This paper establishes a stiffness model for an origami-inspired pneumatic continuum manipulator (OPM) capable of large stretch ratio and active stiffness modulation. A kinematic model is firstly established, using the piecewise constant curvature assumption, in order to describe the end-effector’s posture by configuration states. Subsequently, utilizing virtual work theory, the static model is derived, which integrates both pneumatic actuation and intrinsic elastic energy. Based on this foundation, a Cartesian compliance matrix is formulated to quantitatively predict 3D deformations under external loads. Experimental validation of stiffness model demonstrates spatial prediction accuracy with maximum errors of 2.00 mm (z-axis), 2.04◦(roll) under 500 g payloads for one module. For the OPM, tested up to 300 g loading, positional and angular errors remain below 5 mm (x-axis), 3◦(pitch). This study aims to bridge pressure-stiffness coupling and enable model-based stiffness-position control for adaptive tasks.

With the popularity of blockchains, low transaction throughput has become a significant bottleneck in applications such as cryptocurrencies. Payment channel networks (PCNs) have received attention as a way to improve throughput. However, due to the difficulty of predicting future transactions for nodes, the transactions are prone to failure when the channel balances do not meet required conditions. It has been shown that increasing buffers (queues) in PCNs can increase the success rate of transactions and throughput. Nevertheless, there is no effective transaction scheduling strategy in buffers when transaction values are flexible and variable. To solve this problem, we first formulate the Scheduling Problem in PCNs (named PSP), and then prove it is NP-hard. We design a neural network solver based on the Sequence to Sequence (Seq2Seq) architecture and train the solver using the reinforcement learning method. With the solver, we first give two scheduling strategies to maximize transaction throughput, and then design a PCN simulator for performance evaluation. Extensive experiments are conducted to show the superiority and various performances of our proposal and illustrate that our proposal can get a significant advantage in terms of the transaction throughput compared to the existing works.

This paper presents a novel pneumatic continuum module featuring high contraction-ratio, bidirectional actuation, and active stiffness regulation. The module comprises four linear pneumatic actuators integrating rigid polygon origami frame into soft bellow. This integration not only helps to regulate motion but also enhances structural strength, facilitating contraction and bending performance. The contraction resulting from vacuum pressure serves as a limiting layer for one opposing pair of actuators, while the other pair operates in a virtual antagonistic configuration, allowing for active regulation of joint stiffness through pressure control. The paper provides a detailed workflow covering the design, fabrication, and mathematical modeling of the pneumatic module. Furthermore, the paper presents verifications of the module’s actuation capacity and active variable stiffness. The findings of this study can serve as valuable references for the design of manipulators.

UAV-assisted mobile edge computing (UAV-MEC) has been proposed to offer computing resources for smart devices and user equipment. UAV cluster aided MEC rather than one UAV-aided MEC as edge pool is the newest edge computing architecture. Unfortunately, the data packet exchange during edge computing within the UAV cluster hasn't received enough attention. UAVs need to collaborate for the wide implementation of MEC, relying on the gossip-based broadcast protocol. However, gossip has the problem of long propagation delay, where the forwarding probability and neighbors are two factors that are difficult to balance. The existing works improve gossip from only one factor, which cannot select suitable forwarding probability and avoid redundant messages. Besides, these schemes do not consider the historical packet reception of new neighbors when UAVs fly around, which decreases forwarding efficiency. To solve these problems, we first propose a data structure called Bitgraph that can record the historical packet reception of UAVs. Then, we formulate gossip broadcasting as a partially observable Markov decision process. Based on Bitgraph, we design the reward function. Finally, we design a multi-agent reinforcement learning algorithm, Branching Deep Graph Network (BDGN), which simultaneously makes decisions on forwarding probability and neighbors. Extensive experiments illustrate that our proposal gets more than 29% advantage in terms of the propagation delay and 20% advantage in terms of the redundant messages compared to the existing works.

The utilization of blockchain technology as a distributed information sharing system has gained widespread adoption across various domains. However, its application to Flying Ad-Hoc Network (FANET), characterized by severe packet loss, poses significant challenges. The high packet loss rates in FANETs can result in decreased consensus success rates and negatively impact information sharing consistency and efficiency. In this paper, we proposed RoUBC, a novel consensus scheme for Flying Ad-Hoc Networks (FANET), which is based on the Raft protocol and is designed to address the challenges posed by the severe packet loss network in FANET. The proposed scheme consists of two phases: leader election and block consensus. In the leader election phase, we integrate multi-criteria decision-making and link prediction algorithms to design an efficient stable-leader election method. In the block consensus phase, we propose a dynamic block verification algorithm based on historical verification information to achieve efficient block consensus. Our theoretical analysis demonstrates that the proposed consensus protocol is safe and live, effectively ensuring the consistency of message sharing in FANET. Experiment results show that our scheme outperforms traditional Raft schemes, with 35% increase in consensus success rate and 25% improvement in consensus efficiency.