Determining predictable strike points on tossed objects: A 2D physics simulation approach

Catching flying objects is a significant challenge for humans and robots, particularly when the physical parameters of the objects are unknown. Humans often strike incoming objects to redirect them along a less energetic and more predictable path before attempting to catch them. This work aims to adapt this strategy for robotic grasping by determining predictable striking points on tossed objects. We have developed a computational approach using a 2D physics simulation engine to generate collision maps. These maps are generated by simulating object-manipulator interactions with friction and restitution coefficients. By analyzing the maps obtained from the simulations, we can identify segments of the objects that exhibit stable and predictable collision behavior. We employ an unsupervised clustering technique to classify each segment's predictability across different friction and restitution coefficients. This allows us to pinpoint segments with the highest likelihood of a favorable striking outcome, leading to successful catches. Through simulations, we have demonstrated the effectiveness of our method in altering the flight paths of tossed objects, showcasing its potential for enhancing efficiency in logistics and delivery systems. By equipping robots and drones with our algorithm, mid-air or on-the-move package interception can be achieved, reducing human intervention, and improving delivery times. Our approach has limitations: the 2D analysis may not capture real-world complexities, affecting accuracy, and it assumes knowledge of object geometry and materials. These assumptions may limit the generalizability and applicability of our method. Further research is needed to enhance the performance and applicability of our approach across diverse real-world scenarios.