Multi-axle Swerve-drive Autonomous Mobile Robots (MS-AMRs) equipped with independently steerable wheels are commonly used for high-payload transportation. In this work, we present a novel Model Predictive Control (MPC) method for MS-AGV trajectory tracking that takes tire wear minimization consideration in the objective function. To speed up the problem-solving process, we propose a hierarchical controller design and simplify the dynamic model by integrating the magic formula tire model and simplified tire wear model. In the experiment, the proposed method can be solved by simulated annealing in real-time on a normal personal computer and by incorporating tire wear into the objective function, tire wear is reduced by 19.19% while maintaining the tracking accuracy in curve-tracking experiments. In the more challenging scene: the desired trajectory is offset by 60 degrees from the vehicle’s heading, the reduction in tire wear increased to 65.20% compared to the kinematic model without considering the tire wear optimization.

In this work, we present a data-driven solution for the attitude control of DoubleBee on slopes. DoubleBee is a novel hybrid aerial-ground robot with two rotors and two active wheels. Inspired by the physics modeling of the system, we add a channel-separated attention head to a deep ReLU neural network to predict disturbances from ground effects, motor torques and rotation axis shift. The proposed neural network is Lipschitz continuous, has fewer parameters and performs better for disturbance estimation than the baseline deep ReLU neural network. Then, we design a sliding mode controller using these predictions and establish its input-to-state stability and error bounds. Experiments show improvements of the proposed neural network in training speed and robustness over a baseline ReLU network, and a 40% reduction in tracking error compared to a baseline PID controller.

In response to the evolving challenges posed by small unmanned aerial vehicles (UAVs), which have the potential to transport harmful payloads or cause significant damage, we present AV-FDTI, an innovative Audio-Visual Fusion system designed for Drone Threat Identification. AV-FDTI leverages the fusion of audio and omnidirectional camera feature inputs, providing a comprehensive solution to enhance the precision and resilience of drone classification and 3D localization. Specifically, AV-FDTI employs a CRNN network to capture vital temporal dynamics within the audio domain and utilizes a pretrained ResNet50 model for image feature extraction. Furthermore, we adopt a visual information entropy and cross-attention-based mechanism to enhance the fusion of visual and audio data. Notably, our system is trained based on automated Leica tracking annotations, offering accurate ground truth data with millimeter-level accuracy. Comprehensive comparative evaluations demonstrate the superiority of our solution over the existing systems. In our commitment to advancing this field, we will release this work as open-source code and wearable AV-FDTI design, contributing valuable resources to the research community.