Mechanical Arm Path Planning With Virtual Collision Simulation
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Solution Overview
Problem
Traditional processing path planning for mechanical arms in electronic manufacturing is often suboptimal, leading to increased break time and time costs due to manual programming and reliance on human experience, and results in inefficient paths that avoid collisions by adding unnecessary auxiliary points, which are not the shortest.
Innovation Solution
A processing path planning simulation device and method that utilizes a virtual environment to simulate collision tests and employs an ant colony algorithm to generate an optimal path by dividing candidate poses into groups, optimizing them, and generating an optimal processing path using an ant colony algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual programming and experience-based path planning are used, then the mechanical arm can perform processing tasks, but the processing path is suboptimal and collision risk increases
Solution Approach 1:
The patent performs collision detection and path optimization simulations in a virtual environment before actual execution. The system pre-calculates optimal paths and identifies potential collision points, allowing the mechanical arm to execute pre-validated paths that avoid collisions while maintaining efficiency.
Solution Approach 2:
The patent creates a virtual copy of the physical environment including the mechanical arm, obstacles, and processing targets. This virtual model allows for safe path planning and collision detection simulations without affecting actual production, enabling optimization of paths before real-world execution.
2Reliability
If auxiliary points are arranged to avoid obstacles, then collision avoidance is ensured, but the processing path becomes longer and less efficient
Solution Approach 1:
The patent dynamically adjusts the mechanical arm's path by calculating optimal trajectories that adapt to obstacle positions. Rather than using fixed auxiliary points, the system computes dynamic paths that maintain safe distances from obstacles while minimizing detours and processing time.
Solution Approach 2:
The patent changes the path planning parameters by optimizing the sequence of poses and transition points. The system adjusts path parameters such as pose sequences, transition speeds, and intermediate positions to achieve the shortest safe path, balancing collision avoidance with time efficiency.
3Reliability
If changeover is performed away from obstacles with increased height, then smooth movement and collision avoidance are ensured, but the processing path is not optimal
Solution Approach 1:
The patent pre-calculates optimal changeover positions and poses by simulating the mechanical arm's movement in the virtual environment. The system identifies the best locations for hand configuration changes that maintain smooth movement while minimizing distance from processing targets, rather than using fixed conservative positions.
Solution Approach 2:
The patent uses feedback from collision detection simulations to refine changeover paths. The system tests various changeover positions and poses, receives feedback on potential collisions or inefficiencies, and iteratively optimizes the changeover sequence to achieve smooth transitions with minimal impact on processing efficiency.
4Ease of manufacture
If traditional manual teaching and programming are used, then the mechanical arm can be programmed, but break time costs and time costs increase
Solution Approach 1:
The patent uses a virtual copy of the mechanical arm and environment to perform all path planning and programming simulations. This eliminates the need for manual teaching on the actual machine, as the virtual model can be programmed and tested independently, significantly reducing break time costs and allowing parallel development without occupying physical resources.
Solution Approach 2:
The patent performs all path planning, collision detection, and optimization in advance within the virtual environment before deploying to the physical system. This preliminary programming and validation eliminates the need for time-consuming on-site teaching and adjustment, reducing both break time costs and implementation time.
Data Source
AI summary
A processing path planning simulation device is provided, which includes a memory and a processor. The processor performs following operations: according to an obstacle model, multiple processing point positions, a mechanical arm model, a processing tool model, a model position relative relationship and a production strategy parameter, performing collision test simulation to generate multiple candidate poses of the mechanical arm model; performing path optimization algorithms on the multiple candidate poses to generate a pose sequence; performing an ant colony algorithm based on the pose sequence and the obstacle model to generate an optimal processing path; and based on the optimal processing path, simulating that an end point of the processing tool model on the mechanical arm model performs a virtual processing operation on the multiple processing point positions sequentially according to the multiple optimal nodes.