CNIT 581 - Software Design and Development for Robotics

This is a blog for the course CNIT 581 Software Design and Development in Robotics taught by Prof. Byung-Cheol Min at Purdue University for Spring 2020.

Project Name:- DESKBOT

The aim of this project is to develop a robotic system to arrange/unclutter the average office table environment.

Key Components of the project:

Robot Motion planning and execution
Machine Vision to observe all objects in the environment.

Team Members:-

Hitesh V Gokaraju
Vishnunandan LN Venkatesh

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DESKBOT Update 7 - Final Update !

Final Overview & Results The DeskBot system was setup up on an experimental desk testbed. The Intel Real sense camera was mounted directly above the center of the table at a distance of 30 inches. The right corner of the table is the robots initial resting location. Three major modules were used as explained in the updates previously, Scene Segmentation and Object Detection Robot Path Planning and Object Manipulation Coverage Path Planning The results pertaining to each of these modules are detailed below, Scene Segmentation and Object Detection To help the DeskBot system perceive and observe its environment YOLO object detector was implemented to classify 5 classes (pencil, erasers/rubbers, pens, staplers, remotes). YOLO was successfully implemented with an accuracy of 90%. A dataset was also created an annotated for the same calss of objects for training. As a future work more desk/workspace objects could be trained to be seamlessly declu...

DESKBOT Update 4

The Hamster Robot and Coverage Path Planning: Hamster Robot: The Hamster robot is a small and lovely robot for software education. It includes various devices as shown in the following figures. The Hamster robot can be programmed and controlled over various languages such as Python, C, Processing IDE etc. For our implementation, we plan to program the robot using python. The Hamster robot will be used in three ways: For locomotion i.e. to push objects to their slots during cleaning and moving during Coverage Path Planning (CPP). This will require control of the DC motors. For detecting the edge of the workspace (desk in our case). It is crucial that during robot locomotion we do not fall off the desk. As a safety measure for this purpose the Infrared Floor Sensors will be constantly polled to look for the edge of the workspace to stop the robot. For detecting, if there exists a contact between the robot and the object. The proximity sensors will be used and wil...

DESKBOT UPDATE 3

Scene Segmentation and Object Detection The robot has to know its environment before taking an action, a sensor is required is to perceive the environment and know what things exist. In our case, we use a 2D camera to know where and how the objects and the robots are positioned. We use Mask R-CNN to perform instance segmentation and object detection or use YOLO for object detection. Mask R-CNN is divided into two modules, first, it estimates the regions where the objects can exist on the input image. Second, based on the initial estimation it identifies the class of the object and generates a mask in the pixel level. In the initial step, the RPN (Residual Pooling Network) scans all FPN (Feature Pyramid Network) in a top-bottom approach and estimates where the objects exist on the input image. Once the estimation is done a bounding box is assigned to the anchor (anchors are a set of boxes with predefined locations). RPN helps in the anchor to decide where in the feature...

DESKBOT

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