Robotics for Manufacturing A Michigan Robotics Focus Area: Contributing Faculty: Kira Barton...
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Transcript of Robotics for Manufacturing A Michigan Robotics Focus Area: Contributing Faculty: Kira Barton...
Robotics for Manufacturing
A Michigan Robotics Focus Area:
Contributing Faculty:Kira Barton
Chinedum OkwudireKazuhiro SaitouDawn M. Tilbury
A. Galip Ulsoy
Manufacturing research has long been a strength at the University of Michigan, and robotics is already being widely used in automotive and other manufacturing plants. Continuing research is needed to expand the role of robotics in manufacturing applications by improving robot capabilities and safety and reducing cost and energy consumption.
Introduction
There are approximately 1.5 million industrial robots in operation today world-wide (e.g., approx. 300,000 in Japan, 200,000 in North America, 125,000 in S. Korea)They perform tasks such as welding, spray paining and assembly in a variety of industries.They are typically preprogrammed to repeat the same task, are among the most reliable machines available, and are operated in isolation from humans for safety. Automated Guided Vehicles (AGVs) are also widely used in manufacturing plants for material handling.
Industrial Robots
Automated Guided Vehicles (AGVs)
Research Needs
Safety research is needed to enable operation with and around humans.Cost reduction will enable use for smaller volume production and by small companies.Energy consumption is high, and needs to be reduced.Dexterity and precision must be improved for many assembly tasks.Coordination among robots and with other automated machines.Flexibility and Autonomy are important to move beyond mass production.
Kiva Systems: Flexible Material Handling
Rethink Robotics: Low-Cost Assembly Robot Baxter
Precision Motion Control for High-Speed, High-Resolution Manufacturing (Barton)
Goal: Design advanced sensing and controls algorithms for high precision motion control
Iterative Learning ControlFlexible learning strategiesRobust learning for a range of
applicationsCooperative learning control strategies
Advanced Sensing StrategiesHigh-resolution sensing techniquesAtomic force microscopy for
topographical and charge density sensing
Vision-based detection
ApplicationsEmerging manufacturing processesHigh-resolution, high-speed
manufacturing systemsRehabilitation roboticsUAVs and other autonomous systems Fig. 2: ILC process. As the number of iterations
increases, the feedforward time domain control signal is determined and the error signal is minimized.
Fig. 1: Cooperative learning strategies. Develop cooperative learning control techniques to enable efficient and effective surveillance and monitoring
operations.
Design and Control of Cartesian Robots (Feed Drives) for Improved Performance and Energy Efficiency (Okwudire)
Goal: Improve performance and energy efficiency of feed drives
Dynamically adaptive feed drivesDesign feed drives such that dynamic properties change based on manufacturing operation
Integrally design time-varying controllers to ensure stability and performance under various dynamic configurations
Determine optimal dynamic configurations/controllers to ensure desired performance at minimal energy consumption
Example: Hybrid feed driveFeed drive is driven by linear and/or rotary motors depending on manuf. operation
Moving mass, drive point, sensing location, etc. change dynamically
Up to 25% improvement in energy-efficiency anticipated
Guideway Motor
TableScrew
(smooth shaft)
Linear MotorEngagement/
disengagement mechanism
(located under table)
Pneumatic pistons
Toggle arms
Disengagable Roh’lix nut
Dynamically Adaptive Hybrid
Feed Drive
Improving Energy Efficiency by Multi-Robot Coordination and Task Scheduling (Saitou)
Goal: Reduce total energy consumption and peak energy demands in multi-robot cells
Arm posture optimization to minimize idle time energy consumption
Multi-robot coordination to maximize the use of regenerative energy from one robot in other robots
Task scheduling to reduce the need of rapid acceleration
Task scheduling to spread energy peaks in multiple robots across cycle time Power profile of typical operation exhibiting
multiple energy peaks during cycle time (Duflou, et al, 2012)
Virtual Fusion for Robotic System Design, Evaluation and Monitoring (Tilbury)
Goal: Use high-fidelity simulation models running in parallel with physical system to evaluate new system designs
Validate performance by integration of simulated systems with physical systems
Quickly evaluate multiple scenarios for reconfigurability
Operator training with high-fidelity models
Run high-fidelity simulation models in parallel with physical system for on-line monitoring
Seamlessly swap a virtual robot for real : Identical controls and
networking interface
DeviceNet/ Ethernet
Evaluate new robotic concepts (e.g. Motoman for material handling +
assembly)
Design for Improved Reliability and Efficiency (Ulsoy)
Goal: Design robots to be energy efficient, reliable and safe
Passive-Assist DesignFor each joint motor, design parallel and/or series spring for passive assist
Typical trajectory and load
Optimize spring design
Single-Link ManipulatorExperiments w/ and w/o spring
Properly designed torsional spring reduces max. torque by 50% and energy consumption by up to 25%
Planned ExtensionsRobust design for family of trajectories and loads
Multi-link robot arm
Co-design of robot and controller
Experimental setup: (a) Quanser DC motor and controller, (b) worm gear transmission, and (c) single link arm
.
(a)
(b)
(c)
0 5 10 15 20 25 300
0.05
0.1
0.15
0.2Ele
ctric
al Po
wer [
W]
Time, t [s]
Model - No SpringModel - With SpringExperiment - No SpringExperiment - With Spring