Human-Machine Interface for a Smart Wheelchair

The paper describes the integration of hardware and software with sensor technology and computer processing to develop the next generation intelligent wheelchair. The focus is a computer cluster design to test high performance computing for smart wheelchair operation and human interaction. The LabVIEW cluster is developed for real-time autonomous path planning and sensor data processing. Four small form factor computers are connected over a Gigabit Ethernet local area network to form the computer cluster. Autonomous programs are distributed across the cluster for increased task parallelism to improve processing time performance. The distributed programs operating frequency for path planning and motion control is 50Hz and 12.3Hz for 0.3 megapixel robot vision system. To monitor the operation and control of the distributed LabVIEW code, network automation is integrated into the cluster software along with a performance monitor. A link between the computer motion control program and the wheelchair joystick control of the drive train is developed for the computer control interface. A perception sensor array and control circuitry is integrated with the computer system to detect and respond to the wheelchair environment. Multiple cameras are used for image processing and scanning laser rangefinder sensors for obstacle avoidance in the cluster program. A centralized power system is integrated to power the smart wheelchair along with the cluster and sensor feedback system. The on board computer system is evaluated for cluster processing performance for the smart wheelchair, incorporating camera machine vision and LiDAR perception for terrain obstacle detection, operating in urban scenarios.

[1]  Joelle Pineau,et al.  Automatically characterizing driving activities onboard smart wheelchairs from accelerometer data , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[2]  R. Simpson Smart wheelchairs: A literature review. , 2005, Journal of rehabilitation research and development.

[3]  Guan-Wei Chen,et al.  Development of a smart wheelchair with dual functions: Real-time control and automated guide , 2017, 2017 2nd International Conference on Control and Robotics Engineering (ICCRE).

[4]  R.C. Simpson,et al.  The Hephaestus Smart Wheelchair system , 2002, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[5]  Fitri Utaminingrum,et al.  Development of computer vision based obstacle detection and human tracking on smart wheelchair for disabled patient , 2017, 2017 5th International Symposium on Computational and Business Intelligence (ISCBI).

[6]  Yiannis Demiris,et al.  Learning assistance by demonstration , 2015, J. Hum. Robot Interact..

[7]  C. T. Lin,et al.  Indoor and Outdoor Mobility for an Intelligent Autonomous Wheelchair , 2012, ICCHP.

[8]  S. S. Mantha,et al.  Advances in smart wheelchair technology , 2017, 2017 International Conference on Nascent Technologies in Engineering (ICNTE).

[9]  Yakov Rekhter,et al.  Address Allocation for Private Internets , 1994, RFC.

[10]  Guangming Xiong,et al.  VPH+: An Enhanced Vector Polar Histogram Method for Mobile Robot Obstacle Avoidance , 2007, 2007 International Conference on Mechatronics and Automation.

[11]  L Fehr,et al.  Adequacy of power wheelchair control interfaces for persons with severe disabilities: a clinical survey. , 2000, Journal of rehabilitation research and development.

[12]  Hung Manh La,et al.  A Comprehensive Review of Smart Wheelchairs: Past, Present, and Future , 2017, IEEE Transactions on Human-Machine Systems.

[13]  Po-Jen Wang,et al.  Radial polar histogram: obstacle avoidance and path planning for robotic cognition and motion control , 2011, Electronic Imaging.

[14]  Siddhartha S. Srinivasa,et al.  A Decision-Theoretic Approach for the Collaborative Control of a Smart Wheelchair , 2018, Int. J. Soc. Robotics.

[15]  Wyatt S. Newman,et al.  An evaluation of low-cost sensors for smart wheelchairs , 2013, 2013 IEEE International Conference on Automation Science and Engineering (CASE).

[16]  Shafaque Anjum Mohd Shakir Sheikh,et al.  An evolutionary approach for smart wheelchair system , 2015, 2015 International Conference on Communications and Signal Processing (ICCSP).

[17]  Roland Siegwart,et al.  Introduction to Autonomous Mobile Robots , 2004 .

[18]  C. T. Lin,et al.  Design and development of an autonomous robotic wheelchair for medical mobility , 2018, 2018 International Symposium on Medical Robotics (ISMR).

[19]  Fitri Utaminingrum,et al.  A laser-vision based obstacle detection and distance estimation for smart wheelchair navigation , 2016, 2016 IEEE International Conference on Signal and Image Processing (ICSIP).

[20]  Hong Wang,et al.  VPH: a new laser radar based obstacle avoidance method for intelligent mobile robots , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).