Tactile sensors play a crucial role in enabling robots to interact effectively and safely with objects in everyday tasks. In particular, visuotactile sensors have seen increasing usage in two and three-fingered grippers due to their high-quality feedback. However, a significant gap remains in the development of sensors suitable for humanoid robots, especially five-fingered dexterous hands. One reason is because of the challenges in designing and manufacturing sensors that are compact in size. In this paper, we propose HumanFT, a multimodal visuotactile sensor that replicates the shape and functionality of a human fingertip. To bridge the gap between human and robotic tactile sensing, our sensor features real-time force measurements, high-frequency vibration detection, and overtemperature alerts. To achieve this, we developed a suite of fabrication techniques for a new type of elastomer optimized for force propagation and temperature sensing. Besides, our sensor integrates circuits capable of sensing pressure and vibration. These capabilities have been validated through experiments. The proposed design is simple and cost-effective to fabricate. We believe HumanFT can enhance humanoid robots' perception by capturing and interpreting multimodal tactile information.

This study presents a mobile edge computing (MEC)‐enabled UAV communication system, where a number of UAVs are served by terrestrial base stations (TBSs) equipped with computation resource in the non‐orthogonal multiple access manner. Each UAV has to offload its computing tasks to the proper TBS due to the limited energy supply. For this, the authors aim at minimising the sum of transmission energy of UAVs and computation energy of TBSs through jointly optimising the UAV transmit power, computation resource allocation, and UAV grouping. Considering the non‐convexity of this optimisation problem, they obtain the optimal solution in the coupled steps: the convex resource allocation optimisation and the combinatorial UAV grouping optimisation. By exploiting the convex nature of the resource allocation optimisation problem, they obtain the optimal transmit power and computation allocation based on the KKT conditions and the idea of gradient descent method when considering a single TBS. Then, they adopt the simulated annealing to obtain the optimal UAV grouping and TBS selection based on the proposed resource allocation optimisation algorithm. Finally, simulation results show that the proposed joint optimisation of transmit power, computation resource allocation, and UAV grouping can effectively reduce the energy consumption of MEC‐aware UAV communication system.

In recent years, the 3rd generation partnership project (3GPP) has approved the narrowband Internet of Things (NB-IoT) system to support the low-data-rate machine- type communications. With the rapid development of NB- IoT technology, the NB-IoT traffic volumes have been experiencing the unprecedented growth. To this end, non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) have been proposed as promising technologies for the NB-IoT system. In this paper, our goal is to minimize the total energy consumption subject to the computation capacity and execution latency limits by jointly optimizing the transmit power, computation resource allocation, and successive interference cancellation (SIC) ordering. Considering the NP-hardness of the joint optimization problem, we obtain the optimal solution in the coupled steps: the resource allocation optimization and the combinatorial SIC ordering optimization. By exploiting the convex nature of the resource allocation optimization problem, we obtain the optimal transmit power and computation resource allocation based on the KKT conditions and the idea of gradient descent method when fixing the SIC ordering. Considering the combinatorial optimization of SIC ordering, we further propose a tabu search based SIC ordering algorithm on the basis of the proposed resource allocation optimization algorithm. Finally, simulation results demonstrate that the proposed joint optimization of transmit power, computation resource allocation, and SIC ordering in the context of NOMA can effectively reduce the total energy consumption of MEC- aware NB-IoT system, in comparison with the frequency- division multiple access technique.