This paper presents a manipulation planning algorithm for robots to reorient objects. It automatically finds a sequence of robot motion that manipulates and prepares an object for specific tasks. Examples of the preparatory manipulation planning problems include reorienting an electric drill to cut holes, reorienting workpieces for assembly, and reorienting cargo for packing, etc. The proposed algorithm could plan single and dual arm manipulation sequences to solve the problems. The mechanism under the planner is a regrasp graph which encodes grasp configurations and object poses. The algorithms search the graph to find a sequence of robot motion to reorient objects. The planner is able to plan both single and dual arm manipulation. It could also automatically determine whether to use a single arm, dual arms, or their combinations to finish given tasks. The planner is examined by various humanoid robots like Nextage, HRP2Kai, HRP5P, etc., using both simulation and real-world experiments.
There are also lots of studies on dual-arm or multi-arm preparatory manipulation planning. For example, @cite_30 used predefined grasps to plan sequential manipulation for multiple manipulators in early time. @cite_17 applied the grasp-placement tables developed by @cite_14 to dual-arm robots. @cite_22 used decomposition to plan grasps and implemented dual-arm regrasp of complicated mesh models. Graphs, instead of grasp-placement tables, were used in the planner. @cite_31 presented regrasp and hand-over planning of dual-arm robots across multi-modal configuration spaces, and implemented a practical system in @cite_8 . Details of multi-modal motion planning could be found in @cite_0 , which made a concrete description of the concept and presented several implementations. More recent work like @cite_9 used dual-arm sequential manipulation planning to wrap up the fabric. It is also quite relevant except that reorienting directions are constrained by the wrapping heuristics. @cite_10 , @cite_11 , and Suarez- @cite_29 respectively applied integral prehensile and non-prehensile sequential dual-arm motion planning to the preparatory manipulation of a chair. @cite_7 presented a framework for simultaneous handover and visual recognition.
@cite_30 @cite_14 @cite_11 @cite_22 @cite_7 @cite_8 @cite_9 @cite_29 @cite_0 @cite_31 @cite_10 @cite_17
2005777097 1987985958 2225490378 2023106861
This research develops a robotic vision and manipulation technologies for random bin-picking. Especially, we focus on the manipulation technology where a dual-arm manipulator first picks up an object from the pile, then regrasps it from the right hand to the left hand, and finally places it to the fixture. We first explain an overview of our research project. Then, we explain about the grasp manipulation planner for a dual-arm manipulator. Here, our grasp planner is well applied for objects which can be approximated by multiple cylinders. Also, our pick-and-place planner can effectively find a regrasping posture of an object. In addition to the manipulation technology, we briefly explain about our vision technology to measure the position orientation of an object. Finally, to show the effectiveness of our proposed approach, we show an experimental result of a dual-arm manipulator. Many manipulation planning problems involve several related sub-problems, such as the selection of grasping points on an object, choice of hand posture, and determination of the arm's configuration and evolving trajectory. Traditionally, these planning sub-problems have been handled separately, potentially leading to sub-optimal, or even infeasible, combinations of the individually determined solutions. This paper formulates the combined problem of grasp contact selection, grasp force optimization, and manipulator arm hand trajectory planning as a problem in optimal control. That is, the locally optimal trajectory for the manipulator, hand mechanism, and contact locations are determined during the pre-grasping, grasping, and subsequent object transport phase. Additionally, a barrier function approach allows for non-feasible grasps to be optimized, enlarging the region of convergence for the algorithm. A simulation of a simple planar object manipulation task is used to illustrate and validate the approach. 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. Traditionally, grasp and arm motion planning are considered as separate tasks. This paper presents an integrated approach that only requires the initial configuration of the robotic arm and the pose of the target object to simultaneously plan a good hand pose and arm trajectory to grasp the object. The planner exploits the concept of independent contact regions to look for the best possible grasp. The goal poses for the end effector are obtained using two different methods: one that biases a sampling approach towards favorable regions using principal component analysis, and another one that considers the capabilities of the robotic arm to decide the most promising hand poses. The proposed method is evaluated using different scenarios for the humanoid robot Spacejustin.