Redwan Alqasemi

Control of a 9-DoF Wheelchair-mounted robotic arm system using a P300 Brain Computer Interface: Initial experiments

A wheelchair-mounted robotic arm (WMRA) system was designed and built to meet the needs of mobilityimpaired persons with limitations of upper extremities, and to exceed the capabilities of current devices of this type. The control of this 9-degree-of-freedom system expands upon conventional control methods and combines the 7-DoF robotic arm control with the 2-degree-of-freedom power wheelchair control. The 3- degrees of redundancy are optimized to effectively perform activities of daily living and overcome singularities, joint limits and some workspace limitations. The control system is designed for teleoperated or autonomous coordinated Cartesian control, which offers expandability for future research. A P300 Brain Computer Interface (BCI), the BCI2000, was implemented to control the WMRA system. The control is done by recording and analysing the brain activity through an electrode cap while providing visual stimulation to the user via a visual matrix. The visual matrix contains a symbolic or an alphabetic array corresponding to the motion of the WMRA. By recognizing online and in real-time, which element in the matrix elicited a P300, the BCI system can identify which element the user chose to communicate. The chosen element is then communicated to the controller of the WMRA system. The speed and accuracy of the BCI system was tested. This paper gives details of the WMRAs integration with the BCI2000 and documents the experimental results of the BCI and the WMRA in simulation.

Telemanipulation Assistance Based on Motion Intention Recognition

In telemanipulation systems, assistance through variable position/velocity mapping or virtual fixture can improve manipulation capability and dexterity [3, 5, 6, 7, 8]. Conventionally, such assistance is based on the sensory data of the environment and without knowing user’s motion intention. In this paper, user’s motion intention is combined with real-time environment information for applying appropriate assistance. If the current task is following a path, a virtual fixture is applied. If the task is aligning the end-effector with a target, an attractive force field is produced. Similarly, if the task is avoiding obstacles that block the path, a repulsive force field is generated. In order to successfully recognize user’s motion intention, a Hidden Markov Model (HMM)-based algorithm is developed to classify human actions, such as following a path, aligning target and avoiding obstacles.

Analysis, evaluation and development of wheelchair-mounted robotic arms

This paper focuses on kinematic analysis and evaluation of wheelchair mounted robotic arms (WMRA). It addresses the kinematics of the WMRA with respect to its ability to reach common positions while performing activities of daily living (ADL). A procedure is developed for the kinematic analysis and evaluation of a WMRA. In an effort to evaluate two commercial WMRAs, the procedure for kinematic analysis is applied to each manipulator. Design recommendations and insights with regard to each device are obtained and used to design a new WMRA to overcome the limitations of these devices. This method benefits the researchers by providing a standardized procedure for kinematic analysis of WMRAs that is capable of evaluating independent designs.

Maximizing Manipulation Capabilities for People with Disabilities Using a 9-DoF Wheelchair-Mounted Robotic Arm System

A wheelchair-mounted robotic arm (WMRA) system was designed and built to meet the needs of mobility-impaired persons with limitations of upper extremities, and to exceed the capabilities of current devices of this type. The control of this 9-DoF system expands on the conventional control methods and combines the 7-DoF robotic arm control with the 2-DoF power wheelchair control. The 3-degrees of redundancy are optimized to effectively perform activities of daily living (ADLs) and overcome singularities, joint limits and some workspace limitations. The control system is designed for teleoperated or autonomous coordinated Cartesian control, and it offers expandability for future research, such as voice or sip and puff control operations and sensor assist functions.

Design, simulation and testing of a new modular wheelchair mounted robotic arm to perform activities of daily living

A new and light wheelchair-mounted robotic arm (WMRA-II) was designed and built to meet the needs of mobility-impaired persons with limitations of upper extremities, and to exceed the capabilities of current devices of this type. The structure of the new robotic arm utilizes carbon-fiber and polycarbonate tubes to reduce the arm weight. The mechanical design incorporates DC servo drive, with actuator hardware at each individual joint, allowing reconfigurable link lengths. It has seven degrees of freedom and uses a side mount on a power wheelchair. A single control board that is capable of controlling eight motors was used for coordinated Cartesian control of the robotic arm. This paper discusses the current state of the art in WMRAs; describes the design goals and user requirements for this device; explains the component selection process; discusses details of the mechanical design and the controller design; and describes the testing of the completed arm. Further improvements are also suggested.

Multi-scale superquadric fitting for efficient shape and pose recovery of unknown objects

Rapidly acquiring the shape and pose information of unknown objects is an essential characteristic of modern robotic systems in order to perform efficient manipulation tasks. In this work, we present a framework for 3D geometric shape recovery and pose estimation from unorganized point cloud data. We propose a low latency multi-scale voxelization strategy that rapidly fits superquadrics to single view 3D point clouds. As a result, we are able to quickly and accurately estimate the shape and pose parameters of relevant objects in a scene. We evaluate our approach on two datasets of common household objects collected using Microsofts Kinect sensor. We also compare our work to the state of the art and achieve comparable results in less computational time. Our experimental results demonstrate the efficacy of our approach.

Design and Construction of a Robotic Gripper for Activities of Daily Living for People with Disabilities

A new robotic gripper was designed and constructed for activities of daily living (ADL) to be used with the new wheelchair-mounted robotic arm developed at USF. Two aspects of the new gripper made it unique; one is the design of the paddles, and the other is the design of the actuation mechanism that produces parallel motion for effective gripping. The paddles of the gripper were designed to grasp a wide variety objects with different shapes and sizes that are used in every day life. The driving mechanism was designed to be simple, light, effective, safe, self content, and independent of the robotic arm attached to it.

Optimized dual-trajectory tracking control of a 9-DoF WMRA system for ADL tasks

Wheelchair-bound persons with upper limb motion limitations can utilize a wheelchair-mounted robotic arm (WMRA) to perform activities of daily living (ADL) tasks. In this paper, an optimized control of our 9-DoF system, consisting of a 7-DoF robotic arm and a 2-DoF power wheelchair, is achieved. For effective ADL task execution, positioning the end-effector with proper wheelchair orientation was optimized as part of the control algorithm. Separate wheelchair and end-effector trajectories were simultaneously followed to execute a “Go To and Open the Door” task. The control methodology, implementation and test results in simulation are presented in this paper.

A vision based P300 Brain Computer Interface for grasping using a wheelchair-mounted robotic arm

In this paper, we present a novel vision based interface for selecting an object using a Brain Computer Interface (BCI), and grasping it using a robotic arm mounted to a powered wheelchair. As issuing commands through BCI is slow, this system was designed to allow a user to perform a complete task using the robotic system via the BCI issuing as few commands as possible, without losing concentration on the stimuli or the task. A scene image is captured by a camera mounted on the wheelchair, from which a dynamically sized non-uniform stimulus grid is created using edge information. Dynamically sized grids improve object selection efficiency. Oddball paradigm and P300 event related potentials (ERP) are used to select stimuli, the stimuli being each cell in the grid. Once selected, object segmentation and matching is used to identify the object. Then the user, using BCI, chooses an action to be performed on the object via the wheelchair mounted robotic arm (WMRA). Tests on 6 healthy human subjects validated the functionality of the system. An average accuracy of 85.56% was achieved for stimuli selection over all subjects. With the proposed system, it took the users an average of 5 commands to grasp an object. The system will eventually be useful for completely paralyzed or locked-in patients for performing activities of daily living (ADL) tasks.

VR4VR: vocational rehabilitation of individuals with disabilities in immersive virtual reality environments

This paper presents a virtual reality for vocational rehabilitation system (VR4VR) that is currently in development at the University of South Floridas Center for Assistive, Rehabilitation, and Robotics Technologies (CARRT). VR4VR utilizes virtual reality to assess and train individuals with severe cognitive and physical disabilities. Using virtual reality offers several advantages such as being inexpensive, safer and easily adjustable to different user needs through customization of environments, content and real time interventions. The system is composed of the following components: a virtual reality training area surrounded by an optical motion tracking system, a curved screen with two projectors, a server computer, a remote control interface on a tablet computer for job coaches, and a virtual assistive robot. This paper focuses on virtual reality training for underserved individuals with cognitive disabilities, such as autism spectrum disorder (ASD) and traumatic brain injury (TBI). We describe six transferrable skill modules and corresponding design considerations. Future work focuses on people with severe mobility impairment, such as spinal cord injury (SCI).