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.
For several years, regrasp planning has been the object of attention for different studies. When an object's pick-up grasp is incompatible with its put-down grasp, regrasp can be performed to achieve the goal pose @cite_5 , which is often the solution for many tasks in robotic applications. Some early work includes the regrasp planning of robotic manipulators such as @cite_5 , @cite_7 and @cite_22 , but those solutions only considered one-armed robots with a fixed position, therefore they lack specific considerations that must be taken into account for humanoid robots such as HRP3 @cite_28 , WALK-MAN @cite_10 and TALOS @cite_2 .
@cite_22 @cite_7 @cite_28 @cite_2 @cite_5 @cite_10
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This paper deals with motion planning for robots manipulating movable objects among obstacles. We propose a general manipulation planning approach capable of addressing continuous sets for modeling both the possible grasps and the stable placements of the movable object, rather than discrete sets generally assumed by the previous approaches. The proposed algorithm relies on a topological property that characterizes the existence of solutions in the subspace of configurations where the robot grasps the object placed at a stable position. It allows us to devise a manipulation planner that captures in a probabilistic roadmap the connectivity of sub-dimensional manifolds of the composite configuration space. Experiments conducted with the planner in simulated environments demonstrate its efficacy to solve complex manipulation problems. Part 1 Introduction: HANDEY robot programming why is robot programming difficult? what HANDEY is and is not. Part 2 Planning pick-and-place operations: examples of constraint interactions a brief overview of HANDEY the HANDEY planners combining the planners previous work. Part 3 Basics: polyhedral models robot models world models configuration space. Part 4 Gross motion planning: approximating the COs for revolute manipulators slice projections for polyhedral links efficiency considerations searching for paths a massively parallel algorithm. Part 5 Grasp planning: basic assumptions grasp planner overview using depth data in grasp planning the potential-field planner. Part 6 Regrasp planning: grasps placements on a table constructing the legal grasp placement pairs solving the regrasp problem in handey an example computing the constraints regrasping using two parallel-jaw grippers. Part 7 Co-ordinating multiple robots: co-ordination and parallelism robot co-ordination as a scheduling problem the task completion diagram generating the TC diagram more on schedules other issues. Part 8 Conclusion: evolution path planning experimentation what we learned. (Part contents) The upcoming generation of humanoid robots will have to be equipped with state-of-the-art technical features along with high industrial quality, but they should also offer the prospect of effective physical human interaction. In this paper we introduce a new humanoid robot capable of interacting with a human environment and targeting industrial applications. Limitations are outlined and used together with the feedback from the DARPA Robotics Challenge, and other teams leading the field in creating new humanoid robots. The resulting robot is able to handle weights of 6 kg with an out-stretched arm, and has powerful motors to carry out fast movements. Its kinematics have been specially designed for screwing and drilling motions. In order to make interaction with human operators possible, this robot is equipped with torque sensors to measure joint effort and high resolution encoders to measure both motor and joint positions. The humanoid robotics field has reached a stage where robustness and repeatability is the next watershed. We believe that this robot has the potential to become a powerful tool for the research community to successfully navigate this turning point, as the humanoid robot HRP-2 was in its own time. Robotic manipulators in contemporary work-cells are often incapable of solving even simple pick-and-place operations. More specifically, most systems require each object to be supplied in the same and pre-defined way. Introducing regrasping to the production cycle relaxes the constraints imposed on supply mechanisms. Regrasping describes the operation that must be performed whenever an object's pick-up grasp is incompatible with its put-down grasp. The paper presents a novel approach to solve the regrasp problem for robots equipped with parallel-jaw end-effecters. The proposed method first evaluates the object's possible grasps and placements. These are subsequently combined to form grasp-placement-grasp triples. A regrasp sequence leading from the pick-up to the put-down grasp is generated by searching through the resulting space of compatible grasp-placement-grasp triples. The algorithm takes kinematic and geometric constraints of both the manipulator and the objects into account. Computational concerns are addressed by subdividing the calculation into an off-line and a fast online phase. In this work, we present WALK-MAN, a humanoid platform that has been developed to operate in realistic unstructured environment, and demonstrate new skills including powerful manipulation, robust balanced locomotion, high-strength capabilities, and physical sturdiness. To enable these capabilities, WALK-MAN design and actuation are based on the most recent advancements of series elastic actuator drives with unique performance features that differentiate the robot from previous state-of-the-art compliant actuated robots. Physical interaction performance is benefited by both active and passive adaptation, thanks to WALK-MAN actuation that combines customized high-performance modules with tuned torque velocity curves and transmission elasticity for high-speed adaptation response and motion reactions to disturbances. WALK-MAN design also includes innovative design optimization features that consider the selection of kinematic structure and the placement of the actuators with the body structure to maximize the robot performance. Physical robustness is ensured with the integration of elastic transmission, proprioceptive sensing, and control. The WALK-MAN hardware was designed and built in 11 months, and the prototype of the robot was ready four months before DARPA Robotics Challenge (DRC) Finals. The motion generation of WALK-MAN is based on the unified motion-generation framework of whole-body locomotion and manipulation (termed loco-manipulation). WALK-MAN is able to execute simple loco-manipulation behaviors synthesized by combining different primitives defining the behavior of the center of gravity, the motion of the hands, legs, and head, the body attitude and posture, and the constrained body parts such as joint limits and contacts. The motion-generation framework including the specific motion modules and software architecture is discussed in detail. A rich perception system allows the robot to perceive and generate 3D representations of the environment as well as detect contacts and sense physical interaction force and moments. The operator station that pilots use to control the robot provides a rich pilot interface with different control modes and a number of teleoperated or semiautonomous command features. The capability of the robot and the performance of the individual motion control and perception modules were validated during the DRC in which the robot was able to demonstrate exceptional physical resilience and execute some of the tasks during the competition.