Robot Movement Sequence Control for Multi-Pick Path Optimization
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Solution Overview
Problem
Existing robot movement sequence planning methods are inefficient, requiring significant skill and difficulty in programming, and often result in suboptimal performance, especially in multi-pick-single-place applications, where numerous alternative sequences exist but are not effectively utilized.
Innovation Solution
A method that predicts values of parameters such as time, speed, and energy consumption for alternative movement sequences, allowing for the selection and execution of optimal sequences by the robot, using techniques like neural networks and genetic algorithms to identify collision-free paths and optimize pick and place cycles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional robot movement sequence planning methods are used, then programming is complex and requires significant skill, but the system can still execute basic movement sequences
Solution Approach 1:
The system performs self-optimization of movement sequences by automatically predicting parameter values and selecting optimal sequences without requiring manual programming intervention. The robot controller autonomously evaluates alternative movement sequences and selects the optimal one based on predicted parameters, eliminating the need for complex manual programming while maintaining execution capability.
2Productivity
If traditional movement sequence planning is used, then basic operations can be performed, but robot performance and pick rate are suboptimal
Solution Approach 1:
The system performs preliminary prediction of parameter values for alternative movement sequences before execution. By predicting time, speed, and energy consumption values in advance, the controller can pre-select optimal movement sequences, avoiding suboptimal execution and improving overall productivity and pick rate.
Solution Approach 2:
The system dynamically selects movement sequences based on real-time prediction of parameter values. Instead of using fixed, pre-programmed sequences, the controller adapts by choosing from multiple alternative sequences based on predicted performance parameters, optimizing execution time and pick rate dynamically.
3Reliability
If alternative movement sequences are not evaluated, then programming is simpler, but collision risks increase and performance decreases
Solution Approach 1:
The system uses prediction of parameter values as feedback to evaluate alternative movement sequences. By predicting outcomes such as collision risks and performance metrics for each alternative sequence, the controller can reliably select collision-free paths while managing the complexity through automated evaluation rather than manual programming.
Data Source
AI summary
A method for controlling movement sequences of a robot, the method including predicting values of at least one parameter related to the execution of alternative movement sequences by the robot, where each movement sequence includes at least one movement segment associated with a handling location; selecting a movement sequence based on the predicted values of the at least one parameter; and executing the selected movement sequence by the robot. A control system for controlling movement sequences of a robot is also provided.