Pre-trained models trained with internet-scale data have achieved significant improvements in perception, interaction, and reasoning. Using them as the basis of embodied grasping methods has greatly promoted the development of robotics applications. In this paper, we provide a comprehensive review of the latest developments in this field. First, we summarize the embodied foundations, including cutting-edge embodied robots, simulation platforms, publicly available datasets, and data acquisition methods, to fully understand the research focus. Then, the embodied algorithms are introduced, starting from pre-trained models, with three main research goals: (1) embodied perception, using data captured by visual sensors to perform point cloud extraction or 3D reconstruction, combined with pre-trained models, to understand the target object and external environment and directly predict the execution of actions; (2) embodied strategy: In imitation learning, the pre-trained model is used to enhance data or as a feature extractor to enhance the generalization ability of the model. In reinforcement learning, the pre-trained model is used to obtain the optimal reward function, which improves the learning efficiency and ability of reinforcement learning; (3) embodied agent: The pre-trained model adopts hierarchical or holistic execution to achieve end-to-end robot control. Finally, the challenges of the current research are summarized, and a perspective on feasible technical routes is provided.

Plant protection UAVs are becoming the preferred plant protection method for agricultural pest control. At present, the evaluation of droplet distribution in aerial spraying is collected and evaluated after the completion of prevention and control operations, and there is a lack of real-time evaluation methods. Based on the flight parameter during the UAV plant protection process, real-time estimation of droplet distribution is the key to solving this problem and further improving the effectiveness of aerial spraying. This study proposes a merging algorithm for arbitrary polygonal regions, meshing the boundaries of the region, divide the mesh segments based on the overlapping meshes between the two regions, and connect the valid mesh connection segments of the two regions according to certain rules to obtain the intersection, union, and residual operation results between the regions. Afterwards, software based on this algorithm was developed and applied to generate spraying coverage regions, leakage spray regions, and repeated spray regions. The experimental results on theoretical and irregular routes show that the algorithm can accurately generate droplet distribution regions. The error of the calculation results with a mesh scale of 0.05 m is within 7‰, and the operating speed is above 30 Hz, meeting the real-time requirements. The smaller the mesh scale is, the higher the accuracy of the calculation results is, but the slower the calculation speed. Therefore, in practical applications, it is necessary to choose an appropriate mesh scale based on hardware computing power and accuracy level requirements. This study solves the problem of cumulative calculation of droplet distribution during the operation of plant protection UAVs, providing a basis for objectively evaluating the operation quality of plant protection UAVs and optimizing the setting of operation parameters.

An unmanned aerial manipulator (UAM) is a novel flying robot consisting of an unmanned aerial vehicle (UAV) and a multi-degree-of-freedom (DoF) robotic arm. It can actively interact with the environment to conduct dangerous or inaccessible tasks for humans. Owing to the underactuated characteristics of UAVs and the coupling generated by the rigid connection with the manipulator, robustness and a high-precision controller are critical for UAMs. In this paper, we propose a nonsingular global fast terminal sliding mode (NGFTSM) controller for UAMs to track the expected trajectory under the influence of disturbances based on a reasonably simplified UAM system dynamics model. To achieve active anti-disturbance and high tracking accuracy in a UAM system, we incorporate an RBF neural network into the controller to estimate lumped disturbances, including internal coupling and external disturbances. The controller and neural network are derived according to Lyapunov theory to ensure the system’s stability. In addition, we propose a set of illustrative metrics to evaluate the performance of the designed controller and compare it with other controllers by simulations. The results show that the proposed controller can effectively enhance the robustness and accuracy of a UAM system with satisfactory convergence. The experimental results also verify the effectiveness of the proposed controller.

Energy limitation is one of the intrinsic shortcomings of wireless sensor networks (WSNs), although it has been widely applied in disaster response, battlefield surveillance, wildfire monitoring, radioactivity detection, etc. Due to the large amount of energy consumed for data transmission, how to prolong the network lifespan by designing various hierarchical routing protocols has attracted more and more attention. As a result, numerous achievements have emerged successively. However, these presented mechanisms can rarely guarantee the satisfactory quality of service (QoS), while lowering the energy cost level of WSNs. Meanwhile, invulnerability is undoubtedly an excellent quantitative index to assess QoS. Therefore, it is critical to develop a practical routing method to optimize network lifetime by considering both invulnerability and energy efficiency. Game theory is suitable for such a critical problem as it can be used in node or at network level to encourage the decision-making capabilities of WSNs. In this paper, a novel invulnerability-aware clustering routing algorithm (IACRA) using game-theoretic method is proposed to solve the predicament. The core features of the addressed game-theory-based routing protocol include integral invulnerability awareness, optimal cluster head selection in hierarchical routing, distance-aware cluster head discovery, and cluster rotation update mechanism for lifetime optimization. Particularly, the integral network invulnerability based on weighted fusion is constructed for further defining the profit model by combining the invulnerability indicators used to evaluate the local and whole network. Meanwhile, the optimal probability function of every node elected as CH in per cluster is established through the game between invulnerability and node energy consumption. In addition, the cluster update mechanism base on cluster rotation is proposed to avoid the rapid death of nodes with large energy consumption for maximizing network lifetime. The experimental results indicated a significant improvement in energy balance as well as in invulnerability compared with the other three kinds of well-known clustering routing protocols including GEEC (Game-theory-based energy efficient clustering routing protocol), HGTD (Hybrid, game-theory-based distributed clustering protocol), and EEGC (Efficient energy-aware and game-theory-based clustering protocol). Concretely, at the 400 communication rounds, the invulnerability of IACRA was higher than that of GEEC, HGTD, and EEGC by 77.56%, 29.45% and 15.90%, respectively, and the average residual energy of IACRA was 8.61%, 18.35% and 6.36% larger than that of GEEC, HGTD, and EEGC, respectively. Based on these results, the proposed protocol can be utilized to increase the capability of WSNs against deterioration of QoS and energy constraints.

Contact force control for Unmanned Aerial Manipulators (UAMs) is a challenging issue today. This paper designs a new method to stabilize the UAM system during the formation of contact force with the target. Firstly, the dynamic model of the contact process between the UAM and the target is derived. Then, a non-singular global fast terminal sliding mode controller (NGFTSMC) is proposed to guarantee that the contact process is completed within a finite time. Moreover, to compensate for system uncertainties and external disturbances, the equivalent part of the controller is estimated by an adaptive radial basis function neural network (RBFNN). Finally, the Lyapunov theory is applied to validate the global stability of the closed-loop system and derive the adaptive law for the neural network weight matrix online updating. Simulation and experimental results demonstrate that the proposed method can stably form a continuous contact force and reduce the chattering with good robustness.