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

Learning to visually predict terrain properties for planetary rovers

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 174-180).For future planetary exploration missions, improvements in autonomous rover mobility have the potential to increase scientific data return by providing safe access to geologically interesting sites that lie in rugged terrain, far from landing areas. This thesis presents an algorithmic framework designed to improve rover-based terrain sensing, a critical component of any autonomous mobility system operating in rough terrain. Specifically, this thesis addresses the problem of predicting the mechanical properties of distant terrain. A self-supervised learning framework is proposed that enables a robotic system to learn predictions of mechanical properties of distant terrain, based on measurements of mechanical properties of similar terrain that has been previously traversed. The proposed framework relies on three distinct algorithms. A mechanical terrain characterization algorithm is proposed that computes upper and lower bounds on the net traction force available at a patch of terrain, via a constrained optimization framework. Both model-based and sensor-based constraints are employed. A terrain classification method is proposed that exploits features from proprioceptive sensor data, and employs either a supervised support vector machine (SVM) or unsupervised k-means classifier to assign class labels to terrain patches that the rover has traversed. A second terrain classification method is proposed that exploits features from exteroceptive sensor data (e.g. color and texture), and is automatically trained in a self-supervised manner, based on the outputs of the proprioceptive terrain classifier.(cont.) The algorithm includes a method for distinguishing novel terrain from previously observed terrain. The outputs of these three algorithms are merged to yield a map of the surrounding terrain that is annotated with the expected achievable net traction force. Such a map would be useful for path planning purposes. The algorithms proposed in this thesis have been experimentally validated in an outdoor, Mars-analog environment. The proprioceptive terrain classifier demonstrated 92% accuracy in labeling three distinct terrain classes. The exteroceptive terrain classifier that relies on self-supervised training was shown to be approximately as accurate as a similar, human-supervised classifier, with both achieving 94% correct classification rates on identical data sets. The algorithm for detection of novel terrain demonstrated 89% accuracy in detecting novel terrain in this same environment. In laboratory tests, the mechanical terrain characterization algorithm predicted the lower bound of the net available traction force with an average margin of 21% of the wheel load.by Christopher A. Brooks.Ph.D