New computer vision based Snakes and Ladders algorithm for the safe trajectory of two axis CNC machines

Multi-axis machine production process optimization, automation and intelligence are the key codes of today’s scientific community. Rapid decision and intelligence are becoming more important for precise and safe virtual and real production. Multi-axis CNC production is a high speed machining process that demands less human intervention and high intelligence, to deal with any uncomfortable situation regarding collisions. Current CAM softwares as well as CNC machines are able to detect collisions but are unable to avoid these collisions automatically. This paper aims to make the CAD-CAM/CNC multi-axis safe trajectory generation process optimal, intelligent and automatic, using vision based image processing by the Snakes and Ladders game analogy. Applying the Snakes and Ladders analogy on machine virtual scene (trajectory preparation) and real scene (during production) images gives promising methodology for safe and efficient trajectory generation while avoiding collisions named Snakes and Ladders Analogy for Production Trajectory (SLAPT). Our Rectangular Enveloped Safe and Efficient Trajectory (RESET) algorithm, based on the same principle of the SLAPT methodology is also discussed in this paper. Results include some applications of algorithms on virtual and real machine scene images for the safe and optimized trajectory of tools. This paper focuses on intelligence and optimization of 2D non-functional transversal trajectories of 2-axis machines for production and preparation processes as an initial effort towards the complex safe trajectory generation process (mill-turn).

► The use of CAD model (virtual) and image processing (real setup) for the improvement of preparation and manufacturing processes.
► Intelligent trajectory decision process for collision avoidance.
► Safe and efficient trajectory generation by using vision based image processing.
► The application of Snakes and Ladders analogy to the CAD/CAM environment.