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.
Recent work included more complicated grasp planners and was integrally considered with backtracking, geometrical reasoning, and motion planning. For example, @cite_25 presented a framework which integrated motion planning and preparatory manipulation planning to move objects among obstacles. @cite_28 used shape primitives to plan the grasps of a multi-finger hand and implemented the preparatory manipulation planning of a cup using the multi-finger hand. @cite_20 used Bayesian Network to model task constraints and plan the preparatory manipulation sequence of containers and toys. @cite_12 used integral primitive-based prehensile and non-prehensile grasp planning to exercise preparatory manipulation of everyday objects. Lozano- @cite_1 used symbolic search to find manipulation sequences under various constraints. The method was used to plan sequences for a mobile manipulator to rearrange objects. Similarly, @cite_23 , @cite_16 , Toussaint @cite_18 , and @cite_27 respectively used symbolic reasoning to remove obstacles and pick up or rearrange objects. Especially, Toussaint @cite_18 and @cite_27 presented algorithms to plan motion sequences for robots to stack distributed objects, considering both task and motion constraints. @cite_6 presented a planner considering the constraints from coordinated manipulators. More extensively, @cite_2 and @cite_3 respectively presented a framework work that used integral prehensile and non-prehensile planning to generate sequential manipulation.
@cite_18 @cite_28 @cite_1 @cite_16 @cite_6 @cite_3 @cite_27 @cite_23 @cite_2 @cite_20 @cite_25 @cite_12
2140667257 2010274050 2968156685 1987985958
We present an example-based planning framework to generate semantic grasps, stable grasps that are functionally suitable for specific object manipulation tasks. We propose to use partial object geometry, tactile contacts, and hand kinematic data as proxies to encode task-related constraints, which we call semantic constraints. We introduce a semantic affordance map, which relates local geometry to a set of predefined semantic grasps that are appropriate to different tasks. Using this map, the pose of a robot hand with respect to the object can be estimated so that the hand is adjusted to achieve the ideal approach direction required by a particular task. A grasp planner is then used to search along this approach direction and generate a set of final grasps which have appropriate stability, tactile contacts, and hand kinematics. We show experiments planning semantic grasps on everyday objects and applying these grasps with a physical robot. We design an example based planning framework to generate semantic grasps, stable grasps that are functionally suitable for specific object manipulation tasks. We propose to use partial object geometry, tactile contacts, and hand kinematic data as proxies to encode semantic constraints, which are task-related constraints. We introduce a semantic affordance map, which relates local geometry to a set of predefined semantic grasps that are appropriate to different tasks. Using this map, the pose of a robotic hand can be estimated so that the hand is adjusted to achieve the ideal approach direction required by a particular task. A grasp planner is then used to generate a set of final grasps which have appropriate stability, tactile contacts, and hand kinematics along this approach direction. We show experiments planning semantic grasps on everyday objects and executing these grasps with a physical robot. We present an algorithm that produces a plan for relocating obstacles in order to grasp a target in clutter by a robotic manipulator without collisions. We consider configurations where objects are densely populated in a constrained and confined space. Thus, there exists no collision-free path for the manipulator without relocating obstacles. Since the problem of planning for object rearrangement has shown to be NP-hard, it is difficult to perform manipulation tasks efficiently which could frequently happen in service domains (e.g., taking out a target from a shelf or a fridge).Our proposed planner employs a collision avoidance scheme which has been widely used in mobile robot navigation. The planner determines an obstacle to be removed quickly in real time. It also can deal with dynamic changes in the configuration (e.g., changes in object poses). Our method is shown to be complete and runs in polynomial time. Experimental results in a realistic simulated environment show that our method improves up to 31 of the execution time compared to other competitors. 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.