Fast scene analysis using vision and artificial intelligence for object prehension by an assistive robot

The results obtained in this paper have shown that scene analysis for robotic object prehension in cooperation with a user can be performed with effective promptness. Indeed, the system requires on average 0.6 s to analyze an object in a scene. With the JACO robotic assistance arm, the system can pick up a requested object in 15 s while moving at 50 mm/s, which may be greatly improved upon using a faster robot. The system performance averages 83% accuracy for object recognition and is able to use a decision tree to select a simple approach path for the robot end-effector towards a desired object. This system, in combination with an assistive robot, has great potential for providing users suffering from musculoskeletal disorders with improved autonomy and independence, and for encouraging sustained usage of this type of technical aids.