This paper presents a Center of Mass (CoM) based manipulation and regrasp planner that implements stability constraints to preserve the robot balance. The planner provides a graph of IK-feasible, collision-free and stable motion sequences, constructed using an energy based motion planning algorithm. It assures that the assembly motions are stable and prevent the robot from falling while performing dexterous tasks in different situations. Furthermore, the constraints are also used to perform an RRT-inspired task-related stability estimation in several simulations. The estimation can be used to select between single-arm and dual-arm regrasping configurations to achieve more stability and robustness for a given manipulation task. To validate the planner and the task-related stability estimations, several tests are performed in simulations and real-world experiments involving the HRP5P humanoid robot, the 5th generation of the HRP robot family. The experiment results suggest that the planner and the task-related stability estimation provide robust behavior for the humanoid robot while performing regrasp tasks.
Recently, several manipulation and regrasp planners have been proposed for dual-armed robots with a focus on efficient and collision free object manipulation. In @cite_24 , comparison of different algorithms for single-arm and dual-arm regrasp were performed in different scenarios. In @cite_8 a planner for a robot to keep an object stable under a sequence of external forces was proposed. A CoM-based grasp pose adaptation method for picking up objects with one arm was introduced in @cite_14 . It used 3D perception and force torque feedback to reduce the load of joints during manipulation. In @cite_13 , a planner for autonomous in-hand manipulation using finger gaiting was presented. Also, some researchers have worked on manipulation planning on constraints such as in @cite_9 . Some optimization-base motion planning algorithms are summarized in @cite_23 . These work offered several solutions for regrasp planning tasks, but they failed to consider the balance of robot-object system. In this regard, our planner presents a CoM-based solution that implements stability constraints during regrasp planning.
@cite_14 @cite_8 @cite_9 @cite_24 @cite_23 @cite_13
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This paper explores the problem of autonomous, in-hand regrasping-the problem of moving from an initial grasp on an object to a desired grasp using the dexterity of a robot's fingers. We propose a planner for this problem which alternates between finger gaiting, and in-grasp manipulation. Finger gaiting enables the robot to move a single finger to a new contact location on the object, while the remaining fingers stably hold the object. In-grasp manipulation moves the object to a new pose relative to the robot's palm, while maintaining the contact locations between the hand and object. Given the object's geometry (as a mesh), the hand's kinematic structure, and the initial and desired grasps, we plan a sequence of finger gaits and object reposing actions to reach the desired grasp without dropping the object. We propose an optimization based approach and report in-hand regrasping plans for 5 objects over 5 in-hand regrasp goals each. The plans generated by our planner are collision free and guarantee kinematic feasibility. This paper proposes a planning framework to deal with the problem of computing the motion of a robot with dual arm hand, during an object pick-and-place task. We consider the situation where the start and goal configurations of the object constrain the robot to grasp the object with one hand, to give it to the other hand, before placing it in its final configuration. To realize such a task, the proposed framework treats the grasp computation, for one or two multi-fingered hands, of an arbitrarily-shaped object, the exchange configuration and finally the motion of the robot arms and body. In order to improve the planner performance, a context-independent grasp list is computed offline for each hand and for the given object as well as computed offline roadmap that will be adapted according to the environment composition. Simulation results show the planner performance on a complex scenario. We present a planner for a robot to keep an object stable under a sequence of external forces. We assume a robot with multiple manipulators, with a gripper at the end of each manipulator. We make two key contributions: First, given an external force, we propose a method to check the feasibility of gripper placements on the object to resist the force. This method takes into account both the manipulator joint torque limits and the force torque limits of the grippers-object system. Second, given a sequence of external forces, we propose a planner that generates a sequence of gripper placements and manipulator configurations, along with the motion to move between these configurations. The planner minimizes the number of different gripper placements required to resist the forces, resulting in efficient planning and execution. We perform experiments to verify the predictions of our feasibility checking method and to measure the performance of our planner. The goal of this paper is to develop efficient regrasp algorithms for single-arm and dual-arm regrasp and compares the performance of single-arm and dual-arm regrasp by running the two algorithms thousands of times. We focus on pick-and-place regrasp which reorients an object from one placement to another by using a sequence of pick-ups and place-downs. After analyzing the simulation results, we find dual-arm regrasp is not necessarily better than single-arm regrasp: Dual-arm regrasp is flexible. When the two hands can grasp the object with good clearance, dual-arm regrasp is better and has higher successful rate than single-arm regrasp. However, dual-arm regrasp suffers from geometric constraints caused by the two arms. When the grasps overlap, dual-arm regrasp is bad. Developers need to sample grasps with high density to reduce overlapping. This leads to exploded combinatorics in previous methods, but is possible with the algorithms presented in this paper. Following the results, practitioners may choose single-arm or dual-arm robots by considering the object shapes and grasps. Meanwhile, they can reduce overlapping and implement practical dual-arm regrasp by using the presented algorithms.