Soft origami continuum robots have attracted considerable attention because of their flexibility and adaptability, but they face challenges in control accuracy. This study presents a novel design and control scheme for large-deformation hybrid-actuated soft origami continuum robots to improve their motion performance in real tasks. An origami-based pneumatic chamber is used as the robot backbone to achieve a high extension ratio, and the hybrid variable stiffness principle combining antagonistic actuation and layer jamming further enhances the robot's overall structural stiffness. The robot performs precise movements by controlling tendons distributed in an external origami. An iterative training strategy based on long short-term memory is employed to model the inverse kinematics of the robot in consideration of the inherent hysteresis of origami robots. Step size features are introduced to improve model accuracy with limited data. The capability for variable stiffness enables the migration of the training model on the basis of a single segment, the accuracy of which is close to the submillimeter level. Decoupling control of each segment for a rear-driven multi-segment origami continuum robot is also achieved. Experiment results reveal that the trajectory tracking errors for single-segment and multi-segment of the robot are 1.58, and 2.81 mm, respectively, with relative errors of 0.71% and 0.63% over the robot length, demonstrating the good performance of the proposed design and control method. Orientation data can also be added to the dataset to achieve orientation control of the robot, with an angle error of 0.75°. The robot shows its stability and safety in a series of real tasks, including writing, LED tracking, board cleaning, and pick and place. This study demonstrates a potential solution for continuum robots performing precise tasks without any sensory feedback in confined spaces and specialized environments where electronic sensors fail.

The advantage assumed by conventional distributed edge computing in handling large-scale tasks is often overshadowed by straggling edge nodes (ENs). This in turn catalyzes the recent emergence of coded edge computing that can effectively mitigate straggling via subtle task encoding. Nonetheless, coded edge computing presents new challenges in communications. In particular, existing offloading schemes are mainly designed for conventional distributed edge computing, where the data offloaded to different ENs are often non-overlapping. This makes them not well-suited to coded edge computing, where substantial redundancy exists among the data offloaded to different ENs due to task encoding. To the best of our knowledge, a tailor-designed efficient offloading scheme for coded edge computing still remains underexplored. With this consideration, a novel coding-aware rate splitting scheme is proposed in this work, which splits the data offloaded to different ENs in a coding-aware manner to avoid transmission redundancy and enables multiple concurrent multi-casts to the ENs. In addition, based on the concave-convex procedure and the sequential parametric optimization framework, two optimization algorithms are developed to minimize the overall latency and the energy consumption under the proposed scheme, respectively. Simulations are conducted to corroborate the effectiveness of the proposed scheme.

The ever-increasing scale and more stringent latency requirements of mobile computing tasks have driven the recent development of distributed edge computing. In distributed edge computing, a large-scale computing task is partitioned into multiple small subtasks and executed in parallel on multiple edge nodes (ENs) to reduce computation delay. In early works of this area, the computation results of the subtasks are often transmitted back in a non-cooperative manner, which may lead to suboptimal downlink communication delay. Replicated edge computing can alleviate this issue by replicating the computing task over multiple ENs to enable cooperative transmission in the downlink. However, this will inevitably entail multi-fold increase of computation costs. To bridge the gap between the conventional distributed edge computing and the replicated edge computing, a novel partial replication based distributed edge computing scheme is proposed in this work. In particular, by judiciously determining the portion of task to be replicated at the ENs, the proposed scheme can harvest cooperative transmission gains while avoiding excessive computational replication costs. Accordingly, a partial replication based delay minimization problem is formulated. By leveraging the generic alternating optimization framework, this problem can be divided into two subproblems of power allocation and task partitioning. Through analysis, a semi-closed form solution is derived for the former non-convex subproblem, while the latter subproblem turns out to be linear. Simulation results are presented to corroborate the effectiveness of the proposed scheme.

Distributed edge computing has been advocated as a key enabling technology to tackle large-scale intelligence applications, which is however hampered by the straggling effect. To overcome straggling, coded edge computing emerges as a promising solution by creating judiciously designed redundant computations using coding theory. Nonetheless, existing transmission schemes for coded edge computing that make edge nodes (ENs) transmit independently are often sub-optimal, as the computation results are correlated due to coding redundancy. This entails a pressing need for more effective transmission for coded edge computing. With this consideration, a noveltask-decoding assisted cooperative transmissionscheme is proposed in this work to facilitate cooperative transmission in general coded edge computing settings. Specifically, by exploiting the structural relation among the encoded sub-tasks, a task-decoding mechanism is developed to enable ENs to reconstruct computation results ofallother ENs, so that they can cooperatively transmit withanyother EN by forming a virtual multi-antenna system. To characterize the delay performance of the proposed scheme, an analytic bound with closed-form expression is derived first, followed by a more accurate algorithmic bound for scenarios with a relatively small recovery threshold. Simulations are conducted to validate the effectiveness of the proposed scheme.

The rapid growth in scale and complexity of mobile applications fosters the development of the coded edge computing paradigm. By exploiting the redundancy in the encoded subtasks, coded edge computing enables collaborative transmission of multiple edge nodes and is promising for distributed computing in wireless fading environments. Nonetheless, to the best of our knowledge, due to challenges arising from the selection of the coding parameters, offloading strategy design for coded edge computing in general fading environments still remains open. With this consideration, the coded offloading problem is studied in this work and a delay-optimal coded offloading scheme is proposed. In particular, when the offloaded tasks are encoded by$(k,r)$linear codes, transmission diversity gains can be obtained by performing edge node selection to mitigate fading. However, the corresponding optimization problem turns out to be a highly non-trivial non-linear mixed-integer programming. To this end, through in-depth analysis based on order statistics, it is found that the average processing delay of the offloaded tasks admits a favorable$V$-structure with respect to the coding parameter$r$, under arbitrary fading distribution. This key theoretic result allows us to efficiently solve the original problem using monotonic optimization. Simulations are conducted to validate our analysis and corroborate the effectiveness of the proposed scheme.