As robots integrate into human society, ensuring safe robot-environment interaction, particularly in the event of collisions, has emerged as a growing design priority. A promising solution is introducing proper compliance into existing rigid robots, akin to musculoskeletal systems, to absorb impacts in various directions. However, mimicking longitudinal compliance seen in biological joints to absorb compressive impacts along limbs, such as the role of cartilages like menisci, remains a challenge shadowed by the complexity of joint architecture. Here, exploring and adapting the elastic longitudinal movement structure of knee, we incorporated traditional mechanical hinges with a compact buffer structure to enable both simple rotational motion and effective longitudinal impact absorption. Under longitudinal loading, the buffer structure functions as a mechanism transmitting the limited compression to amplified deformations of elastic elements, thus producing resistance against load. The resistive load-displacement relationship is tailorable by tuning diverse elastic components, allowing for a high-static-low-dynamic stiffness to improve energy absorption efficiency. Drop tests and walking robot demonstrations confirm that the proposed knee-inspired hinge not only mitigates acceleration transmitted to the robot's main body but also reduces ground reaction forces, thereby improving robot-environment interaction safety. This work highlights the design paradigm of adapting natural solutions to mechanical systems, and holds potential for direct integration into diverse robots, exoskeletons, and prostheses.

During robotic locomotion, shock forces from ground impact propagate through the leg and may cause fatigue or damage to joints and sensitive hardware. To attenuate impacts in diverse aspects and directions, multi-approaches, including active control strategies and passive compliant joints, are essential for providing a comprehensive solution. Here, inspired by human knees, a meniscus-like structure was developed for a compliant anthropomorphic joint to provide a complementary way of shock absorption, especially along the axial direction. The proposed meniscus-like structure comprises a pair of curved arms wrapped with preloaded elastic bands whose elongations produce restoring forces against the axial load. This structure simultaneously realized impact attenuation in three aspects: decreasing contact stress by designing consistently conformal contact interfaces under axial movement; reducing peak impact forces by tuning load-displacement curves to obtain a high-static-low-dynamic nonlinear stiffness; and dissipating energy by hysteresis due to sliding frictions. The effectiveness in attenuating impacts on robotic legs was further verified by both analytical analyses and impact experiments that it outperforms regular elastic buffers at multiple leg configurations. Inserting meniscus-like structures into anthropomorphic joints efficiently utilized the joint space to attenuate axial impacts, complementing the system of interaction safety for the robot community.

The mechanical work performed by the individual legs affects the metabolic cost of locomotion. The effects of an elastically suspended backpack (ESB) on the mechanical work performed by the individual legs have not yet been quantified. This article explores the impact of variables, such as the stiffness and damper of an ESB, walking speed, and load mass, on the leg mechanical work (LMW). A model integrating an improved bipedal walking submodel and a spring−mass−damper submodel is proposed to estimate the mechanical work performed by the individual legs (LMW model). Experimental data were collected to estimate the accuracy of the proposed model. Seven subjects walked with a loaded ESB prototype at speeds ranging from 3.6 to 6.0 km/h with the suspension engaged and with the suspension locked out. The measured mechanical work performed by the individual legs was compared to the LMW model estimates. The proposed model estimates corresponded well with the empirical results (averageR2= 0.909; estimated average error 3.6%). The LMW model was then used to simulate the effects of variables. The ESB produces positive or negative effects under different variables. With increasing ESB stiffness, the ESB first produces positive effects, then negative effects, and finally approaches the rigid backpack effect. The ESB damper also affects the magnitude of the effect. The smaller the damping, the larger the effect. These results could assist engineers trying to design ESB to minimize the mechanical work performed by the legs which may also minimize the metabolic energy cost.

Cross-modal communication is playing an increasingly important role in improving receivers' immersive experience. The main challenge lies in ensuring the heterogeneous requirements of the cross-modal stream. Especially, the discontinuity of the received haptic signals caused by the delay should be eliminated. Unfortunately, existing schemes study the cross-modal stream separately, which leads to that the haptic signal is distorted, or the audio-visual quality is reduced. To solve this problem fundamentally, we propose a joint transmission framework combining prediction and device-to-device (D2D) communication by taking advantage of the correlation of the haptic signals and the proximity feature of the receivers. Specifically, on the theoretical end, to completely eliminate the discontinuity, we propose a prediction mechanism by predicting and sending the future signals in advance. To compensate for the reliability loss brought by prediction, D2D links are efficiently established on the receivers' side. On the technical end, we first design a minimum resource (e.g., power) consumption search algorithm based on the binary search to obtain the optimal prediction horizon. Moreover, we develop a simple but efficient transmission mode selection algorithm based on the Hungarian algorithm. Experimental results demonstrate the advantages of our proposed scheme in saving the power consumption.

In order to make device-to-device (D2D) content sharing give full play to its advantage of improving local area services, one of the important issues is to decide the channels that D2D pairs occupy. Most existing works study this issue in static environment, and ignore the guidance for D2D pairs to select the channel adaptively. In this paper, we investigate this issue in dynamic environment where D2D pairs’ activeness and wireless channel are dynamic. Specifically, we propose a pricing-based approach to guide D2D pairs to select different channels according to the spectrum resource states adaptively. Then, we formulate the pricing-based channel selection problem as an expected global price-to-performance ratio minimum problem. In order to solve it in a tractable manner, we make an approximately equivalent transformation to it. After that, we model the transformed problem as a stochastic game and prove it to be an exact potential game, which has at least one pure strategy Nash Equilibrium (NE) point. In order to reach the pure strategy NE points in dynamic environment, we design a channel selection learning algorithm based on stochastic learning automata, which only requires little information exchange. Simulation results show that our proposed algorithm outperforms other benchmark algorithms.

Content recommendation based on device-to-device (D2D) multicast communications is expected to become a promising approach to improve local area services. Importantly, two main challenges should be considered: i) user clustering-in order to be tailored to recommend contents, the members in the same cluster should have great similarity in multiple characteristics, and ii) link allocation-we should use as little resource consumption and information exchange as possible while keeping the recommendation accuracy. In this paper, we firstly quantify the degree of the similarity between two target users with regard to multiple characteristics. Guided by such similarity, we define a clustering validity index in terms of between-within proportion (BWP) to characterize the clustering performance. Then, the issue of user clustering is modeled as a sum BWP maximum problem, and a user clustering algorithm based on modified K-means algorithm is designed to solve it in a fast-operating and low-complexity way. After user clustering, we model the issue of link allocation as a weighted aggregate interference minimization problem, and then transform it to an exact potential game. As such, a link allocation algorithm based on stochastic learning algorithm is proposed which helps to obtain the result of this game in a distributed way without complete information. Also, we analyze its convergence and optimality performance. Simulation results demonstrate the effectiveness of our proposed algorithms.

In this paper, we investigate secure unmanned aerial vehicle (UAV) communication in the presence of multiple UAV adaptive eavesdroppers (AEs), where each AE can conduct eavesdropping or jamming adaptively by learning others’ actions for degrading the secrecy rate more seriously. The one-leader and multi-follower Stackelberg game is adopted to analyze the mutual interference among multiple AEs, and the optimal transmit powers are proven to exist under the existing conditions. Following that, a mixed-strategy Stackelberg Equilibrium based on finite and discretized power set is also derived and a hierarchical Q-learning based power allocation algorithm (HQLA) is proposed to obtain the optimal power allocation strategy of the transmitter. Numerical results show that secrecy performance can be degraded severely by multiple AEs and verify the availability of the optimal power allocation strategy. Finally, the effect of the eavesdropping cost on the AE’s attack mode strategies is also revealed.

Device-to-device (D2D) multicast communications have become a promising technology to satisfy the increasing demands for popular content sharing among users. However, most of the existing works mainly focus on the benefits resulting from constituting the clusters, while ignoring the ever-present costs. Facing the coexistence of the benefits and costs, the two issues should be tackled, i.e., i) how to motivate the users to form clusters, and ii) how to achieve a balance between them. In our work, a social-monetary-aware incentive model based on the social ties and the content fee is proposed to motivate both the cluster head (CH) and content requesters (CRs) to form clusters. Then, we formulate the cluster formation problem as an incentive-based coalition formation game with nontransferable utility, where the utility takes into account the benefits in terms of the content fee and the costs in terms of the transmission delay. An incentive-based cluster formation algorithm based on the merge-and-split rules and the Pareto order is proposed. Its performance, e.g., the convergence, the stability, is discussed, and is also evaluated by extensive simulations based on the comparisons with other algorithm.

Device-to-Device (D2D) multicast communication is becoming a promising technology to improve local area service. In this paper, we present a distributed social-aware clustering method to group the content requesters (CRs) into clusters. Specifically, a cluster head (CH) selection algorithm based on social maximum weight (SMW) is firstly proposed to select the CHs from the original CRs. After that, we propose a cluster formation optimization framework via a non-transferable utility coalition formation game (CFG), and a distributed coalition formation algorithm for clustering is proposed based on preference relationship and switch operations. Moreover, the final coalition structure is proved to be Nash-stable. Numerical results are presented to verify the effectiveness of our proposed schemes.

Joint link and power allocation for device‐to‐device (D2D) communication underlaying cellular networks are necessary to coordinate the mutual interference. Most of the works consider the sum interference from D2D pairs. However, mobile devices are carried by human beings who are connected with social ties. For one cellular user, the strengths of the social ties with D2D pairs are different. Thus, his interference tolerance temperatures (ITTs) are various. In this study, we distinguish D2D pairs through the social ties, and propose a social aware pricing‐based joint link and power allocation scheme. Specifically, the base station (BS) prices the interference on every link and updates the prices according to the ITTs of cellular users. Subsequently, with the prices, the competition of D2D pairs for the links is modelled as a non‐cooperative game. The authors prove the existence and the uniqueness of the Nash equilibrium (NE), and demonstrate that the NE is Pareto optimal. Moreover, an iterative decentralised algorithm is designed to solve the game. Especially, if the number of the price on one link for one D2D pair being updated is up to an upper bound, the power is forced to be 0. Numerical simulations verify the effectiveness of the authors' proposed scheme.