This paper presents a robotic system Chitrakar which autonomously converts any image of a human face to a recognizable non-self-intersecting loop (Jordan Curve) and draws it on any planar surface. The image is processed using Mask R-CNN for instance segmentation, Laplacian of Gaussian (LoG) for feature enhancement and intensity-based probabilistic stippling for the image to points conversion. These points are treated as a destination for a travelling salesman and are connected with an optimal path which is calculated heuristically by minimizing the total distance to be travelled. This path is converted to a Jordan Curve in feasible time by removing intersections using a combination of image processing, 2-opt, and Bresenham’s Algorithm. The robotic system generates n instances of each image for human aesthetic judgement, out of which the most appealing instance is selected for the final drawing. The drawing is executed carefully by the robot’s arm using trapezoidal velocity profiles for jerk-free and fast motion. The drawing, with a decent resolution, can be completed in less than 30 minutes which is impossible to do by hand. This work demonstrates the use of robotics to augment humans in executing difficult craft-work instead of replacing them altogether.
State of the art automated bin packing systems fail to handle dynamic scenarios in which information on dimensions of objects to be loaded is not available in advance. These systems also fail to consider capabilities of robots used for the automated packing of objects/bins. The disclosure herein generally relates to automated bin packing, and, more particularly, to a system and method for autonomous multi-bin parcel loading system. The system handles an online object packing in which information on dimensions of objects to be loaded is not available in advance. The system is also configured to consider capabilities of one or more robots used for loading objects to containers, while generating recommendations for object packing.
We present a path planning technique inspired by the bug algorithm to quickly compute paths in an obstacle rich environment (or to report that no such path exists). In our approach, we simulate virtual bugs that upon sensing an obstacle splits into two bugs exploring the obstacle boundary in opposite directions, until the bugs find the goal in the line-ofsight. Then the bug leaves the obstacle and proceeds towards the goal. The process of splitting a bug into two continues until all the bugs reach the goal. The algorithm is simple to implement and it rapidly finds a solution if one exists. We provide worst case bounds on the path length with provable guarantees on convergence and develop heuristics to minimize the number of active bugs in the environment. We compare the performance of our algorithm with different planners from Open Motion Planning Library (OMPL) and visibility graph methods. The results show that the proposed algorithm delivers lower cost paths compared to other planners with lower computational time and rapidly indicates if a path does not exist.