Indoor Terrains Research Articles

Mobile Application based Indoor Positioning and Navigational System using Dijkstra’s Algorithm

This paper offers a method for developing an efficient indoor navigation system with the consideration of the shortest path between source and destination. The challenge for the indoor navigation system is to provide personal navigation information and the optimal route. Applications of indoor navigation systems need consideration of the Shortest Path problem. The shortest pathways can be used to find solutions to the current problems using Dijkstra’s algorithm. Based on the issue with the indoor navigation system, the shortest way and the best path are calculated. This is crucial to navigation systems since it can aid in making wise decisions and time-saving choices. The primary goal is to obtain the implementation at an affordable price. These applications and services are made available indoors, where the GPS does not function. The goal of indoor navigation is to direct users inside buildings. Dijkstra’s algorithm for locating objects and for moving along the shortest path in an indoor setting are examined in this work. Experimental results of indoor navigation systems were carried out on my organization’s indoor environment and verified the applicability of the presented Indoor Navigation System. The techniques provided include map digitization, locating a user, and choosing the shortest route. This is accomplished through a mobile application created for the Android operating system, and indoor navigation is carried out by using Dijkstra’s algorithm. The proposed method is implemented in our college academic block, and the experimental results show that our navigation method is feasible and effective. To verify the reliability of the algorithm, the proposed application fulfils the criteria of an indoor navigation system to produce the optimal route between two points when applied to a map of our college’s indoor terrain.

Design and Control of TAWL—A Wheel-Legged Rover With Terrain-Adaptive Wheel Speed Allocation Capability

This article presents the design, control, and implementation of a novel four-legged rover for planetary exploration. A novel active-passive coupled leg mechanism is proposed for the design of the planetary rover. Then, based on the design of the actuator and leg mechanism, the novel wheel-legged robotic system is developed. Furthermore, the kinematic model of the robot is established and then a kinematics-based control strategy is presented, incorporating roll-and-pitch control, contact force control, wheel speed allocation, and steering modules. Experiments are carried out on regular indoor terrain and irregular outdoor terrain. The variations of rover attitude angles and contact forces in regular terrain decrease by more than 90% and 60%, respectively. In the outdoor environment, rover attitude angles are regulated within 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sup> and variations of contact forces are reduced by more than half. In addition, the proposed control strategy decreases the slippage on hard unstructured terrain by almost half. Finally, the robustness and failure responses of the robot are investigated, which are also verified by simulations and experiments.

Egocentric vision-based detection of surfaces: towards context-aware free-living digital biomarkers for gait and fall risk assessment

BackgroundFalls in older adults are a critical public health problem. As a means to assess fall risks, free-living digital biomarkers (FLDBs), including spatiotemporal gait measures, drawn from wearable inertial measurement unit (IMU) data have been investigated to identify those at high risk. Although gait-related FLDBs can be impacted by intrinsic (e.g., gait impairment) and/or environmental (e.g., walking surfaces) factors, their respective impacts have not been differentiated by the majority of free-living fall risk assessment methods. This may lead to the ambiguous interpretation of the subsequent FLDBs, and therefore, less precise intervention strategies to prevent falls.MethodsWith the aim of improving the interpretability of gait-related FLDBs and investigating the impact of environment on older adults’ gait, a vision-based framework was proposed to automatically detect the most common level walking surfaces. Using a belt-mounted camera and IMUs worn by fallers and non-fallers (mean age 73.6 yrs), a unique dataset (i.e., Multimodal Ambulatory Gait and Fall Risk Assessment in the Wild (MAGFRA-W)) was acquired. The frames and image patches attributed to nine participants’ gait were annotated: (a) outdoor terrains: pavement (asphalt, cement, outdoor bricks/tiles), gravel, grass/foliage, soil, snow/slush; and (b) indoor terrains: high-friction materials (e.g., carpet, laminated floor), wood, and tiles. A series of ConvNets were developed: EgoPlaceNet categorizes frames into indoor and outdoor; and EgoTerrainNet (with outdoor and indoor versions) detects the enclosed terrain type in patches. To improve the framework’s generalizability, an independent training dataset with 9,424 samples was curated from different databases including GTOS and MINC-2500, and used for pretrained models’ (e.g., MobileNetV2) fine-tuning.ResultsEgoPlaceNet detected outdoor and indoor scenes in MAGFRA-W with 97.36% and 95.59% (leave-one-subject-out) accuracies, respectively. EgoTerrainNet-Indoor and -Outdoor achieved high detection accuracies for pavement (87.63%), foliage (91.24%), gravel (95.12%), and high-friction materials (95.02%), which indicate the models’ high generalizabiliy.ConclusionsEncouraging results suggest that the integration of wearable cameras and deep learning approaches can provide objective contextual information in an automated manner, towards context-aware FLDBs for gait and fall risk assessment in the wild.

ZUPT-Aided INS Bypassing Stance Phase Detection by Using Foot-Instability-Based Adaptive Covariance

In this paper, we propose a Foot-Instability-Based Adaptive (FIBA) covariance to dynamically adjust the covariance matrix for the pseudo-zero-velocity measurements in the Zero velocity UPdaTe (ZUPT)-aided Inertial Navigation Systems (INS). The proposed ZUPT-aided INS using the FIBA covariance is implemented in an Extended Kalman Filter (EKF) framework, which utilizes a time-varying measurement covariance matrix that is updated in each iteration according to the FIBA covariance. The FIBA covariance is designed to have a very high value during the swing phases in a gait cycle, and the value significantly decreases during the stance phases. As a result, the proposed method eliminates a need to use a binary stance phase detector in implementation of the ZUPT-aided INS. Properties of EKF innovation sequences in the algorithm were studied, and two series of indoor pedestrian navigation experiments were conducted to demonstrate the navigation performance of the proposed system. In the first series of experiments, which included cases of walking and running, localization solutions produced by the system using the FIBA covariance demonstrated 36% and 64% improvements in navigation accuracy along the horizontal and vertical directions, respectively. In the second series of experiments, which included a pedestrian walking on different indoor terrains, such as flat planes, stairs, and ramps, the navigation accuracy of the system using the FIBA covariance reduced horizontal and vertical position errors by 12% and 45%, respectively, as compared to the conventional ZUPT-aided INS.

Motion Improvement of Four-Wheeled Omnidirectional Mobile Robots for Indoor Terrain

The four-wheeled omnidirectional platform is great to use for an indoor mobile robot. It can increasingly move and change heading directions. However, the robot is easy to slip when it is moving. One or more wheels are sometimes not touching the ground. This paper approaches to solving these problems by computational simulation in locomotion. The mathematical models simulate the robot movement. The robot struggles to go to the target with randomly simulated slips and untouched ground circumstances. The robot can estimate the positions using the Kalman filter and readjust itself to the planned path. Consequently, this paper demonstrates the motion improvement and compares the results of decreasing errors.

Motion Improvement of Four-Wheeled Omnidirectional Mobile Robots for Indoor Terrain

Motion Improvement of Four-Wheeled Omnidirectional Mobile Robots for Indoor Terrain