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Outline. Previous Accomplishments Last year's SURG Mapkin Proposal Concept Why is this useful? The MikroKopter platform Previous work Criteria For Success. Previous Accomplishments. http://www.youtube.com/watch?v=TkMKTtFGlxc&feature=player_embedded Quadrotor with AeroQuad base
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Outline • Previous Accomplishments • Last year's SURG • Mapkin • Proposal Concept • Why is this useful? • The MikroKopter platform • Previous work • Criteria For Success
Previous Accomplishments http://www.youtube.com/watch?v=TkMKTtFGlxc&feature=player_embedded Quadrotor with AeroQuad base • Flexible platform for aerial robotics research. • Implemented full automatic stabilization • Only inputs required are desired translation and rotation. • PID Tuned from AeroQuad base • Sonar based altitude hold • Controller capable of supporting other aerial platforms.
Quadrotor in action • Flown by remote control • "Stable mode" lets the quadrotor balance itself • "Acrobatic mode" is rate control for the quadrotor's movement.
Mapkin • Our Build 18 Project, first week of the semester • Ground vehicle • XBOX Kinect sensor • Map out obstacles in a 2D Grid • Future goal: SLAM in 2D to learn concepts to apply to Quadrotor
Proposal Concepts "Aerial Point-Cloud Generation using the Microsoft Kinect on an Autonomous Quadrotor" • Last SURG: outdoor, unstructured environments, control of the quadrotor and mapping. • Current proposal: • Constrained to indoor environments, tight obstacles to avoid • XBOX Kinect depth and color sensing • Point-Cloud generation in 3D (offboard) • More online computation for autonomous navigation • Goal: quadrotor navigates itself a short distance indoor, builds a point-cloud representation on a nearby computer
Why is this useful? • Feedback from robots needs to be useful for humans • Applications: • Exploring disaster zones • Exploring unreachable areas • Military • Navigation can be autonomous or RC • Point-clouds mean the robot's perspective could easily be understood by a human operator
The MikroKopter Platform • Quadrotor kit • Many parts of the navigation, etc are done (most are editable code) • Waypoint navigation provided • We'll need to add: • Microsoft Kinect sensor for vision • Any additional sensors (e.g. downward sonar) • BeagleBoard processor for onboard control
Previous Work • CMU Robotics Institute, including the Micro Air Vehicle Lab • University of Pennsylvania, "Aggressive Maneuvers" • We'd like our perception and obstacle detection onboard. • Our own last SURG • We're confident we can get a flying quadrotor quickly and move on to complex behaviors • Other mapping/SLAM projects • http://www.youtube.com/watch?v=D_nt5Qg1Nag
Criteria For Success • Successfully fly a Kinect around inside using RC • Navigate inside autonomously over a short distance • Produce a point-cloud map from Kinect imagery • Final Question: Is our product usable?