Game Strategy for APC 2016

History (APC 2015) Broad range of hardware: Several Baxters, Universal Robotics Arms, ABBs, PR2, Barrett Arms, custom built 3d printer style rigs and factory automation. There were scoops, hands, grippers, and suction. The teams used an assortment of technologies during the event: LIDAR lasers, three-fingered robotic arms, tape measures that function as a tongue-like grasper and several types of suction mechanisms.

Team RBO (Rank 1: 148 points) Platform: WAM Arm (Barrett Technology) + Mobile Base. End-Effecter: Suction. Successfully picked and dropped around 11 objects in ~20 minutes. Failed to grasp only 2 objects: (i) Meshed pencil cup, (ii) Probably a Cheese-it box (Failed while trying to grasp the object sideways, ended up dropping it).

Team MIT (Rank 2: 88 points) Platform: Industrial ABB 1600ID Arm. No Mobile Base. End-Effecter: Multiple options, very impressive. “Custom robot end-effector "fingers". Made from aviation-grade aluminum, which gives right compliance and endurancet. At the outer-most end of bottom finger tip is a spatula-like finger nail, can scoop objects from underneath, or grasp objects that are flush against a shelf wall. On the top finger, there is a suction.7 motion primitives: grasping, suction down, scooping, toppling, push-rotate, etc.” Did not drop/fail to pick any objects, used multiple grasping options defined, scooping objects from underneath effective. Only drawback seemed that the huge arm was not fast enough. For perception, statically mounted two Microsoft Kinect2 cameras to the left and right of the robot, and one Intel Realsense camera on the robot arm, close to our gripper. To classify and find the pose of objects, utilized a software package by a startup company - Capsen Robotics. Capsens Robotics' software receives pre-processed data from cameras and instructions on what objects to look for, it returns the position and orientation of the target objects.

Suction System Design

CAD Model – Simple Design + Easy to manufacture + Vertical compliance + Low profile - Must approach from top or side

CAD Model – Complex Design + Can press into objects + Might need this angle of approach for stowing task Complexity Size

System Components Pressure Transducer Air Compressor Vacuum Suction Cup 12V Solenoid Digital Digital Analog

Arduino + ROS

Vision Processing

2D Item Recognition Built 2D item recognition system using OpenCV Ground truth dataset from Berkeley and competition shelf images from Rutgers Feature detection and description using SIFT Feature matching using brute force matcher RANSAC to estimate homography

3D Recognition Build 3D recognition process using PCL Feature match 3D point clouds using FPFH (Fast Point Feature Histograms) Align frames using RANSAC to determine gripping position

PR2 Platform Analysis Robot cannot reach the top shelf (~1.86m) unless we build a ‘boost’ platform PR2 will probably need additional camera to see items in all shelf areas Two hands means we can have two unique gripping devices

Plan of Action

Deliverables for 10/29 Large scale 3D recognition test (Lekha) Extract point cloud from Kinect2 and use within ROS (Feroze) Compare feature recognition algorithms (Lekha) Refine gripper design (Rick) Trade study impeller vs. vacuum pump Spec all components, determine proper airflow and suction Finalized design, ready for review, by next progress review PR2 simulation (Feroze) Get arm to move from one point to another (Feroze) Get mobility base to move (Abhishek) Establish SMACH controller (Alex)