Robot Arm Trajectory Generation Under Collision and Torque Constraints
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for generating robot arm trajectories in manufacturing systems are inefficient, often requiring manual trial and error to avoid collisions and optimize motion speed, leading to increased operational costs and suboptimal cycle times due to fixed numbers of intermediate command values, which fail to satisfy constraints on motor torque and other motion parameters.
Innovation Solution
A trajectory generating method and apparatus that iteratively generate, evaluate, and optimize multiple trajectories between start and target teaching points, using evaluation values to select and update trajectories, ensuring avoidance of obstacles and optimal operation times while satisfying physical constraints such as joint torque and motion speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual trial and error method is used to prepare teaching points, then the robot arm can avoid obstacles and operate within safe zones, but the cycle time is delayed and operational costs increase
Solution Approach 1:
The patent replaces manual mechanical trial-and-error teaching methods with an automated computer-based simulation system. The simulation apparatus generates and evaluates multiple trajectories automatically, substituting human operators with computational algorithms that can process and optimize robot paths without physical trial runs, thereby reducing both time and cost while maintaining collision avoidance reliability
Solution Approach 2:
The patent performs preliminary trajectory evaluation and optimization in a virtual simulation environment before actual robot execution. By pre-calculating and validating multiple candidate trajectories in silico, the system identifies optimal paths that avoid obstacles and minimize cycle time, eliminating the need for time-consuming on-site trial and error adjustments
2Device complexity
If fixed number of intermediate command values is used, then the trajectory generation is simplified, but the operation time cannot be optimized and constraints on motor torque and motion parameters cannot be satisfied
Solution Approach 1:
The patent implements dynamic trajectory evaluation where the number and distribution of intermediate command values are not fixed but adaptively determined based on trajectory quality metrics. The simulation system evaluates multiple trajectories with varying numbers of intermediate points and selects those that optimize operation time while satisfying motor torque and motion parameter constraints, making the system flexible rather than rigid
Solution Approach 2:
The patent changes key parameters including the number of intermediate command values, their distribution along the path, and timing parameters to optimize trajectory performance. By systematically varying these parameters and evaluating resulting trajectories against constraints, the system finds optimal configurations that minimize operation time while ensuring motor torque and motion parameter requirements are met
3Reliability
If teaching points are prepared to ensure robot arm movability and non-interference, then collision avoidance is achieved, but optimal motion speed cannot be maintained and manual rework is required
Solution Approach 1:
The simulation apparatus performs self-evaluation of generated trajectories by automatically checking collision avoidance, motor torque constraints, and motion parameter requirements. The system independently identifies valid trajectories without requiring manual verification or iterative rework by operators, making the trajectory generation process autonomous and eliminating repetitive manual adjustment work
Solution Approach 2:
The patent implements a feedback loop where trajectories are generated, evaluated against multiple constraints including safety and performance criteria, and refined based on evaluation results. This automated feedback mechanism continuously improves trajectory quality by identifying and correcting issues such as collisions or constraint violations without human intervention, maintaining both safety and optimal performance
Data Source
AI summary
A trajectory generating method includes a first generating process of generating a plurality of trajectories between a start teaching point and a target teaching point, an evaluation process of evaluating a motion of the robot arm on each trajectory to calculate an evaluation value of each trajectory, a selection process of selecting one of the plurality of trajectories based on calculated evaluation values, and an update process of updating the trajectory by repeating the processes of generating a plurality of new trajectories by changing a selected trajectory in the selection process, of calculating an evaluation value of a motion of the robot arm on each changed trajectory and of selecting a trajectory based on calculated evaluation values.