The growing diversity and number of industrial robots make energy consumption prediction and optimization increasingly essential. Current data-driven approaches, particularly those based on multi-layer perception (MLP), have shown feasibility but typically overlook the variability or unknown nature of load-related parameters in real-world applications. This paper presents a KAN-LSTM model designed to accurately predict energy consumption under unknown load conditions, alongside a particle swarm optimization (PSO) algorithm for minimizing energy use. First, an industrial robot dynamics and energy consumption model is established. Then, the KAN-LSTM model is trained on datasets from the AUBO-E5 robot, with its predictions compared to alternative network models. Finally, PSO is applied to optimize energy consumption. Experimental results indicate that the KAN-LSTM model achieves high prediction accuracy (95.7–97.1%) and offers substantial energy optimization potential (53.1–64.7%). Optimized industrial robots are particularly suitable for tasks such as picking and palletizing in the courier industry, saving operational costs and increasing the sustainability of automated systems in logistics environments.

Given the continual rise in mission diversity and environmental complexity, the adept integration of a robot’s aerial and terrestrial locomotion modes to address diverse application scenarios has evolved into a formidable challenge. In this paper, we design a reconfigurable airframe robot endowed with the dual functionalities of rolling and flying. This innovative design not only ensures a lightweight structure but also incorporates morphing capabilities facilitated by a slider-crank mechanism. Subsequently, a land-to-air transformation strategy for the robot is introduced, achieved through the coordinated movement of the robotic arm and the servo motor. To ensure stable control of the robot amid external wind disturbances, we leverage the collaboration between a Generative Adversarial Network (GAN)and a Nonlinear Model Predictive Control (NMPC) controller. After the wind force magnitude is predicted through the neural network, the robot’s adeptness in flexible trajectory tracking is verified. Under simulated wind conditions of 12.1 m/s, the trajectory error consistently remains within the range of 10–15 cm, affirming the effectiveness of this control method.

With the rapid expansion of drones in last‐mile delivery, high energy consumption has become a critical challenge, affecting both operational efficiency and sustainability. Among the various factors influencing unmanned aerial vehicle (UAV) energy consumption, payload weight plays a significant role in determining power usage. However, most existing studies assume known payload conditions, while in real‐world applications, payload is often unknown, limiting the accuracy of energy consumption prediction and optimization. To address this issue, this study proposes a long short‐term memory (LSTM)‐Kolmogorov–Arnold network (KAN) energy consumption modeling approach, integrating deep learning with prior knowledge of quadrotor dynamics to enhance UAV energy prediction under unknown load conditions. The proposed framework consists of a load prediction model based on an improved LSTM network and an energy consumption model employing the KAN for power estimation, effectively capturing the impact of payload variations on energy consumption. Additionally, a particle swarm optimization (PSO) algorithm is applied to dynamically adjust flight parameters, further improving energy efficiency. Experimental validation on a customized quadrotor platform demonstrates that the proposed model achieves R ² values of 0.9852 and 0.9235 for load and power prediction, respectively, indicating high predictive accuracy. When combined with PSO, the approach enhances energy efficiency by 23.14%–28.21% under various payload conditions. This study provides an effective solution for UAV energy consumption prediction and optimization, contributing to improved operational efficiency and the sustainability of drone‐based logistics.