The present disclosure relates to a method for generating a novel impedance configuration for a three-degree-of-freedom (3DOF) leg of a hydraulically-driven legged robot. The method includes: separately determining variations of input signals of an inner position-based control loop and an inner force-based control loop of a hydraulic drive unit of each joint based on an obtained mathematical model; generating a novel impedance configuration in which position-based control is performed on a hydraulic drive unit of a hip joint, and force-based control is performed on hydraulic drive units of a knee joint and an ankle joint in a hydraulic drive system of the leg of a to-be-controlled robot; and performing forward calculation by using the leg mathematical model, to obtain an actual position and a force variation of the foot of the leg of the to-be-controlled robot to control motion of the foot of the to-be-controlled robot within motion space.

A method is described for collision-free motion planning of a first manipulator with closed kinematics. The method includes defining a dynamic optimization problem, solving the optimization problem using a numerical approach, and determining a first movement path for the first manipulator based on the solution of the optimization problem. The dynamic optimization problem includes a cost function that weights states and control variables of the first manipulator, a dynamic that defines states and control variables of the first manipulator as a function of time, and at least one inequality constraint for a distance to collisions. Furthermore, the optimization problem includes at least one equality constraint for the closed kinematics.

A grasp generation technique for robotic pick-up of parts. A database of solid or surface models is provided for all objects and grippers which are to be evaluated. A gripper is selected and a random initialization is performed, where random objects and poses are selected from the object database. An iterative optimization computation is then performed, where many hundreds of grasps are computed for each part with surface contact between the part and the gripper, and sampling for grasp diversity and global optimization. Finally, a physical environment simulation is performed, where the grasps for each part are mapped to simulated piles of objects in a bin scenario. The grasp points and approach directions from the physical environment simulation are then used to train neural networks for grasp learning in real-world robotic operations, where the simulation results are correlated to camera depth image data to identify a high quality grasp.

An automated construction robot system includes: a mobile base assembly configured to be displaceable within the work area; a head assembly configured to process a work surface; an arm assembly configured to moveably-couple the head assembly and the mobile base assembly and controllably-displace the head assembly with respect to the work surface; a machine vision system configured to scan a target area and generate target area information; and a computational system configured to: process the target area information to identify a surface defect, generate one or more remedial instructions based, at least in part, upon the surface defect identified, and manipulate one or more of the mobile base assembly, the head assembly and the arm assembly based, at least in part, upon the one or more remedial instructions.

A gripping system for an autonomous guided vehicle (AGV) and such AGV are disclosed herein. The gripping system for automated gripping and pulling/pushing a cart comprises a unique gripping end effector ensuring controlled steering of the cart while allowing rolling of the cart relative to the body of the AGV. The end effector comprises means for indication of state of connection between the cart and the gripping system, ensuring a reliable, safe and efficient cart gripping and pulling operation.

A gripper device and method is provided. The method includes capturing, using an electronic device, information of an object that is indicative of holding position; determining, using the information, by a hardware processor, an optimal holding orientation and an optimal movement of at least one of (i) a plurality of fingers, or (ii) a plurality of suction cups of a gripper device; identifying the at least one of (i) the plurality of fingers, and (ii) the plurality of suction cups as one or more grasping components based on the information, the optimal holding orientation and the optimal movement; and enabling, using an actuator, the one or more identified grasping components to grasp the object based on the information, the optimal holding orientation and the optimal movement.

Systems and methods for a stud plate connector end effector are disclosed. A system includes a first clamping gripper and a second clamping gripper configured to secure a first piece of lumber during a lumber joining process. An abutting gripper located perpendicular to the first and second clamping grippers is configured to secure a second piece of lumber during the lumber joining process. One end of the second piece of lumber is positioned in contact with the first piece of lumber. A fastening tool located on an opposite end from the abutting gripper is configured to attach the first and second pieces of lumber together. A vision system is configured to align the second piece of lumber to the first piece of lumber. The first, second and abutting grippers align the first and second pieces of lumber based on an alignment data from the vision system.

A robotic assembly for servicing equipment, the robotic assembly including an area configured to receive components associated with a workscope of the equipment; an environmental capture device configured to capture information associated with an environment in which the robotic assembly is disposed; and one or more computing devices configured to: locate the equipment in the environment; autonomously navigate the robotic assembly through the environment to the equipment; and autonomously adjust a position of the robotic assembly in response to the workscope.

A soft gripper including tentacles, each tentacle includes lower and upper members connected by a connector. Each member includes guide discs, and each guide disc includes a ring with passthrough holes, and a spacer located in a donut hole of the ring with passthrough holes, the passthrough holes collectively define cable pathways. The connector includes a center thru-hole and transfer channels. Cables have proximal ends attached to actuators and extend through apertures of a baseplate located at a proximal end of the lower member. A set of lower cables extend through the lower ring passthrough holes to couple to a distal lower guide disc. A set of upper cables extend through the lower spacer passthrough holes, through the transfer channels to the upper ring passthrough holes to couple to a distal upper guide ring, and an end cap is attached to the distal end of the upper member.

Asynchronous robotic control utilizing a trained critic network. During performance of a robotic task based on a sequence of robotic actions determined utilizing the critic network, a corresponding next robotic action of the sequence is determined while a corresponding previous robotic action of the sequence is still being implemented. Optionally, the next robotic action can be fully determined and/or can begin to be implemented before implementation of the previous robotic action is completed. In determining the next robotic action, most recently selected robotic action data is processed using the critic network, where such data conveys information about the previous robotic action that is still being implemented. Some implementations additionally or alternatively relate to determining when to implement a robotic action that is determined in an asynchronous manner.