Robot Motion Control Using Global and Local MPC Path Planning
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Solution Overview
Problem
Existing motion planning methods for robots fail to effectively generate an appropriate motion plan from a starting point to a goal point, leading to suboptimal motion control.
Innovation Solution
A control system utilizing first and second model predictive controls to generate first and second path data for global and local sections, respectively, determining manipulative variables based on these path data to improve motion control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a single model predictive control is used for global path planning, then the control system is simple, but the motion control precision is insufficient
Solution Approach 1:
The patent divides the global path planning into multiple local sections, each handled by a separate model predictive control unit. This segmentation allows each local controller to focus on specific path segments, improving motion control precision while maintaining manageable system complexity through modular architecture
Solution Approach 2:
The patent transitions from a single-dimensional global path approach to a multi-dimensional structure by introducing both global path planning and local section planning layers. This dimensional expansion enables more precise control at different scales simultaneously
2Manufacturing precision
If the global path is divided into many local sections, then the motion control precision is improved, but the computational complexity increases
Solution Approach 1:
The global path is segmented into multiple local sections, with each section processed by dedicated model predictive control units. This segmentation improves path planning precision by allowing detailed local optimization while distributing computational load across multiple independent units
Solution Approach 2:
Each local model predictive control unit processes only its assigned local section rather than the entire global path, performing partial action that collectively achieves complete path planning. This reduces individual computational complexity while maintaining overall precision
3Manufacturing precision
If a detailed local path plan is generated for each section, then the motion control is optimized, but the processing time increases
Solution Approach 1:
The path planning is segmented into independent local sections that can be processed in parallel. Each section's detailed optimization does not block others, reducing total processing time while maintaining motion control optimization through localized precision planning
Solution Approach 2:
Local sections are pre-divided and prepared for parallel processing. The system performs preliminary segmentation of the global path into manageable local sections before execution, enabling concurrent processing that reduces overall computation time while maintaining detailed optimization
Data Source
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AI summary
Disclosed herein is a technique for improving the motion control of a control target. A control system (1) performs motion control on a control target (such as a robot Rb1) from a starting point (S1) through a goal point (S2). The control system (1) includes a first path generator (11), a second path generator (12), and a control unit (13). The first path generator (11) outputs, using first model predictive control, first path data about a first target path (G1) for the control target with respect to a global path (A1) leading from the starting point (S1) to the goal point (S2). The first target path (G1) includes at least one passage point (P1). The second path generator (12) outputs, using second model predictive control, second path data based on the first path data. The second path data is data about a second target path (G2) for the control target with respect to each of a plurality of local sections (B1) defined by dividing the global path (A1) with respect to the at least one passage point (P1). The control unit (13) determines a manipulative variable with respect to the control target based on the second path data.