In this paper, we consider folding assembly as an assembly primitive suitable for dual-arm robotic assembly, that can be integrated in a higher level assembly strategy. The system composed by two pieces in contact is modelled as an articulated object, connected by a prismatic-revolute joint. Different grasping scenarios were considered in order to model the system, and a simple controller based on feedback linearisation is proposed, using force torque measurements to compute the contact point kinematics. The folding assembly controller has been experimentally tested with two sample parts, in order to showcase folding assembly as a viable assembly primitive.
Robotic assembly with dual-arm manipulators has advantages and poses specific challenges; an in-depth review of the state-of-the-art for dual arm manipulation can be found in @cite_23 . The increased flexibility and larger independence from a structured environment comes at the cost of an increase in system complexity. In particular, interaction forces between the manipulators and grasped objects require careful consideration, due to the closed kinematic chain @cite_14 . While both single and dual arm robots can be used for applying planning strategies @cite_0 @cite_11 @cite_15 , a dual arm robot can take advantage from the cooperation between manipulators that allows for a reduction in task complexity @cite_24 . Several works detail master-slave implementations. When carrying a load, it is common to have one arm perform the motion control of the overall system, while its slave uses force feedback to ensure tracking of the object changing position @cite_10 @cite_9 . Master-slave relationships are also relevant in scenarios where the need to overcome a physical distance between an operator and its work subject is present @cite_19 @cite_8 .
@cite_14 @cite_8 @cite_9 @cite_0 @cite_24 @cite_19 @cite_23 @cite_15 @cite_10 @cite_11
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To make production lines more flexible, dual-arm robots are good candidates to be deployed in autonomous assembly units. In this paper, we propose a sparse kinematic control strategy, that minimizes the number of joints actuated for a coordinated task between two arms. The control strategy is based on a hierarchical sparse QP architecture. We present experimental results that highlight the capability of this architecture to produce sparser motions (for an assembly task) than those obtained with standard controllers. Advanced modern humanoid robots often have complex manipulators with a large number of degrees of freedom. Thus, motion planning for such manipulators is a very computationally challenging problem. However, often robotic manipulators allow the wrist degrees of freedom to be controlled independently from the configuration of the rest of the arm. In this paper we show how to split the high dimensional planning problem into two lower-dimensional sub-problems—planning for the main arm joints and planning for the wrist joints, without losing guarantees on completeness. This approach is an extension of our previously developed framework for planning with adaptive dimensionality. Experimentally, we show that this approach is very effective in speeding up planning for robotic arms on Willow Garage’s PR2 platform. We compare our algorithm with several popular alternative approaches for performing motion planning for robotic arms. The results we observe illustrate that our algorithm provides a good balance between planning time, planning success rate, path consistency, and path quality. This paper proposes an optimization method for dual-arm robots, inspired by a study on human asymmetric bimanual action called Guiard's principle which states that when humans perform asymmetric bimanual tasks, the right hand (as the lateral preference) performs a fine motion (and force) resolution, while the left hand performs a coarse resolution. To effectively transfer the human bimanual-task knowledge to dual-arm robots, we proposed a cost function, based on task-compatibility index, which is used to set the desired motion and force resolution for each end-effector of the dual-arm according to its role in the bimanual action. Thus the right-arm posture is optimized such that the tool attached to its end-effector can exert fine motion and force. And the left-arm posture is optimized to assume a strong and dynamic structural support for the right arm action. We experimentally compare the proposed cost function against previous methods for dual-arm robots using two six degrees-of-freedom torque-controlled manipulators. The control performance, when mimicking Guiard's principle shows considerably better results. This paper demonstrates a method for simultaneous transfer of positional and force requirements for in-contact tasks from a human instructor to a robotic arm through kinesthetic teaching. This is achieved by a specific use of the sensory configuration, where a force torque sensor is mounted between the tool and the flange of a robotic arm endowed with integrated torque sensors at each joint. The human demonstration is modeled using Dynamic Movement Primitives. Following human demonstration, the robot arm is provided with the capacity to perform sequential in-contact tasks, for example writing on a notepad a previously demonstrated sequence of characters. During the reenactment of the task, the system is not only able to imitate and generalize from demonstrated trajectories, but also from their associated force profiles. In fact, the implemented framework is extended to successfully recover from perturbations of the trajectory during reenactment and to cope with dynamic environments.