hours of training, leading to a substantial 32-fold reduction in training costs in terms of GPU hours

Wireless Monitoring of Small Strains in Intelligent Robots via a Joule Heating Effect in Stretchable Graphene–Polymer Nanocomposites

Flexible strain sensors are an important component for future intelligent robotics. However, the majority of current strain sensors must be electrically connected to a corresponding monitoring system via conducting wires, which increases system complexity and restricts the working environment for monitoring strains. Here, stretchable graphene–polymer nanocomposites that act as strain sensors using a Joule heating effect are reported. When the resistance of the sensor changes in response to a strain, the resulting change in temperature is wirelessly detected in an intelligent robot. By engineering and optimizing the surface structure of graphene–polymer nanocomposites, the fabricated strain sensors exhibit excellent stability when subjected to periodic temperature signals over 400 cycles while being periodically strained and deliver a high strain sensitivity of 7.03 × 10−4 °C−1 %−1 for strain levels of 0% to 30%. As a wearable electronic device, the approach provides the capability to wirelessly monitor small strains for intelligent robots at a high strain resolution of ≈0.1%. Moreover, when the strain sensing system operates as a multichannel structure, it allows precise strain detection simultaneously, or in sequence, for each finger of an intelligent robot.</p