Adaptive Bin-Picking Grasp Planning for Direct Part Placement
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Solution Overview
Problem
Existing industrial robot systems for pick and place operations often require multiple grasping and repositioning steps due to hard-coded logic, leading to inefficient motion sequences and increased travel distances, especially when parts are in random poses and orientations, and may miss opportunities for single-motion placement.
Innovation Solution
An adaptive robot grasp and motion planning technique that analyzes workpiece shapes to identify robust grasp options and stable intermediate poses, formulating a search problem to evaluate feasibility and optimality of motion paths while considering collision avoidance and joint motion constraints, allowing for direct or minimized motion to a final pose.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hard-coded logic is used to select preferred gripping surfaces, then the robot can reliably grasp parts, but it may miss opportunities for single-motion placement and require unnecessary intermediate poses
Solution Approach 1:
The system dynamically adapts the grasp planning based on the actual part pose and environment. Instead of following fixed hard-coded sequences, the planner evaluates multiple feasible grasps and selects the optimal one that enables direct placement when possible, or determines the minimal intermediate poses required. This dynamic adaptation resolves the contradiction by maintaining reliability through systematic evaluation while improving productivity by eliminating unnecessary motions.
Solution Approach 2:
The system changes the approach parameters by considering multiple grasp orientations and positions rather than being constrained to a single preferred surface. By evaluating different grasp configurations and their corresponding placement feasibility, the system can switch between single-motion and multi-motion strategies based on real-time conditions, thus resolving the contradiction between reliable grasping and efficient placement.
2Adaptability or versatility
If the robot uses pre-defined alternate gripping surfaces with intermediate poses, then it can handle inaccessible preferred surfaces, but the robot travel distance and joint motions increase
Solution Approach 1:
The system dynamically determines whether intermediate poses are necessary by evaluating the accessibility of preferred surfaces and the feasibility of direct placement. When the preferred surface is inaccessible, the planner identifies alternative grasps that may still enable direct placement or minimizes the number of intermediate poses required. This dynamic decision-making reduces unnecessary travel distance while maintaining adaptability to handle various part configurations.
Solution Approach 2:
The system applies different grasp strategies locally based on the specific part geometry and pose. Instead of universally applying pre-defined alternate gripping surfaces, the planner evaluates each part's characteristics and selects grasps that are locally optimal for achieving direct placement. This localized approach reduces overall robot travel distance by avoiding unnecessary intermediate poses for parts that can be placed directly.
3Productivity
If the robot evaluates all feasible grasps and optimizes motion paths, then it can minimize travel distance and regrasping, but the computational complexity increases
Solution Approach 1:
The grasp planning process is segmented into distinct evaluation stages: identifying feasible grasps, evaluating direct placement possibility, and determining intermediate poses if needed. This segmentation allows the system to systematically evaluate options without being overwhelmed by computational complexity, as each stage builds on the previous one and can be pruned early if direct placement is feasible.
Solution Approach 2:
The system performs preliminary evaluation of direct placement feasibility before committing to multi-motion sequences. By first checking whether a single-motion placement is possible for each feasible grasp, the system avoids unnecessary computational effort in planning intermediate poses and regrasping sequences. This preliminary action resolves the contradiction by enabling efficient optimization only when truly necessary.
Data Source
AI summary
An adaptive robot grasp planning technique for bin picking. Workpieces in a bin having random positions and poses are to be grasped by a robot and placed in a goal position and pose. The workpiece shape is analyzed to identify a plurality of robust grasp options, each grasp option having a position and orientation. The workpiece shape is also analyzed to determine a plurality of stable intermediate poses. Each individual workpiece in the bin is evaluated to identity a set of feasible grasps, and the workpiece is moved to the goal pose if such direct movement is possible. If direct movement is not possible, a search problem is formulated, where each stable intermediate pose is a node. The search problem is solved by evaluating the feasibility and optimality of each link between nodes. Feasibility of each link is evaluated in terms of collision avoidance constraints and robot joint motion constraints.