9 results about "Gait planning" patented technology

The application discloses a kind of small celestial body weak gravity condition under detection robot movement control method and device, the method of the present application includes first to detection robot is carried out steady gait planning, at the end time of the foot end trajectory obtained, add the active attachment bias Δr for ensuring that detection robot is attached to the clamping of small celestial body star table;Then the foot end trajectory is as leg desired position, inverse kinematics is solved to obtain the desired joint angle of each joint, obtains the current joint angle of each joint of detection robot, and calculates the deviation between the current joint angle and the desired joint angle of each joint, and the deviation is input into the movement controller to obtain the joint output control torque, the corresponding joint is controlled by joint output control torque, until the deviation is less than set value.The present application can realize the stable attachment and movement of detection robot on the surface of small celestial body under the condition of weak gravity of small celestial body, create prerequisite for the smooth development of detection task.

This invention discloses a hierarchical reinforcement learning method and system for adaptive gait generation in small robots. The method includes: collecting environmental perception data and robot body state data through a perception module, and extracting environmental features and robot state information; generating gait planning instructions through a high-level planning module based on the environmental features and robot state information; generating joint control instructions through a low-level control module combined with a multibody dynamics model based on the gait planning instructions and robot state information; and executing the joint control instructions to control the robot joint movement to achieve adaptive gait generation. Using this invention, the problem of adaptive gait generation in complex environments can be solved, improving the robot's motion intelligence, control accuracy, and rapid adaptation to unknown terrain.

The invention provides a wheel step type rotary drilling rig intelligent gait planning and cooperative control method and system, and the method comprises the steps: collecting the torque value of a drill rod, the rotation direction of the drill rod, the contact force deflection angle of each supporting leg steel claw, the leg vibration frequency, and the feeding speed and rotation speed of drilling operation; fourier transform is carried out on the torque value of the drill rod to obtain the torque fluctuation frequency of the drill rod; evaluating a phase angle between the rotation direction of the drill rod and the circumferential layout of the supporting legs, screening out leg positions with the phase angle exceeding a preset safety angle threshold value and vibration exceeding a limit, and adjusting the unfolding radius; identifying load concentration orientation distribution of each leg by taking the expansion radius adjustment amount of the supporting leg as an independent variable and the steel claw contact force deflection angle change rate as a dependent variable; gait scheduling arrangement, feeding speed and rotation speed are fused and optimized, a switching time window is divided according to the leg lifting and falling action period of each supporting leg, and feeding propulsion and rotation start-stop takt is matched in an alternating gap of adjacent supporting legs to form a cooperative control signal.

The application discloses a disturbance estimation and humanoid robot whole-body disturbance rejection control method based on DCM residual, and relates to the technical field of humanoid robot motion control. A disturbance observer based on DCM residual is designed, external force disturbance acting on the robot centroid can be estimated in real time without the need of a foot force sensor, and the disturbance observer has stronger robustness to model errors and sensor noise; secondly, a hierarchical online motion planner is proposed; a foothold planner optimizes the foothold position and gait cycle in real time through quadratic programming; a gait generator adopts a model predictive controller based on three-dimensional DCM, generates a robust centroid trajectory and gait according to the planned foothold; further, a whole-body disturbance rejection controller is developed, the estimated disturbance is embedded into the whole-body dynamics constraint, active compensation of the disturbance and direct control of the joint torque are realized. The application is used to solve the problems of insufficient robustness, low disturbance estimation accuracy and coupling of gait planning and whole-body control existing in the prior art.

This invention relates to a phased adaptive control method for an exoskeleton used in rehabilitation training, belonging to the field of human-machine collaborative motion assistance technology. This method identifies gait phases based on hip joint angle and angular velocity, dividing the gait into four phases: early swing phase, late swing phase, early stance phase, and late stance phase. It then uses a polynomial curve with control points to complete basic gait planning. Finally, it adaptively adjusts the gait trajectory control points based on the swing dynamics and stance phase. This invention enables phased adaptive adjustment of gait trajectory and auxiliary torque, improving training flexibility and patient participation, effectively avoiding sensor signal interference and insufficient accuracy, and contributing to improved rehabilitation training outcomes.

This invention discloses a hierarchical collaborative reinforcement learning motion control architecture for bipedal robots. First, a sensing unit collects real-time robot state and environmental data, providing rich information to the upper-level main control unit to assist in making rational decisions and improve adaptability to complex terrains. Second, the upper-level main control unit uses reinforcement learning models for inference, enabling gait planning, motion pattern decision-making, and terrain recognition. It can automatically adjust according to terrain and task, improving stability and flexibility. It can also output control parameters for adaptive control, dynamically adjusting based on multiple factors to meet complex motion requirements. Furthermore, the hierarchical collaborative architecture features clear division of labor and collaborative cooperation, efficiently handling control problems and improving system performance and response speed. Finally, the middle-level computation unit dynamically allocates computation tasks according to the computing power of heterogeneous chips, fully utilizing chip potential, improving computational efficiency, reducing computational latency, enhancing the robot's real-time response capabilities, and better adapting to complex environments and tasks.

The present application relates to the technical field of robot control, in particular to a kind of water reservoir bottom mud dredging robot gait planning method and system, comprising: based on the collection multimodal sensing information, constructs the environment semantic map of dredging robot;According to the environment semantic map, drive foot end actuator and water reservoir bottom mud environment interact, and according to the real-time force and motion data existing in the interaction process, obtain the local environment dynamics parameters of water reservoir bottom mud environment;According to local environment dynamics parameters and preset gait sequence instruction, predict the predicted joint control instruction on future period, and drive foot end actuator according to predicted joint control instruction;The execution parameter of foot end actuator is monitored, and when execution parameter exceeds preset safety execution parameter, generate fault-tolerant joint control instruction to drive foot end actuator, effectively solve the technical problems that existing gait planning method cannot guarantee the stable operation and efficient operation of dredging robot.

The application provides a low-resistance gait planning method, device and equipment for an underwater robot and a storage medium, and relates to the technical field of robot control. The method comprises the following steps: dividing the motion gait of a legged robot into a support phase and a swing phase. In the support phase, the legged robot is propelled forward by the reaction force of the contact between the foot end and the ground. In the swing phase, the legged robot moves forward in the posture of retracting the limbs to reduce the frontal area by imitating the frog swimming. The spatial position of the foot end of the legged robot in the trunk coordinate system is calculated according to the rotation angle of each joint. The real-time resistance of the legged robot is calculated based on the real-time frontal area of each leg segment of the legged robot and the real-time speed relative to the water flow. A prediction model is constructed, a target function and a constraint condition are set, a sequential quadratic programming algorithm is used for rolling optimization and solving, and the optimal torque sequence of each joint is obtained. The optimal torque sequence is sent to the joint motor for execution, so as to control the low-resistance gait motion of the underwater robot.

The application provides a four-degree-of-freedom origami soft robot for underground cable inspection, and belongs to the technical field of soft crawling robots. The structure comprises an origami soft driver, movable rigid-flexible clamping jaws, a ring-type silica gel soft foot, a front sleeve, a rear sleeve, a front soft gasket, a rear soft gasket, a front fixed pin, a rear fixed pin, a front Z-shaped base, a rear Z-shaped base, an arc-shaped front base, an arc-shaped rear base, a front rotating shaft, a rear rotating shaft, a front clamping groove, a rear clamping groove, a front infrared camera and a rear infrared camera, and further comprises a manufacturing process of the origami soft driver, parallel gas-liquid driving control multi-chamber and gait planning of four-degree-of-freedom movement in imitation of lepidopterous larva peristalsis. The origami soft driver has four degrees of freedom. Through parallel control algorithm design and multi-chamber gas-liquid driving control, different movement effects of the origami soft robot are achieved. Through imitation of the crawling process of lepidopterous larvae, peristaltic crawling of the robot and various movement effects can be achieved.