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

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(a)

(b)

(c)

0 5 10 15 20 25 300

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0.1

0.15

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ctric

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wer [

W]

Time, t [s]

Model - No SpringModel - With SpringExperiment - No SpringExperiment - With Spring