Hydraulic legged mobile manipulators are favored because they provide high power density and a strong explosive force. Hydraulic mobile manipulators require contact force control to handle increasingly mobile interaction tasks. However, the highly nonlinear dynamics and model inaccuracy of multiple hydraulic joint systems increase the difficulty of force control. To overcome this problem, we propose an interaction force control strategy for a hydraulic legged mobile manipulator operating via a force-controlled floating base. The interaction task involving multiple subtasks, such as torso motion, contact force, operational end-free motion, and leg motion, is decomposed into motion and force-control subparts under positional constraints. These tasks are transformed into a quadratic programming optimization problem with multiple constraints, and the task priorities are adjusted by a weight matrix. The effectiveness of our control strategy is demonstrated through simulations and physical experiments. Under the proposed strategy, the hydraulic legged mobile manipulator maintains its advantage (performing large load operations) while controlling the appropriate contact force during interactive operations.

Quadruped robots equipped with manipulators need fast and precise grasping and detection algorithms for the transportation of disaster relief supplies. To address this, we developed a framework for these robots, comprising a Grasp Detection Controller (GDC), a Joint Trajectory Planner (JTP), a Leg Joint Controller (LJC), and a Manipulator Joint Controller (MJC). In the GDC, we proposed a lightweight grasp detection CNN based on DenseBlock called DES-LGCNN, which reduced algorithm complexity while maintaining accuracy by incorporating UP and DOWN modules with DenseBlock. For JTP, we optimized the model based on quadruped robot kinematics to enhance wrist camera visibility in dynamic environments. We integrated the network and model into our homemade robot control system and verified our framework through multiple experiments. First, we evaluated the accuracy of the grasp detection algorithm using the Cornell and Jacquard datasets. On the Jacquard dataset, we achieved a detection accuracy of 92.49% for grasp points within 6 ms. Second, we verified its visibility through simulation. Finally, we conducted dynamic scene experiments which consisted of a dynamic target scenario (DTS), a dynamic base scenario (DBS), and a dynamic target and base scenario (DTBS) using an SDU-150 physical robot. In all three scenarios, the object was successfully grasped. The results demonstrate the effectiveness of our framework in managing dynamic environments throughout task execution.

The quadruped robots have superior adaptability to complex terrains, compared with tracked and wheeled robots. Therefore, leader-following can help quadruped robots accomplish long-distance transportation tasks. However, long-term following has to face the change of day and night as well as the presence of interference. To solve this problem, we present a day/night leader-following method for quadruped robots toward robustness and fault-tolerant person following in complex environments. In this approach, we construct an Adaptive Federated Filter algorithm framework, which fuses the visual leader-following method and the LiDAR detection algorithm based on reflective intensity. Moreover, the framework uses the Kalman filter and adaptively adjusts the information sharing factor according to the light condition. In particular, the framework uses fault detection and multisensors information to stably achieve day/night leader-following. The approach is experimentally verified on the quadruped robot SDU-150 (Shandong University, Shandong, China). Extensive experiments reveal that robots can identify leaders stably and effectively indoors and outdoors with illumination variations and unknown interference day and night.

Precisely forecasting the lifetime of lithium-ion batteries is crucial for addressing consumer worries regarding their safety and dependability. However, existing research predominantly focuses on individual degradation characteristics of batteries, neglecting their multiple degradation features or their interactions. To address these issues, this paper proposes a time series method based on Transformer with a parallel self-attention mechanism to forecast the remaining useful life of lithium-ion battery. First, the method processes lithium-ion battery data through a sampling layer and integrates a time step variable block, where the latter combines a time step encoding layer and a variable encoding layer to capture degradation information from both the time and feature dimensions. The time step encoding layer learns long-term dependencies through the self-attention mechanism, while the variable encoding layer focuses on the local degradation features from different sensors. The time step encoding layer and the variable encoding layer operate in parallel to extract both temporal data and sensor degradation features. Then, these two layers focus on the different aspects within the feature vector, capturing the correlations between these features through a multi-head self-attention mechanism, and determining the relative significance of each feature in forecasting the current time step. These correlations and the weighted features are fused into a new feature vector. Finally, the new feature vector is passed into the decoder to compute the prediction result. Experimental results on two classical lithium battery datasets show that our approach surpasses the existing methods in predicting battery remaining useful life.