Redundant Robot Motion Planning for Real-Time Obstacle Avoidance
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Solution Overview
Problem
Existing motion planning techniques for robots with redundant degrees of freedom are limited in flexibility, unable to handle dynamic obstacles, and require artificial constraints, making them inefficient for real-time applications and line tracking tasks.
Innovation Solution
A dynamic motion planning system that formulates motion planning as a quadratic programming optimization calculation with a multi-component objective function and a collision avoidance constraint, allowing for real-time computation and adaptive weighting factors based on obstacle proximity to ensure efficient path deviation minimization and obstacle avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional motion planning techniques with artificial constraints are used, then computation can be performed analytically, but robot flexibility is reduced and extra programming steps are required
Solution Approach 1:
The patent replaces traditional mechanical constraint-based motion planning with a computational optimization approach. Instead of using artificial mechanical constraints like elbow joint planes to simplify calculations, the system uses quadratic programming optimization that naturally handles redundant degrees of freedom without additional constraints, thereby maintaining robot flexibility while achieving computational efficiency.
Solution Approach 2:
The patent changes the fundamental parameters of motion planning by transitioning from analytical inverse kinematics with fixed constraints to a dynamic optimization framework. The objective function parameters and weighting factors can be adjusted in real-time based on task requirements and obstacle conditions, allowing the system to adapt to different scenarios without reprogramming or artificial constraints.
2Adaptability or versatility
If optimization computation is used for motion planning, then robot flexibility is maintained, but computation time increases and real-time performance is compromised
Solution Approach 1:
The patent implements a dynamic optimization approach where the objective function and weighting factors are updated in real-time based on current robot state and obstacle conditions. This dynamic adaptation allows the system to maintain optimal performance across varying conditions while completing computations within each control cycle, achieving both flexibility and real-time performance.
Solution Approach 2:
The patent applies different weighting factors to different components of the objective function based on local conditions such as obstacle proximity and task priorities. By locally optimizing different aspects of motion (e.g., path tracking accuracy vs. obstacle avoidance) based on current conditions, the system achieves efficient real-time computation while maintaining overall robot flexibility.
3Reliability
If static obstacle avoidance is used, then pre-planned motions can avoid permanent objects, but dynamic obstacles moving through workspace cannot be handled
Solution Approach 1:
The patent combines preliminary action with real-time adaptation by pre-defining the optimization framework and constraint structures, then populating them with current obstacle data at each control cycle. This allows the system to maintain reliable collision avoidance for static obstacles while simultaneously adapting to dynamic obstacles through real-time sensor feedback and recomputation.
Solution Approach 2:
The patent uses continuous feedback from sensors to detect dynamic obstacles and update the optimization computation in real-time. This feedback mechanism allows the system to maintain reliable collision avoidance by constantly monitoring the workspace and adjusting motion plans to avoid both static and dynamic obstacles, rather than relying solely on pre-planned paths.
Data Source
AI summary
A method and system for motion planning for robots with a redundant degree of freedom. The technique computes a collision avoidance motion plan for a robot with a redundant degree of freedom, without artificially constraining the extra degree of freedom. The motion planning is formulated as a quadratic programming optimization calculation having a multi-component objective function and a collision avoidance constraint function. The formulation is efficient enough to compute the motion plan in real time at every robot control cycle. The collision avoidance constraint ensures clearance of all parts of the robot from both static and dynamic obstacles. Objective function terms include minimizing path deviation, joint velocity regularization and robot configuration or pose regularization. Weighting factors on the terms of the objective function are changeable for each control cycle calculation based on obstacle proximity conditions at the time.