Model-based force control for motion and force tracking faces significant challenges on real quadruped platforms due to the apparent model inaccuracies. In this paper, we present a multi-objective optimal torque control for hydraulic quadruped robots under significant model errors, such as non-modelable hydraulic components, linearization, disturbances, etc. More specifically, the centroidal dynamics are first modeled to project the dynamics of the floating-based whole-body behaviors to the centroidal frame. Model error compensation mechanisms are subsequently developed to track the reference motion of the CoM, torso, and foot-end trajectories, which are mapped into the joint space. Furthermore, a multi-objective optimal torque control scheme is formulated using quadratic programming (QP) to coordinate follow the reference motion and ground reaction forces simultaneously while satisfying all constraints. Finally, we present a series of simulations as well as experiments on a real hydraulic quadruped platform, EHbot. The results demonstrate that the proposed torque control scheme is robust to large model inaccuracies and improves the performance of the overall system.

Legged robots have shown great adaptability to various environments. However, conventional walking gaits are insufficient to meet the motion requirements of robots. Therefore, achieving high-speed running for legged robots has become a significant research topic. In this paper, based on the Spring-Loaded Inverted Pendulum (SLIP) model and the optimized Double leg—Spring-Loaded Inverted Pendulum (D-SLIP) model, the running control strategies for the double flying phase Bound gait and the Rotatory gallop gait of quadruped robots are designed. First, the dynamics of the double flying phase Bound gait and Rotatory gallop gait are analyzed. Then, based on the “three-way” control idea of the SLIP model, the running control strategy for the double flying phase Bound gait is designed. Subsequently, the SLIP model is optimized to derive the D-SLIP model with two touchdown legs, and its dynamic characteristics are analyzed. And the D-SLIP model is applied to the running control strategy of the Rotatory gallop gait. Furthermore, joint simulation verification is conducted using Adams virtual prototyping and MATLAB/Simulink control systems for the designed control strategies. Finally, experimental verification is performed for the double flying phase Bound gait running control strategy. The experimental results demonstrate that the quadruped robot can achieve high-speed and stable running.

To improve the path optimization effect and search efficiency of ant colony optimization (ACO), an improved ant colony algorithm is proposed. A collar path is generated based on the known environmental information to avoid the blindness search at early planning. The effect of the ending point and the turning point is introduced to improve the heuristic information for high search efficiency. The adaptive adjustment of the pheromone intensity value is introduced to optimize the pheromone updating strategy. A variety of control strategies for updating the parameters are given to balance the convergence and global search ability. Then, the improved obstacle avoidance strategies are proposed for dynamic obstacles of different shapes and motion states, which overcome the shortcomings of existing obstacle avoidance strategies. Compared with other improved algorithms in different simulation environments, the results show that the algorithm in this paper is more effective and robust in complicated and large environments. On the other hand, the comparison with other obstacle avoidance strategies in a dynamic environment shows that the strategies designed in this paper have higher path quality after local obstacle avoidance, lower requirements for sensor performance, and higher safety.

Prostate cancer has one of the highest incidences of male malignant tumors worldwide. Its treatment involves the robotic implantation of radioactive seeds in the perineum, a safe and effective procedure for early, low-risk prostate cancer. In order to ensure precise positioning, the seed implantation needle is set at low terminal velocity. In this paper, the motion output position instability caused by the friction torque of the robot’s motor and rotating joint during low velocity motion was analyzed and studied. This paper also presents a compensation control method based on the LuGre friction model, which offers piecewise parameter identification with GA-PSO. First, based on an analysis of its structure and working principle, the friction torque model of the robotic system and the torque model of the driving motor are established, and the influence of friction torque on motion stability analyzed. Then, based on experimental data of the relationship between velocity and friction torque for no-friction compensation, the velocity point of the minimum torque of the rotating joint and the critical Stribeck velocity point were used for segmental parameter identification; cubic spline interpolation was used for segmental fitting. Furthermore, on the basis of the LuGre model identification method, parameter identification of the genetic algorithm-particle swarm optimization, and compensation control of the LuGre friction model, a control method is analysed and set forth. Malab2017a/Simulink simulation software was used to simulate and analyze the control method, and verify its feasibility. Finally, the cantilever prostate seed implantation robot system was tested to verify the effectiveness of the segmented identification method and the compensation control strategy. The results reveal that motion output position stability at low velocity meets the requirements of the cantilever prostate seed implantation robot, thus providing a vital reference for further research.

When treating prostate cancer, the use of puncture robots is an effective method to perform radioactive seed implantation surgery. However, when the puncture needle enters the lesion, the soft tissue is easily deformed owing to the complex force between the puncture needle and soft tissue, which leads to a puncture deviation between the needle tip and target point. To solve this problem effectively, the prostate soft tissue puncture process is studied based on the analysis of the puncture needle–soft tissue interaction. First, the puncture force is classified into contact, friction, and cutting forces by a quantitative decomposition method, and the corresponding force model is established. Based on the theoretical analysis of the model, it is deduced that these factors can affect the deformation created by puncturing the soft tissue. Subsequently, a puncture platform is built and many biomimetic soft tissue models are established. Multiple puncture experiments on the influencing factors are conducted using the method of controlling a single variable. Using the spatial puncture deviation as the test metrics, the significance of the influencing factors of the puncture deformation is verified. Finally, it can be concluded from the experimental analysis that the main factor that affects the puncture deviation is the puncture speed, whereas the puncture depth has no significant influence. The puncture speed was optimized and verified by experiments, and the results showed that a stable puncture accuracy under different puncture depths can be obtained by selecting an optimized puncture speed (12.6 mm/s). This work provides a design reference to study the positioning accuracy of minimally invasive puncture surgery.