Object Isolation In A Random Environment Using Manipulation Primitives Approach

As autonomous robots become less task specific to able to handle a larger variety of task that it may come across in the world, its ability to isolate the environmental objects from the object of interest becomes an increasingly important ability to have. This is in comparison to the current literature interest which focuses on the isolation of the object from the environment due to its precise nature. This method often consists of two distinct actions which is the action to improve the robot’s perception followed by another to manipulate the object of interest. This however becomes a problem when considering it might be impossible or undesirable to manipulate the object of interest such as the case of cleaning up archeological finds or even rescue missions of earthquake victims. In cases such as those, the precise nature of the actuation becomes a hindrance due to the long duration needed in order to manipulation every single object in the field one object at a time. This research explores the idea of combining both actions to improve perception as well as action to manipulate the object into one single motion with the added goal of manipulating as many objects as possible in a single action with minimal/no effect towards the object of interest. This research proposes three novel algorithms on a robot to plot out the position for each unwanted objects and its destined position as well as its trajectory and then utilizes manipulation primitives (pushing motion) to move said object along the planned trajectory. The algorithm was demonstrated using a KUKA Youbot with a camera fitted above the workspace. Results from the experiment indicated that all proposed algorithms successfully reduced the number of manipulations per object up to 0.605 manipulations albeit with a small tradeoff in accuracy from 97.7% to 93% which translates to average of 0.85cm/actuation for MSMAPPS, 0.75cm/actuation for MSMAPOS and finally 0.27cm/actuation for BSMAPOS. These results indicate a relatively small displacement per actuation at 4.35% displacement per actuation, 3.75% displacement per actuation, and 1.35% displacement per actuation relative to the workspace respectively. As a conclusion, the proposed methods is shown to be significantly more efficient then current methods employed in the field of object isolation in exchange for a small reduction in performance in terms of accuracy of actuation

Efficient Object Isolation In Complex Environment Using Manipulation Primitive On A Vision Based Mobile 6DOF Robotic Arm

This paper explores the idea of manipulation aided- perception in the context of isolating an object of interest from other small objects of varying degree of clusterization in order to obtain high quality training images.The robot utilizes a novel algorithm to plot out the position for each noise objects and its destined position as well as its trajectory and then utilizes manipulation primitives (pushing motion) to move said object along the planned trajectory.The method was demonstrated using Vrep simulation software which simulated a Kuka YouBot fitted with a camera on the gripper.We evaluated our approach by simulating the robot manipulators in an experiment which successfully isolate the object of interest from noise objects with at a rate of 77.46% at an average of 0.56 manipulations per object compared to others at 1.76 manipulations subsequently speeding up the time taken for manipulation from 12.58 minutes to 2.6 minutes however suffers from a tradeoff in terms of accuracy when comparing the similar works to our proposed method

Object Isolation With Minimal Impact Towards The Object Of Interest In A Complex Environment Using Manipulation Primitives

It is common in the field of robotic manipulation to specifically target and precisely move, displace or manipulate the targeted object of interest. This however may not always be the best possible course of action as there are situations where it is not possible to manipulate the object of interest or is not in a condition to be manipulated. This research paper explores and subsequently proposes 3 object isolation technique for the purpose of isolating a targeted object of interest from the environment incontras to the standard utilization of object singulation technique. The goal was to develop an algorithm than can successfully isolate the object of interest from the environment via removing the environment without/with minimal impact towards the object of interest. Results from the experiment indicated that the proposed algorithms can successfully isolate the object of interest with minimal impact towards the object of interest scoring an average of 0.85cm/actuation for MSMAPPS, 0.75cm/actuation for MSMAPOS and finally 0.27cm/actuation for BSMAPOS. These results indicates a relatively small displacement per actuation at 4.35% displacement per actuation, 3.75% displacement per actuation, and 1.35% displacement per actuation relative to the workspace respectivel

Development and analysis of face recognition system on a mobile robot environment / Jit Shen Quah and Mariam Md Ghazaly

In today’s society, face recognition technology is widely used and applied in various fields such as biometric identification and security surveillance. However it is apparent that as technology advances, even more so in the direction of mobile robots such as a mobile security surveillance robot or a humanoid robot, the application of face recognition would need to transition from the traditional fixed position recognition to a mobile environment recognition as well. This research thus aimed at analyzing the performance of face recognition algorithm performance in a mobile environment as compared to a static environment. This is done via integrating a developed face recognition software onto a mobile robot in terms of image captured distance and in extension its accuracy during static and dynamic conditions. The results from this research shows that when there is an increase in mobile robot speed from 0 ~ 65% duty cycle there seem to be a reduction in performance in terms of range of capture of approximately 30% for both face recognition and face identification which is a clear reduction in performance. From the results as well, the optimum speed for the mobile robot to move to obtain optimum performance for both recognition and identification was found to be at 60% PWM with minimum neighbors and scaling factors both set to 1