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The purpose of this paper is to describe a compact wheelchair, which has two 3-degrees of freedom (DOF) legs and a 1-DOF base (the total DOF of the leg system is 7) for stair-climbing, and wheels for flat surface driving.

The proposed wheelchair climbs stairs using the two 3-DOF legs with boomerang-shaped feet. The leg mechanisms are folded into the compact wheelchair body when the wheelchair moves over flat surfaces. The authors also propose a simple estimation method of stair shape using laser distance sensors, and a dual motor driving system to increase joint power.

The proposed wheelchair can climb arbitrary height and width stairs by itself, even when they are slightly curved. During climbing, the trajectory of the seat position is linear to guarantee the comfort of rider, and the wheelchair always keeps a stable condition to ensure the stability in an emergency stop.

The wheelchair mechanism with foldable legs and driving wheels enables smooth stair climbing, efficient flat surface driving and additional useful motions such as standing and tilting.

The purpose of this study is to propose a novel plane-based mapping method for legged-robot navigation in a stairway environment.

The approach implemented in this study estimates a plane for each step of a stairway using a weighted average of sensor measurements and predictions. It segments planes from point cloud data via random sample consensus (RANSAC). The prediction uses the regular structure of a stairway. When estimating a plane, the algorithm considers the errors introduced by the distance sensor and RANSAC, in addition to stairstep irregularities, by using covariance matrices. The plane coefficients are managed separately with the data structure suggested in this study. In addition, this data structure allows the algorithm to store the information of each stairstep as a single entity.

In the case of a stairway environment, the accuracy delivered by the proposed algorithm was higher than those delivered by traditional mapping methods. The hardware experiment verified the accuracy and applicability of the algorithm.

The proposed algorithm provides accurate stairway-environment mapping and detailed specifications of each stairstep. Using this information, a legged robot can navigate and plan its motion in a stairway environment more efficiently.

The purpose of this paper is to design and test effective indoor navigation solutions for visually impaired people in situations where GPS, bluetooth or Wi-Fi signals are unavailable. The authors use the inertial measurement units (IMU), the compass and the barometer of a smart phone.

The authors have used commercial Android smart phones with IMU, compass and barometer to record a path and to give navigation instructions in an adapted way using a mobility-specific vocabulary. The method proposed is to save paths taking into account different indoor waypoints such as the stairs (change from one floor to another) and the change of direction of the trajectory of the path (e.g. one-fourth turn right or left), recording data from the IMU sensor’s, compass and barometer of the smart phone. Having this information and the characteristics of the each segment (distance, azimuth to the north and pressure) of the path, it is possible to provide functional navigation guidance to the visually impaired subject. Three different visually impaired people (one partially sighted and two fully blind) and three sighted people have tested the paths. The efficiency of the navigation is analyzed in terms of distance and time using the comparison between blind and sighted people.

The main finding is that it is possible to guide visually impaired people some hundreds of meters just using the sensors of a smart phone under certain conditions: the visually impaired person has to understand the guidance instructions and respect some strategies (e.g. not to walk diagonally across vast spaces). Additionally it is observed that the visually impaired participants walked distances, which are not much different to the optimal values. On the other hand; because of their hesitations using their white cane to find free paths, they take in some cases 50 percent more time to arrive (for a few minutes path, this time is not critical and even more efficient than looking for a guide). One thing to highlight is that even with this hesitation, the subjects arrived to the final destination.

This paper demonstrates how an IMU coupled to a compass and a barometer from a Smart Phone employing a spoken mobility language (e.g. next corridor to the left; at the end of the stairs turn right, turn left, etc.) can guide visually impaired people inside buildings.

Lower-limb exoskeleton systems enable people with spinal cord injury to regain some degree of locomotion ability, as the expected motion curve needs to adapt with changing scenarios, i.e. stair heights, distance to the stairs. The authors’ approach enables exoskeleton systems to adapt to different scenarios in stair ascent task safely.

In this paper, the authors learn the locomotion from predefined trajectories and walk upstairs by re-planning the trajectories according to external forces posed on exoskeleton systems. Moreover, instead of using complex sensors as inputs for re-planning in real-time, the approach can obtain forces acting on exoskeleton through dynamic model of human-exoskeleton system learned by an online machine learning approach without accurate parameters.

The proposed approach is validated in both simulation environment and a real walking assistance exoskeleton system. Experimental results prove that the proposed approach achieves better performance than the traditional predefined gait approach.

First, the approach obtain the external forces by a learned dynamic model of human-exoskeleton system, which reduces the cost of exoskeletons and avoids the heavy task of translating sensor input into actuator output. Second, the approach enables exoskeleton accomplish stair ascent task safely in different scenarios.

This study aims to investigate if smartphone sensors can be used in an unmanned aerial vehicle (UAV) localization system. With the development of technology, smartphones have been tentatively used in micro-UAVs due to their lightweight, inexpensiveness and flexibility. In this study, a Samsung Galaxy S3 smartphone is selected as an on-board sensor platform for UAV localization in Global Positioning System (GPS)-denied environments and two main issues are investigated: Are the phone sensors appropriate for UAV localization? If yes, what are the boundary conditions of employing them?

Efficient accuracy estimation methodologies for the phone sensors are proposed without using any expensive instruments. Using these methods, one can estimate his phone sensors accuracy at any time without special instruments. Then, a visual-inertial odometry scheme is introduced to evaluate the phone sensors-based path estimation performance.

Boundary conditions of using smartphone in a UAV navigation system are found. Both indoor and outdoor localization experiments are carried out and experimental results validate the effectiveness of the boundary conditions and the corresponding implemented scheme.

With the phone as a payload, UAVs can be further realized in smaller scale at lower cost, which will be used widely in the field of industrial robots.

Devices for step estimation are body-worn devices used to compute steps taken and/or distance covered by the user. Even though textiles or clothing are foremost to come to mind in terms of articles meant to be worn, their prominence among devices and systems meant for cadence is overshadowed by electronic products such as accelerometers, wristbands and smart phones. Athletes and sports enthusiasts using knee sleeves should be able to track their performances and monitor workout progress without the need to carry other devices with no direct sport utility, such as wristbands and wearable accelerometers. The purpose of this study thus is to contribute to the broad area of wearable devices for cadence application by developing a cheap but effective and efficient stride measurement system based on a knee sleeve.

A textile strain sensor is designed by weft knitting silver-plated nylon yarn together with nylon DTY and covered elastic yarn using a 1 × 1 rib structure. The area occupied by the silver-plated yarn within the structure served as the strain sensor. It worked such that, upon being subjected to stress, the electrical resistance of the sensor increases and in turn, is restored when the stress is removed. The strip with the sensor is knitted separately and subsequently sewn to the knee sleeve. The knee sleeve is then connected to a custom-made signal acquisition and processing system. A volunteer was employed for a wearer trial.

Experimental results establish that the number of strides taken by the wearer can easily be correlated to the knee flexion and extension cycles of the wearer. The number of peaks computed by the signal acquisition and processing system is therefore counted to represent stride per minute. Therefore, the sensor is able to effectively count the number of strides taken by the user per minute. The coefficient of variation of over-ground test results yielded 0.03%, and stair climbing also obtained 0.14%, an indication of very high sensor repeatability.

The study was conducted using limited number of volunteers for the wearer trials.

By embedding textile piezoresistive sensors in some specific garments and or accessories, physical activity such as gait and its related data can be effectively measured.

To the best of our knowledge, this is the first application of piezoresistive sensing in the knee sleeve for stride estimation. Also, this study establishes that it is possible to attach (sew) already-knit textile strain sensors to apparel to effectuate smart functionality.

The purpose of this paper is to develop an online smoothing zero-velocity-update (ZUPT) method that helps achieve smooth estimation of human foot motion for the ZUPT-aided inertial pedestrian navigation system.

The smoothing ZUPT is based on a Rauch–Tung–Striebel (RTS) smoother, using a six-state Kalman filter (KF) as the forward filter. The KF acts as an indirect filter, which allows the sensor measurement error and position error to be excluded from the error state vector, so as to reduce the modeling error and computational cost. A threshold-based strategy is exploited to verify the detected ZUPT periods, with the threshold parameter determined by a clustering algorithm. A quantitative index is proposed to give a smoothness estimate of the position data.

Experimental results show that the proposed method can improve the smoothness, robustness, efficiency and accuracy of pedestrian navigation.

Because of the chosen smoothing algorithm, a delay no longer than one gait cycle is introduced. Therefore, the proposed method is suitable for applications with soft real-time constraints.

The paper includes implications for the smooth estimation of most types of pedal locomotion that are achieved by legged motion, by using a sole foot-mounted commercial-grade inertial sensor.

This paper helps realize smooth transitions between swing and stance phases, helps enable continuous correction of navigation errors during the whole gait cycle, helps achieve robust detection of gait phases and, more importantly, requires lower computational cost.

The purpose of this paper is to demonstrate that significant surface finish improvements can be accomplished in stereolithography (SL) fabricated parts by applying a new process planning method based on parameter estimation (PE).

PE is a method that finds a set of parameter values that minimize a measure of deviation. In this work, the measure of deviation is the difference between the exposure received by points along down‐facing surfaces and the SL resin's critical exposure.

The surface finish of down‐facing surfaces can be improved by a factor of 2‐9, depending upon the surface angle, compared with parts prepared using commercially available software. Surface finishes less than 1 μm Ra have been demonstrated on a SLA‐250/50 machine.

Only down‐facing surfaces can have their surface finish improved using this method.

Common form errors known as “stair‐stepping” can be diminished on parts fabricated using SL.

The usage of PE methods for process planning is a new approach.

This study aims to design a new low-cost localization platform for estimating the location and orientation of a pedestrian in a building. The micro-electro-mechanical systems (MEMS) sensor error compensation and the algorithm were improved to realize the localization and altitude accuracy.

The platform hardware was designed with common low-performance and inexpensive MEMS sensors, and with a barometric altimeter employed to augment altitude measurement. The inertial navigation system (INS) – extended Kalman filter (EKF) – zero-velocity updating (ZUPT) (INS-EKF-ZUPT [IEZ])-extended methods and pedestrian dead reckoning (PDR) (IEZ + PDR) algorithm were modified and improved with altitude determined by acceleration integration height and pressure altitude. The “AND” logic with acceleration and angular rate data were presented to update the stance phases.

The new platform was tested in real three-dimensional (3D) in-building scenarios, achieved with position errors below 0.5 m for 50-m-long route in corridor and below 0.1 m on stairs. The algorithm is robust enough for both the walking motion and the fast dynamic motion.

The paper presents a new self-developed, integrated platform. The IEZ-extended methods, the modified PDR (IEZ + PDR) algorithm and “AND” logic with acceleration and angular rate data can improve the high localization and altitude accuracy. It is a great support for the increasing 3D location demand in indoor cases for universal application with ordinary sensors.