Robot Obstacle Avoidance via Joint Influence Value and Model Predictive Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Robots face challenges in navigating complex workspaces with various static or dynamic obstacles, leading to potential collisions and difficulties in completing tasks while avoiding obstacles effectively.
Innovation Solution
A method for determining the influence of obstacles on a robot's motion range, combined with model predictive control, to adjust joint angles and velocities, ensuring collision avoidance and efficient task completion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dynamic monitoring of distance based on visual equipment is used to predict collisions, then collision prediction capability is improved, but task completion efficiency deteriorates due to increased complexity and time consumption
Solution Approach 1:
The patent pre-calculates and stores the relationship between joint angles and end effector positions in a lookup table before operation. During actual task execution, the robot directly queries the pre-computed data rather than performing real-time calculations, significantly reducing computation time while maintaining accurate collision prediction capability
Solution Approach 2:
The system uses its own kinematic model and pre-stored transformation relationships to perform self-assessment of collision risk. By comparing the planned trajectory with obstacle positions using pre-computed joint angle mappings, the robot autonomously determines collision probability without requiring complex external sensing or real-time visual processing
2Reliability
If the robot adjusts motion to avoid obstacles, then collision avoidance capability is improved, but task completion time increases
Solution Approach 1:
The patent assesses collision probability and applies avoidance actions only when necessary. By using the pre-computed joint angle relationships to quickly evaluate risk, the system performs partial avoidance maneuvers only for high-probability collision scenarios, avoiding unnecessary motion adjustments that would waste time
Solution Approach 2:
The robot dynamically adjusts its motion trajectory based on real-time obstacle detection and the pre-stored kinematic model. When obstacles are detected, the system uses the lookup table to rapidly compute alternative joint angle configurations that avoid collision while minimizing deviation from the original task trajectory, thus reducing time loss
3Manufacturing precision
If complex real-time calculation of joint angles is performed during operation, then motion precision is improved, but computational speed deteriorates
Solution Approach 1:
The patent pre-calculates the transformation relationships between joint angles and end effector positions for all possible joint configurations and stores them in a lookup table. During operation, the system simply queries this pre-computed data structure, reducing complex real-time calculations to simple table lookups that maintain precision while dramatically improving computational speed
Solution Approach 2:
Instead of performing complex forward kinematics calculations in real-time, the system creates a simplified copy of the kinematic relationships in the form of a lookup table. This copied representation allows rapid querying of joint angle to position mappings without repeating the full calculation process, achieving both speed and precision
Data Source
AI summary
A robot obstacle avoidance method, a robot controller using the same, and a storage medium are provided. The method includes: determining an influence value of an obstacle on a motion range of a joint of the robot according to a position of the obstacle in a workspace of the robot; establishing a state transition relationship of the robot by taking a joint velocity of the robot as a control target and a joint angular velocity of the robot as a control input quantity; and avoiding the robot from colliding with the obstacle during a movement process of the robot by performing a model predictive control on the robot according to the state transition relationship and the influence value. In the present disclosure, the influence of the obstacle on the motion range of the joint of the robot is fully considered.