Radio-Frequency Identification (RFID) positioning promises a prospective future for industrial automation and Industrial Internet of Things (IIoT) applications. However, the radio waves carry multiple parameters including position, orientation, and ambient environment factors, which raises challenges in simultaneous detection of position and orientation of product objects. This investigation proposes a simple RFID array-based position and orientation simultaneous detection technique for moving object in industrial chain. The main contributions of this investigation include: (1) Theoretical analysis and integrated model of position and orientation variation with the antenna parameters and interrogation variables in RF backscatter coupling-based sensing. (2) Development of an innovative simple RFID array-based phase separation technique with differential sensing, which determines the position-and orientation-induced phase without their mutual coupling impact. (3) Proposal of a simultaneous detection technique for moving objects’ position and orientation by integrating the Multiple Signal Classification (MUSIC) algorithm and hyperbolic positioning algorithm. In the experimental verification with a range from -75 cm to 75cm, the average error of position and orientation estimation is 4.29 cm and 4.89 degrees.

This paper investigates master-auxiliary unmanned aerial vehicles (UAVs)-enabled wireless-powered Internet-of-Things (WPIoT) networks, which overcome the inflexibility and site selection issues caused by traditional fixed-point intelligent reflecting surface (IRS). Specifically, multiple rechargeable Master UAVs (MUAVs) and IRS-integrated Auxiliary UAVs (AUAVs) are applied in pairs to cooperatively charge and collect data from IoT devices clustered in different subareas under cloud scheduling. Given the constraints of limited onboard battery capacity and complete data collection, we formulate a system energy minimization problem, which is then divided into three sub-problems. We first utilize a pair of U-nets with quantization layers trained by deep unsupervised learning (DUL) to output discrete downlink (DL) and uplink (UL) IRS phases separately. Gradient functions with specific features are also proposed to solve non-differentiable issue during training. Given the optimized IRS phase policies, off-policy deep reinforcement learning (DRL) is exploited to optimize intra-subarea data collection policy and inter-subarea multi-UAV scheduling scheme. Two assistive techniques, positive transition initialization (PTI) and action mask, are proposed to guide the learning of the agent and alleviate the burden of optimization. Numerical results indicate that our proposed approach combining DUL with DRL can improve performance by more than 90% compared to the pure DRL method. Furthermore, our proposed Master-Auxiliary-UAV collaborative data collection scheme (MACS) can achieve better energy performance than homogeneous MUAV data collection scheme (HMCS) in cases with low onboard battery capacity while halving task completion time.

This paper investigates heterogeneous unmanned aerial vehicles (UAVs)-enabled wireless-powered Internet-of-Things (WPIoT) networks, where a Master UAV (MUAV) and an intelligent reflecting surface (IRS)-integrated Auxiliary UAV (AUAV) cooperatively collect data from terrestrial IoT devices. Specifically, the MUAV leverages a successive hover-and-fly strat-egy for energy broadcasting and data collection, while the AUAV accompanies MUAV and assists in both wireless power transfer (WPT) and wireless information transmission (WIT) processes through adaptive adjustment of IRS. Given the stringent re-quirement of complete data collection, we divide the energy-efficiency maximization problem into two sub-problems: discrete phase control and joint trajectory design and time allocation. To address these issues, we propose combining deep unsupervised learning (DUL) and artificial replay buffer initialization (ARBI)-enhanced off-policy deep reinforcement learning (DRL). Numeri-cal results demonstrate that our proposed scheme achieves better performance in terms of energy-efficiency, task complete time and reward convergence.

This article investigates a master unmanned aerial vehicle (MUAV)-powered Internet of Things (IoT) network, in which we propose using a rechargeable auxiliary UAV (AUAV) equipped with an intelligent reflecting surface (IRS) to enhance the communication signals from the MUAV and also leverage the MUAV as a recharging power source. Under the proposed model, we investigate the optimal collaboration strategy of these energy-limited UAVs to maximize the accumulated throughput of the IoT network. Depending on whether there is charging between the two UAVs, two optimization problems are formulated. To solve them, two multi-agent deep reinforcement learning (DRL) approaches are proposed, which are centralized training multi-agent deep deterministic policy gradient (CT-MADDPG) and multi-agent deep deterministic policy option critic (MADDPOC). It is shown that the CT-MADDPG can greatly reduce the complexity of optimization, and the proposed MADDPOC is able to support low-level multi-agent cooperative learning in the continuous action domains, which has great advantages over the existing option-based hierarchical DRL that only supports single-agent learning and discrete actions.

In this paper, we propose a novel Intelligent Reflected Surface (IRS)-aided Master-Auxiliary-Unmanned Aerial Vehicle (UAV)-powered Internet of Things (IoT) Network (IRS-MAIN). Compared to the conventional terrestrial or aerial IRS-assisted communication networks, the IRS-MAIN not only benefits from wide communication range due to the mobility of UAVs, but also enjoys enhanced channel condition brought by the IRS. To be specific, let the Auxiliary UAV (AUAV) carry an IRS to enhance signals from the Master UAV (MUAV) served as a radio frequency (RF) transmitter. We focus on the problem to maximize the total throughput by jointly optimizing the trajectories and transmit power of the MUAV. A modified multi-agent deep reinforcement learning (MADRL) based algorithm, named as Pre-activation Penalty Multi-agent Deep Deterministic Policy Gradient (PP-MADDPG), is proposed to solve the formulated problem in an accurate and efficient way. Simulation results are provided to demonstrate that PP-MADDPG outperforms the baseline method in terms of the throughput as well as the convergence rate.

In this paper, we address efficiently and robustly collecting objects stored in different trays using a mobile manipulator. A resolution complete method, based on precomputed reachability database, is proposed to explore collision-free inverse kinematics (IK) solutions and then a resolution complete set of feasible base positions can be determined. This method approximates a set of representative IK solutions that are especially helpful when solving IK and checking collision are treated separately. For real world applications, we take into account the base positioning uncertainty and plan a sequence of base positions that reduce the number of necessary base movements for collecting the target objects, the base sequence is robust in that the mobile manipulator is able to complete the part-supply task even there is certain deviation from the planned base positions. Our experiments demonstrate both the efficiency compared to regular base sequence and the feasibility in real world applications.