Chaomin Luo

A privacy-aware Kinect-based system for healthcare professionals

In this paper, we present a novel system for healthcare professionals to enhance their compliance with best practices and regulations using Microsoft Kinect sensors and smart watches while strictly protecting patient privacy. A core contribution of this study is a registration mechanism for a healthcare professional to explicitly give our system the permission to monitor his or her activities. Our system supports the use of multiple Kinect sensors for improved tracking accuracy and better coverage for large workplaces. Furthermore, we introduce a non-intrusive biometrics-based single sign-on mechanism to allow a user to register once for all Kinect sensors within each session. Finally, our system generates alerts reliably on detection of non-compliant activities and delivers the alerts discreetly to a consented healthcare professional via a designated smart watch according to his/her personal preference.

Sensor-based autonomous robot navigation under unknown environments with grid map representation

Real-time navigation and mapping of an autonomous robot is one of the major challenges in intelligent robot systems. In this paper, a novel sensor-based biologically inspired neural network algorithm to real-time collision-free navigation and mapping of an autonomous mobile robot in a completely unknown environment is proposed. A local map composed of square grids is built up through the proposed neural dynamics for robot navigation with restricted incoming sensory information. With equipped sensors, the robot can only sense a limited reading range of surroundings with grid map representation. According to the measured sensory information, an accurate map with grid representation of the robot with local environment is dynamically built for the robot navigation. The real-time robot motion is planned through the varying neural activity landscape, which represents the dynamic environment. The proposed model for autonomous robot navigation and mapping is capable of planning a real-time reasonable trajectory of an autonomous robot. Simulation and comparison studies are presented to demonstrate the effectiveness and efficiency of the proposed methodology that concurrently performs collision-free navigation and mapping of an intelligent robot.

LiftingDoneRight: A Privacy-Aware Human Motion Tracking System for Healthcare Professionals

This article describes the design and implementation of LiftingDoneRight, a novel system for healthcare professionals to enhance their compliance with best practices and regulations regarding proper body mechanics for lifting and pulling activities. The system uses Microsoft Kinect to track the motion of consented users non-intrusively. The system relies on the use of a smartwatch to deliver an alert via vibration and text display whenever a wrong activity that violated the proper body mechanics has been detected. A core contribution of this study is a registration mechanism for a healthcare professional to explicitly give permission to the system to monitor his or her activities. Furthermore, a non-intrusive biometrics-based single sign-on mechanism is incorporated into the system to allow a user to be automatically identified for tracking as long as the user has manually registered with the system before. Finally, the system offers a number of configurations to accommodate different usability needs and privacy requirements.

An effective vector-driven biologically-motivated neural network algorithm to real-time autonomous robot navigation

A novel biologically-motivated neural networks approach associated with developed vector-driven autonomous robot navigation is proposed in this paper. The biologically-motivated neural networks (BNN) algorithm is employed to guide an autonomous robot to reach goal with obstacle avoidance motivated by Grossbergs model for a biological neural system. As the robot plans its trajectory toward the goal, unreasonable path will be inevitably planned. A vector-based guidance paradigm is developed for guidance of the robot locally so as to plan more reasonable trajectories. In addition, square cell map representations are proposed for realtime autonomous robot navigation. The BNN based scheme demonstrates that the algorithms avoid the issue of local minima in path planning. In this paper, both simulation and comparison studies of an autonomous robot navigation demonstrate that the proposed model is capable of planning more reasonable and shorter collision-free paths in non-stationary and unstructured environments compared with other approaches.

An effective trace-guided wavefront navigation and map-building approach for autonomous mobile robots

This paper aims to address a trace-guided real-time navigation and map building approach of an autonomous mobile robot. Wave-front based global path planner is developed to generate a global trajectory for an autonomous mobile robot. Modified Vector Field Histogram (M-VFH) is employed based on the LIDAR sensor information to guide the robot locally to be autonomously traversed with obstacle avoidance by following traces provided by the global path planner. A local map composed of square grids is created through the local navigator while the robot traverses with limited LIDAR sensory information. From the measured sensory information, a map of the robot’s immediate limited surroundings is dynamically built for the robot navigation. The real-time wave-front based navigation and map building methodology has been successfully demonstrated in a Player/Stage simulation environment. With the wave-front-based global path planner and M-VFH local navigator, a safe, short, and reasonable trajectory is successfully planned in a majority of situations without any templates, without explicitly optimizing any global cost functions, and without any learning procedures. Its effectiveness, feasibility, efficiency and simplicity of the proposed real-time navigation and map building of an autonomous mobile robot have been successfully validated by simulation and comparison studies. Comparison studies of the proposed approach with the other path planning approaches demonstrate that the proposed method is capable of planning more reasonable and shorter collision-free trajectories autonomously.

A Novel LIDAR-driven Two-level Approach for Real-time Unmanned Ground Vehicle Navigation and Map Building

In this paper, a two-level LIDAR-driven hybrid approach is proposed for real-time unmanned ground vehicle navigation and map building. Top level is newly designed enhanced Voronoi Diagram (EVD) method to plan a global trajectory for an unmanned vehicle. Bottom level employs Vector Field Histogram (VFH) algorithm based on the LIDAR sensor information to locally guide the vehicle under complicated workspace, in which it autonomously traverses from one node to another within the planned EDV with obstacle avoidance. To find the least-cost path within the EDV, novel distance and angle based search heuristic algorithms are developed, in which the cost of an edge is the risk of traversing the edge. An EVD is first constructed based on the environment, which is utilized to generate the initial global trajectory with obstacle avoidance. The VFH algorithm is employed to guide the vehicle to follow the path locally. Its effectiveness and efficiency of real-time navigation and map building for unmanned vehicles have been successfully validated by simulation studies and experiments. The proposed approach is successfully experimented on an actual unmanned vehicle to demonstrate the real-time navigation and map building performance of the proposed method. The vehicle appears to follow a very stable path while navigating through various obstacles.

Multi-goal Motion Planning of an Autonomous Robot in Unknown Environments by an Ant Colony Optimization Approach

An ant colony optimization (ACO) approach is proposed in this paper for real-time concurrent map building and navigation for multiple goals purpose. In real world applications such as rescue robots, and service robots, an autonomous mobile robot needs to reach multiple goals with the shortest path that, in this paper, is capable of being implemented by an ACO method with minimized overall distance. Once a global path is planned, a foraging-enabled trail is created to guide the robot to the multiple goals. A histogram-based local navigation algorithm is employed locally for obstacle avoidance along the trail planned by the global path planner. A re-planning-based algorithm aims to generate path while a mobile robot explores through a terrain with map building in unknown environments. In this paper, simulation results demonstrate that the real-time concurrent mapping and multi-goal navigation of an autonomous robot is successfully performed under unknown environments.

Byzantine fault tolerance for collaborative editing with commutative operations

In this paper, we present a study on how to achieve Byzantine fault tolerance for collaborative editing systems with commutative operations. Recent research suggests that Conflict-free Replicated Data Types (CRDTs) can be used to construct collaborative editing systems where concurrent update operations are commutative. This new approach is shown to avoid the complex issue of conflict resolution for concurrent updates to a shared document. The shared document is often modeled as a linear text buffer where each basic element is assigned a globally unique and totally ordered identifier. The linear text buffer constructed this way would constitute as a CRDT, which would make concurrent update operations issued by different users commutative. State convergence at all users can be achieved automatically as long as the users could receive the same set of operations irrespective of their relative ordering. However, it is not straightforward to guarantee state convergence in the presence of malicious users and external adversaries. In this paper, we carefully analyze the threats towards this type of systems, and propose a lightweight solution to achieve Byzantine fault tolerance with low runtime overhead. We define a set of correctness properties for such systems and prove that the proposed Byzantine fault tolerance mechanisms guarantee these properties.

An effective search and navigation model to an auto-recharging station of driverless vehicles

An electric vehicle auto-recharging station is a component in an infrastructure supplying electric energy for the recharging of plug-in electric vehicles. An auto-recharging station is usually accessible to an autonomous driverless vehicle driven by intelligent algorithms. A driverless vehicle is assumed to be capable of autonomously searching and navigating it into a recharging station. In this paper, a novel hybrid intelligent system is developed to navigate an autonomous vehicle into a recharging station. The driverless vehicle driven by D*Lite path planning methodology in conjunction with a Vector Field Histogram (VFH) local navigator is developed for search and navigation purpose to reach an auto-recharging station with obstacle avoidance. Once it approaches vicinity of the recharging station, the driverless vehicle should be directed at the recharging station at a proper angle, which is accomplished by a Takagi-Sugeno fuzzy logic model. A novel error control of angle and distance heuristic approach is proposed to adjust the vehicle straight at the recharging station. Development of the driverless vehicle in terms of hardware and software design is described. Simulation studies on the Player/Stage platform demonstrate that the proposed model can successfully guide an autonomous driverless vehicle into the recharging station. Experimental effort shows its promising results that the driverless vehicle is able to autonomously navigate it to an auto-recharging station.

Planning optimal trajectory for histogram-enabled mapping and navigation by an efficient PSO algorithm

A particle swarm optimization (PSO) algorithm associated with a histogram navigation method is proposed in this paper for real-time map building and path planning for multiple goals applications. In real world applications such as rescue robots, service robots, mining mobile robots, and mine searching robots, etc., an autonomous mobile robot needs to reach multiple goals with a shortest path that, in this paper, is capable of being implemented by a PSO method with minimized overall distance. Once a global trajectory is planned, a foraging-enabled trail is created to guide the robot to the multiple goals. A histogram-based local navigation algorithm is employed to plan a collision-free path along the trail planned by the global path planner. A replanning-based algorithm aims to generate path while a mobile robot explores through a terrain with map building in unknown environments. In this paper, simulation and experimental results demonstrate that the real-time concurrent mapping and multi-goal navigation of an autonomous robot is successfully performed under unknown environments.