A novel legged robot with 6 limbs driven by 20 proprioceptive motors, named SDUHex, is proposed in this letter. The limbs located at the middle of the robot can work as manipulators or locomotors. The topology design of the multi-mode robot is introduced and the kinematics and dynamics of the robot leg is analysed. A hierarchical optimized whole body controller with virtual model active compliance, called VM-WBC, is proposed. This control method unified the foot locomotion and arm operation. The terrain adaptation strategy is proposed to improve the non-flat terrain passability. A series of locomotion and operation experiments are carried out on SDUHex to test the designed robot and control method. Robust locomotion of the robot is verified by lateral impact recovery and slope terrain adaptation experiments. Arm operation of the robot including single-arm pushing and dual-arm picking are experimented to highlight the novel multiplexing function of the robot limbs.

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.

The accurate perception of external environment information through the robot foot is crucial for the mobile robot to evaluate its ability to traverse terrain. Adequate foot-end contact signals can provide robust support for robot motion control and decision-making processes. The shape and uncertain rotation of the wheel-legged robot foot end represent a significant challenge to sensing the robot foot-end contact state, which current foot-end sensing schemes cannot solve. This paper presents a sensing method for the tire stress field of wheel-legged robots. A finite element analysis was conducted to study the deformation characteristics of the foot-end tire under force. Based on this analysis, a heuristic contact position estimator was designed that utilizes symmetrical deformation characteristics. Strain sensors, arranged in an array, extract the deformation information on the inner surface of the tire at a frequency of 200 Hz. The contact position estimator reduces the dimensionality of the data and fits the eigenvalues to the estimated contact position. Using support vector regression, the force estimator utilizes the estimated contact position and sensor signal to estimate the normal reaction force, designated as F_Z. The sensing system is capable of detecting the contact position on the wheel circumference (with a root mean square error of 1.150°), as well as the normal force of 160 N on the Z axis (with a root mean square error of 6.04%). To validate the efficacy of the sensor detection method, a series of randomized and repeated experiments were conducted on a self-constructed test platform. This novel approach offers a promising avenue for perceiving contact states in wheel-legged robots.

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.