Soft Robot Motion Planning via State Candidate Evaluation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current robot control systems face difficulties in handling soft and deformable objects due to the nonlinearity of flexible robot movements, making it challenging to model and generate accurate motions, especially for robots with pneumatic components that deform during operation.
Innovation Solution
A robot control system that includes a state candidate generation unit, a control amount estimation unit, a state candidate evaluation unit, and a selection unit, which uses a coincidence degree index to evaluate and select state candidates, reducing the number of modeling processes and improving motion planning accuracy by considering both distance and coincidence degree between planned and executed states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional motion planning methods are used for soft robots, then the robot can perform simple reaching motions, but the robot cannot accurately generate complex motions due to nonlinearity and deformation
Solution Approach 1:
The motion planning process is segmented into distinct functional units: state candidate generation, control amount estimation, state candidate evaluation, and selection. This segmentation allows each unit to handle specific aspects of the nonlinear soft robot dynamics independently, improving accuracy without requiring a complete complex model of the entire system.
Solution Approach 2:
The invention changes the approach from modeling physical parameters (mass, stiffness, damping) to using state-space parameters (position, velocity, acceleration). By representing soft robot dynamics in terms of state transitions rather than physical properties, the system achieves high accuracy in motion generation while avoiding the complexity of modeling material nonlinearity and deformation.
2Measurement precision
If detailed dynamic models are created to improve motion accuracy, then complex motions can be generated, but the number of modeling processes increases significantly
Solution Approach 1:
Instead of creating detailed dynamic models through complex modeling processes, the invention uses state transition data that copies the essential behavioral patterns of the soft robot. The state candidate generation unit creates virtual state transitions that replicate actual robot behavior, achieving accurate motion generation without time-consuming detailed modeling.
Solution Approach 2:
The system performs preliminary state candidate generation and evaluation before actual motion execution. By pre-computing multiple state candidates and their evaluation values, the system avoids the need for complex real-time modeling during motion generation, significantly reducing the time required for motion planning while maintaining high accuracy.
3Ease of manufacture
If simple motion models are used, then modeling is easier and faster, but the robot cannot generate accurate motions other than simple reaching motions
Solution Approach 1:
The invention implements a dynamic motion planning approach where state candidates are generated and evaluated based on current robot state and target state. The system adapts to different motion types (reaching, grasping, manipulation) by dynamically adjusting state transitions and evaluation criteria, enabling versatile motion generation while keeping the underlying model simple and easy to implement.
4Measurement precision
If multiple modeling processes are used to capture soft robot nonlinearity, then motion accuracy improves, but the system complexity and difficulty of identification increase
Solution Approach 1:
The invention extracts only the essential dynamic characteristics of soft robots needed for motion planning, separating them from unnecessary complex modeling details. By focusing solely on state transition relationships (position, velocity, acceleration) rather than complete physical models, the system achieves accurate dynamics identification with minimal modeling complexity.
Data Source
AI summary
A robot control system includes a state candidate generation unit that generates a state candidate that is a state transition destination of a robot at next time, a control amount estimation unit that estimates a control amount for transitioning to the state candidate, a state candidate evaluation unit that calculates a distance between the target state of the robot and the state candidate, calculates a coincidence degree between (i) a state at next time estimated from a state at current time of the robot and the control amount and (ii) the state candidate, and sets a sum of the distance and the coincidence degree to be an evaluation value, and a selection unit that selects a state candidate with a minimum evaluation value from state candidates and generate a motion corresponding to the selected state candidate.