Robotic Grasp Region Planning for Manipulation Reliability
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Solution Overview
Problem
Existing robotic systems face challenges in efficiently planning and executing grasps on objects due to the decoupling of grasp determination and movement planning, leading to potential grasp failures and repeated attempts.
Innovation Solution
The implementation of grasp regions associated with an object, allowing for a continuous set of potential grasps by an end effector, provides flexibility in object manipulation by considering information about the object's shape, the robot's capabilities, and environmental constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If grasp determination and movement planning are decoupled into separate steps, then the planning process can be simplified and implemented more easily, but the robotic device may fail to achieve the desired grasp and require repeated attempts
Solution Approach 1:
The patent combines grasp determination and movement planning into a unified optimization process that simultaneously determines the grasp pose and the sequence of robot configurations. This integration ensures that the planned movement trajectory is compatible with the desired grasp, eliminating the need for repeated attempts and improving grasp execution reliability while maintaining computational tractability through structured optimization.
2Manufacturing precision
If a specific grasp is selected based on object shape and robot capabilities, then the manipulation task can be performed with precision, but the system becomes less flexible when encountering variations in object position or orientation
Solution Approach 1:
The patent employs dynamic grasp planning where the grasp pose and movement trajectory are optimized together based on the actual object position and orientation. The system adapts the grasp configuration dynamically during the planning phase to account for variations in object pose, ensuring both precision in achieving the desired grasp and adaptability to different object positions through a unified optimization framework that considers both geometric constraints and robot kinematics.
Data Source
AI summary
Computer-implemented methods and apparatus for manipulating an object using a robotic device are provided. The method includes associating a first grasp region of an object with an end effector of a robotic device, wherein the first grasp region includes a set of potential grasps achievable by the end effector of the robotic device. The method further includes determining, within the first grasp region, a grasp from among the set of potential grasps, wherein the grasp is determined based, at least in part, on information associated with a capability of the robotic device to perform the grasp, and instructing the robotic device to manipulate the object based on the grasp.