DESIGN, DEVELOPMENT, AND USABILITY EVALUATION OF CONTROL ALGORITHMS FOR A MOBILITY ENHANCEMENT ROBOTIC WHEELCHAIR (MEBOT)

An Electric Powered Wheelchair (EPW) is a key mobility device for people with disabilities providing mobility, independence, and improved quality of life. However, the design of current EPWs remains limited when driving in environments with architectural barriers and uneven terrain, making EPW users susceptible to safety issues - such as tipping or falling - which may lead to serious injury. To overcome these limitations, we developed a series of control algorithms for a novel mobility enhancement robotic wheelchair (MEBot). MEBot consists of six wheels with pneumatic actuators to control the elevation and inclination of the wheelchair as well as electric actuators in the driving wheel carriage to change its driving wheel configuration. Its controller is comprised of a single board computer, and a sensor package that aids obstacle detection and provides information about joint movements to develop MEBOT’s control algorithms. The ability of the MEBot controller to perform control algorithms, such as the dynamic seat leveling, curb climbing, and descending applications, was evaluated and validated in both simulation and a controlled environment for broader accessibility in architectural barriers. A stability analysis showed that while the footprint of the wheelchair changed during the process of its control algorithms when overcoming architectural barriers such as curbs and slopes; MEBot maintained its center of mass within the wheelchair footprint. Furthermore, a usability evaluation with ten power wheelchair users was conducted to compare the MEBot’s controller with that of their own power wheelchair in simulated indoor, outdoor, and advanced (architectural barriers) environments. Results show that MEBot was able to perform a significantly higher number of tasks than currently available commercial power wheelchairs in the advanced environment. In addition, participant’s feedback was obtained for further improvement of the device and its control algorithms

A non-holonomic, highly human-in-the-loop compatible, assistive mobile robotic platform guidance navigation and control strategy

The provision of assistive mobile robotics for empowering and providing independence to the infirm, disabled and elderly in society has been the subject of much research. The issue of providing navigation and control assistance to users, enabling them to drive their powered wheelchairs effectively, can be complex and wide-ranging; some users fatigue quickly and can find that they are unable to operate the controls safely, others may have brain injury re-sulting in periodic hand tremors, quadriplegics may use a straw-like switch in their mouth to provide a digital control signal. Advances in autonomous robotics have led to the development of smart wheelchair systems which have attempted to address these issues; however the autonomous approach has, ac-cording to research, not been successful; users reporting that they want to be active drivers and not passengers. Recent methodologies have been to use collaborative or shared control which aims to predict or anticipate the need for the system to take over control when some pre-decided threshold has been met, yet these approaches still take away control from the us-er. This removal of human supervision and control by an autonomous system makes the re-sponsibility for accidents seriously problematic. This thesis introduces a new human-in-the-loop control structure with real-time assistive lev-els. One of these levels offers improved dynamic modelling and three of these levels offer unique and novel real-time solutions for: collision avoidance, localisation and waypoint iden-tification, and assistive trajectory generation. This architecture and these assistive functions always allow the user to remain fully in control of any motion of the powered wheelchair, shown in a series of experiments

Usability evaluation of a Self-levelling robotic wheelchair for tip prevention in outdoor environments

Tips and falls are the most prominent causes of wheelchair accidents in the US when driving on uneven terrains and non-accessible environments. The Mobility Enhancement Robotic Wheelchair (MEBot) was designed to tackle these environmental challenges and address the mobility limitations of conventional electric-powered wheelchairs (EPW). MEBot offers a self-leveling application to maintain a stable seat in uneven terrains with the use of position sensors at each wheel and an attitude sensor to move each wheel accordingly. The self-leveling application can be enabled/disabled via a switch. The goal of the study was to perform a usability evaluation of MEBot’s self-leveling application in terms of the wheelchair’s performance and the participant’s perception. Ten participants were asked to drive their own EPW and MEBot through a driving course that simulated outdoor environmental obstacles for five times in each device. The wheelchair’s performance hypotheses included MEBot’s ability to be safe by maintaining a lower change in seat angle change than participants’ EPWs and MEBot’s self-leveling time would be within or lower than an average person’s walking speed. Additionally, it was hypothesized that participants would score better on the NASA-TLX and QUEST assessment tools for MEBot than their own EPW. Results showed that MEBot has lower angle change when going up and down a 10° slope; MEBot (5.6° ± 1.6°, 6.6° ± 0.5°) than their own wheelchair (14.6° ± 2.6°, 12.1° ± 2.6°) absolute deviation going up and down the slope, respectively. This contrasts with the participants’ EPWs when ascending and descending both slopes as MEBot required a longer time (7.8 ± 3.0 seconds) with a greater angle change when driving over an obstacle. The participant’s perception towards each EPW favored MEBot with respect to the NASA TLX and QUEST than their own wheelchair based upon the interpretation of the written feedback. The results demonstrated that the self-leveling application can work effectively but it is hindered by mechanical limitations. Future work will involve a redesign with electro-hydraulic actuators to mitigate this mechanical limitation and similar usability evaluation to evaluate MEBot improvements

DEVELOPMENT AND EVALUATION OF AN ADVANCED REAL-TIME ELECTRICAL POWERED WHEELCHAIR CONTROLLER

Advances in Electric Powered Wheelchairs (EPW) have improved mobility for people with disabilities as well as older adults, and have enhanced their integration into society. Some of the issues still present in EPW lie in the difficulties when encountering different types of terrain, and access to higher or low surfaces. To this end, an advanced real-time electrical powered wheelchair controller was developed. The controller was comprised of a hardware platform with sensors measuring the speed of the driving, caster wheels and the acceleration, with a single board computer for implementing the control algorithms in real-time, a multi-layer software architecture, and modular design. A model based real-time speed and traction controller was developed and validated by simulation. The controller was then evaluated via driving over four different surfaces at three specified speeds. Experimental results showed that model based control performed best on all surfaces across the speeds compared to PID (proportional-integral-derivative) and Open Loop control. A real-time slip detection and traction control algorithm was further developed and evaluated by driving the EPW over five different surfaces at three speeds. Results showed that the performance of anti-slip control was consistent on the varying surfaces at different speeds. The controller was also tested on a front wheel drive EPW to evaluate a forwarding tipping detection and prevention algorithm. Experimental results showed that the tipping could be accurately detected as it was happening and the performance of the tipping prevention strategy was consistent on the slope across different speeds. A terrain-dependent EPW user assistance system was developed based on the controller. Driving rules for wet tile, gravel, slopes and grass were developed and validated by 10 people without physical disabilities. The controller was also adapted to the Personal Mobility and Manipulation Appliance (PerMMA) Generation II, which is an advanced power wheelchair with a flexible mobile base, allowing it to adjust the positions of each of the four casters and two driving wheels. Simulations of the PerMMA Gen II system showed that the mobile base controller was able to climb up to 8” curb and maintain passenger’s posture in a comfort position

Energy Regeneration and Environment Sensing for Robotic Leg Prostheses and Exoskeletons

Robotic leg prostheses and exoskeletons can provide powered locomotor assistance to older adults and/or persons with physical disabilities. However, limitations in automated control and energy-efficient actuation have impeded their transition from research laboratories to real-world environments. With regards to control, the current automated locomotion mode recognition systems being developed rely on mechanical, inertial, and/or neuromuscular sensors, which inherently have limited prediction horizons (i.e., analogous to walking blindfolded). Inspired by the human vision-locomotor control system, here a multi-generation environment sensing and classification system powered by computer vision and deep learning was developed to predict the oncoming walking environments prior to physical interaction, therein allowing for more accurate and robust high-level control decisions. To support this initiative, the “ExoNet” database was developed – the largest and most diverse open-source dataset of wearable camera images of indoor and outdoor real-world walking environments, which were annotated using a novel hierarchical labelling architecture. Over a dozen state-of-the-art deep convolutional neural networks were trained and tested on ExoNet for large-scale image classification and automatic feature engineering. The benchmarked CNN architectures and their environment classification predictions were then quantitatively evaluated and compared using an operational metric called “NetScore”, which balances the classification accuracy with the architectural and computational complexities (i.e., important for onboard real-time inference with mobile computing devices). Of the benchmarked CNN architectures, the EfficientNetB0 network achieved the highest test accuracy; VGG16 the fastest inference time; and MobileNetV2 the best NetScore. These comparative results can inform the optimal architecture design or selection depending on the desired performance of an environment classification system. With regards to energetics, backdriveable actuators with energy regeneration can improve the energy efficiency and extend the battery-powered operating durations by converting some of the otherwise dissipated energy during negative mechanical work into electrical energy. However, the evaluation and control of these regenerative actuators has focused on steady-state level-ground walking. To encompass real-world community mobility more broadly, here an energy regeneration system, featuring mathematical and computational models of human and wearable robotic systems, was developed to simulate energy regeneration and storage during other locomotor activities of daily living, specifically stand-to-sit movements. Parameter identification and inverse dynamic simulations of subject-specific optimized biomechanical models were used to calculate the negative joint mechanical work and power while sitting down (i.e., the mechanical energy theoretically available for electrical energy regeneration). These joint mechanical energetics were then used to simulate a robotic exoskeleton being backdriven and regenerating energy. An empirical characterization of an exoskeleton was carried out using a joint dynamometer system and an electromechanical motor model to calculate the actuator efficiency and to simulate energy regeneration and storage with the exoskeleton parameters. The performance calculations showed that regenerating electrical energy during stand-to-sit movements provide small improvements in energy efficiency and battery-powered operating durations. In summary, this research involved the development and evaluation of environment classification and energy regeneration systems to improve the automated control and energy-efficient actuation of next-generation robotic leg prostheses and exoskeletons for real-world locomotor assistance

Effort reduction and collision avoidance for powered wheelchairs : SCAD assistive mobility system

The new research described in this dissertation created systems and methods to assist wheelchair users and provide them with new realistic and interesting driving opportunities. The work also created and applied novel effort reduction and collision avoidance systems and some new electronic interactive devices. A Scanning Collision Avoidance Device (SCAD) was created that attached to standard powered wheelchairs to help prevent children from driving into things. Initially, mechanical bumpers were used but they made many wheelchairs unwieldy, so a novel system that rotated a single ultra-sonic transducer was created. The SCAD provided wheelchair guidance and assisted with steering. Optical side object detectors were included to cover blind spots and also assist with doorway navigation. A steering lockout mode was also included for training, which stopped the wheelchair from driving towards a detected object. Some drivers did not have sufficient manual dexterity to operate a reverse control. A reverse turn manoeuvring mode was added that applied a sequential reverse and turn function, enabling a driver to escape from a confined situation by operating a single turn control. A new generation of Proportional SCAD was created that operated with proportional control inputs rather than switches and new systems were created to reduce veer, including effort reduction systems. New variable switches were created that provided variable speed control in place of standard digital switches and all that research reduced the number of control actions required by a driver. Finally, some new systems were created to motivate individuals to try new activities. These included a track guided train and an adventure playground that including new interactive systems. The research was initially inspired by the needs of young people at Chailey Heritage, the novel systems provided new and more autonomous driving opportunities for many powered wheelchair users in less structured environments.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Reimagining Robotic Walkers For Real-World Outdoor Play Environments With Insights From Legged Robots: A Scoping Review

PURPOSE For children with mobility impairments, without cognitive delays, who want to participate in outdoor activities, existing assistive technology (AT) to support their needs is limited. In this review, we investigate the control and design of a selection of robotic walkers while exploring a selection of legged robots to develop solutions that address this gap in robotic AT. METHOD We performed a comprehensive literature search from four main databases: PubMed, Google Scholar, Scopus, and IEEE Xplore. The keywords used in the search were the following: “walker”, “rollator”, “smart walker”, “robotic walker”, “robotic rollator”. Studies were required to discuss the control or design of robotic walkers to be considered. A total of 159 papers were analyzed. RESULTS From the 159 papers, 127 were excluded since they failed to meet our inclusion criteria. The total number of papers analyzed included publications that utilized the same device, therefore we classified the remaining 32 studies into groups based on the type of robotic walker used. This paper reviewed 15 different types of robotic walkers. CONCLUSIONS The ability of many-legged robots to negotiate and transition between a range of unstructured substrates suggests several avenues of future consideration whose pursuit could benefit robotic AT, particularly regarding the present limitations of wheeled paediatric robotic walkers for children’s daily outside use. For more information: Kod*lab (link to kodlab.seas.upenn.edu