Robot Gripping Priority Control for Irregular Conveyor Objects
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Solution Overview
Problem
Automated robot systems in manufacturing and packaging face inefficiencies due to irregularly positioned and timed objects on conveyor belts, leading to downtimes and unproductive phases, as they primarily prioritize objects based on one-dimensional position on the conveyor belt, lacking optimal coordination and energy management among multiple robots.
Innovation Solution
A method utilizing a machine learning algorithm to determine priority identifiers and assignments for robots, considering optimization criteria such as energy consumption and throughput, which coordinates the selection and gripping of objects across multiple robots, incorporating factors like object positions, robot capacity, and conveyor belt movement, using algorithms like monitored or reinforcement learning and instance-based learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If robots prioritize objects based on one-dimensional position on the conveyor belt, then the picking process is simple to implement, but downtimes and unproductive phases occur due to irregular object positions and timing
Solution Approach 1:
The patent transitions from one-dimensional position-based prioritization (conveyor belt position only) to multi-dimensional optimization by incorporating robot capacity, energy consumption, and coordinated assignment across multiple robots. This dimensional expansion enables the system to optimize picking sequences considering both spatial position and temporal coordination, thereby reducing robot downtimes while maintaining throughput.
Solution Approach 2:
The system dynamically adjusts robot assignments and picking sequences based on real-time robot capacity and conveyor belt situation. Instead of static position-based prioritization, the optimized process continuously adapts to varying object positions, robot states, and energy consumption levels, minimizing idle phases and maximizing productive operation time.
2Ease of operation
If multiple robots operate independently on the conveyor belt, then each robot can operate autonomously, but coordination inefficiency leads to suboptimal energy consumption and throughput
Solution Approach 1:
The patent merges the control logic of multiple independent robots into a coordinated system. By combining robot capacity information, energy consumption data, and real-time conveyor belt status into a unified optimization process, the system achieves efficient coordination while maintaining individual robot autonomy. This collaborative approach reduces overall energy consumption compared to independent operation.
Solution Approach 2:
The optimized process implements feedback mechanisms where robot capacity and energy consumption are continuously monitored and fed back into the assignment algorithm. This feedback loop enables dynamic adjustment of robot assignments and picking sequences, optimizing energy usage while maintaining autonomous operation capabilities.
3Productivity
If robots traverse longer paths to pick up irregularly positioned objects, then all objects can be picked up, but energy consumption increases and mechanical stress rises
Solution Approach 1:
The system performs preliminary analysis of the conveyor belt situation and object positions before robot movement. By pre-calculating optimized picking sequences and assignments based on predicted object positions and robot capacities, the system minimizes unnecessary robot movements and energy consumption while ensuring complete object pickup.
Solution Approach 2:
The optimization process dynamically changes robot movement parameters (speed, path, timing) based on object positions, robot capacity, and energy consumption considerations. By adjusting these parameters in real-time, the system reduces energy loss and mechanical stress while maintaining complete object pickup.
Data Source
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AI summary
The invention relates to a method for optimizing an automated process for selecting and grasping an object by a robot in an arrangement of several robots with respect to a predefinable optimization criterion, wherein the potentially graspable objects occur irregularly with respect to their position in space and the time of their arrival, comprising the following steps: - Determining objects potentially graspable by the robots; - Determining a priority identifier and an assignment to one of the robots for each of the potentially graspable objects by means of a machine learning algorithm taking into account the optimization criterion; and - Selecting and grasping depending on the assignment and the priority identifier.