Alexandre Campeau-Lecours

A Cable-Suspended Intelligent Crane Assist Device for the Intuitive Manipulation of Large Payloads

This paper presents a cable-suspended crane system to assist operators in moving and lifting large payloads. The main objective of this work is to develop a simple and reliable system to help operators in industry to be more productive while preventing injuries. The system is based on the development of a precise and reliable cable angle sensor and a complete dynamic model of the system. Adaptive horizontal and vertical controllers designed for direct physical human-robot interaction are then proposed. Different techniques are then proposed to estimate the payload acceleration in order to increase the controller performances. Finally, experiments performed on a full-scale industrial system are presented.

Intuitive wireless control of a robotic arm for people living with an upper body disability.

Assistive Technologies (ATs) also called extrinsic enablers are useful tools for people living with various disabilities. The key points when designing such useful devices not only concern their intended goal, but also the most suitable human-machine interface (HMI) that should be provided to users. This paper describes the design of a highly intuitive wireless controller for people living with upper body disabilities with a residual or complete control of their neck and their shoulders. Tested with JACO, a six-degree-of-freedom (6-DOF) assistive robotic arm with 3 flexible fingers on its end-effector, the system described in this article is made of low-cost commercial off-the-shelf components and allows a full emulation of JACOs standard controller, a 3 axis joystick with 7 user buttons. To do so, three nine-degree-of-freedom (9-DOF) inertial measurement units (IMUs) are connected to a microcontroller and help measuring the users head and shoulders position, using a complementary filter approach. The results are then transmitted to a base-station via a 2.4-GHz low-power wireless transceiver and interpreted by the control algorithm running on a PC host. A dedicated software interface allows the user to quickly calibrate the controller, and translates the information into suitable commands for JACO. The proposed controller is thoroughly described, from the electronic design to implemented algorithms and user interfaces. Its performance and future improvements are discussed as well.

Modeling of physical human–robot interaction : admittance controllers applied to intelligent assist devices with large payload

Enhancement of human performance using an intelligent assist device is becoming more common. In order to achieve effective augmentation of human capacity, cooperation between human and robot must be safe and very intuitive. Ensuring such collaboration remains a challenge, especially when admittance control is used. This paper addresses the issues of transparency and human perception coming from vibration in admittance control schemes. Simulation results obtained with our suggested improved model using an admittance controller are presented, then four models using transfer functions are discussed in detail and evaluated as a means of simulating physical human–robot interaction using admittance control. The simulation and experimental results are then compared in order to assess the validity and limitations of the proposed models in the case of a four-degree-of-freedom intelligent assist device designed for large payload.

Wireless sEMG-Based Body–Machine Interface for Assistive Technology Devices

Assistive technology (AT) tools and appliances are being more and more widely used and developed worldwide to improve the autonomy of people living with disabilities and ease the interaction with their environment. This paper describes an intuitive and wireless surface electromyography (sEMG) based body–machine interface for AT tools. Spinal cord injuries at C5–C8 levels affect patients’ arms, forearms, hands, and fingers control. Thus, using classical AT control interfaces (keypads, joysticks, etc.) is often difficult or impossible. The proposed system reads the AT users’ residual functional capacities through their sEMG activity, and converts them into appropriate commands using a threshold-based control algorithm. It has proven to be suitable as a control alternative for assistive devices and has been tested with the JACO arm, an articulated assistive device of which the vocation is to help people living with upper-body disabilities in their daily life activities. The wireless prototype, the architecture of which is based on a 3-channel sEMG measurement system and a 915-MHz wireless transceiver built around a low-power microcontroller, uses low-cost off-the-shelf commercial components. The embedded controller is compared with JACOs regular joystick-based interface, using combinations of forearm, pectoral, masseter, and trapeze muscles. The measured index of performance values is 0.88, 0.51, and 0.41 bits/s, respectively, for correlation coefficients with the Fitts model of 0.75, 0.85, and 0.67. These results demonstrate that the proposed controller offers an attractive alternative to conventional interfaces, such as joystick devices, for upper-body disabled people using ATs such as JACO.

Improving cable driven parallel robot accuracy through angular position sensors

Conventionally, a cable driven parallel mechanism (CDPM) pose is obtained through the forward kinematics from measurements of the cable lengths. However, this estimation method can be limiting for some applications requiring more precision. This paper proposes to use cable angle position sensors in addition to cable length measurements in order to improve the accuracy of such mechanisms. The robot pose is first obtained individually by the cable length measurements and the cable angle position measurements. A data fusion scheme combining these two types of measurements is then proposed in order to improve the CPDM accuracy. Finally, simulations and experiments are presented in order to assess the benefits of using cable angle position sensors on the CDPM.

Improving the Forward Kinematics of Cable-Driven Parallel Robots Through Cable Angle Sensors

This paper presents a sensor fusion method that aims at improving the accuracy of cable-driven planar parallel mechanisms (CDPMs) and simplifying the kinematic resolution. While the end-effector pose of the CDPM is usually obtained with the cable lengths, the proposed method combines the cable length measurement with the cable angle by using a data fusion algorithm. This allows for a resolution based on the loop closure equations and a weighted least squares method. The paper first presents the resolution of the forward kinematics for planar parallel mechanisms using cable angle only. Then, the proposed sensor fusion scheme is detailed. Finally, an experiment comparing the different procedures for obtaining the pose of the CDPM is carried out, in order to demonstrate the efficiency of the proposed fusion method.

Kinova Modular Robot Arms for Service Robotics Applications

This article presents Kinovas modular robotic systems, including the robots JACO2 and MICO2, actuators and grippers. Kinova designs and manufactures robotics platforms and components that are simple, sexy and safe under two business units: Assistive Robotics empowers people living with disabilities to push beyond their current boundaries and limitations while Service Robotics empowers people in industry to interact with their environment more efficiently and safely. Kinova is based in Boisbriand, Quebec, Canada. Its technologies are exploited in over 25 countries and are used in many applications, including as service robotics, physical assistance, medical applications, mobile manipulation, rehabilitation, teleoperation and in research in different areas such as computer vision, artificial intelligence, grasping, planning and control interfaces. The article describes Kinovas hardware platforms, their different control modes position, velocity and torque, control features and possible control interfaces. Integration to other systems and application examples are also presented.

A multimodal adaptive wireless control interface for people with upper-body disabilities

Multimodal body-machine interfaces play an important role in providing severely impaired with interaction solutions that can adapt to their functional capacities [1]. This demonstration will allow the visitors to experience an intuitive and wearable control interface, designed for people with upper body disabilities, that translates head motion, shoulder elevation and surface electromyography into appropriate commands for controlling assistive devices, like robotic arms. Visitors will be invited to interact with JACO using the proposed interface, a 6 degree-of-freedom assistive robotic arm developed by Kinova Robotics, within a control task that will consist in moving items or stack objects at specific locations on a table.

Active stability observer using artificial neural network for intuitive physical human–robot interaction

Physical human–robot interaction may present an obstacle to transparency and operations’ intuitiveness. This barrier could occur due to the vibrations caused by a stiff environment interacting with the robotic mechanisms. In this regard, this article aims to deal with the aforementioned issues while using an observer and an adaptive gain controller. The adaptation of the gain loop should be performed in all circumstances in order to maintain operators’ safety and operations’ intuitiveness. Hence, two approaches for detecting and then reducing vibrations will be introduced in this study as follows: (1) a statistical analysis of a sensor signal (force and velocity) and (2) a multilayer perceptron artificial neural network capable of compensating the first approach for ensuring vibrations identification in real time. Simulations and experimental results are then conducted and compared in order to evaluate the validity of the suggested approaches in minimizing vibrations.

An anticipative kinematic limitation avoidance algorithm for collaborative robots: Three-dimensional case

This paper presents an anticipative robot kinematic limitation avoidance algorithm for collaborative robots. The main objective is to improve the performance and the intuitivity of physical human-robot interaction. Currently, in such interactions, the human user must focus on the task as well as on the robot configuration. Indeed, the user must pay a close attention to the robot in order to avoid limitations such as joint position limitations, singularities and collisions with the environment. The proposed anticipative algorithm aims at relieving the human user from having to deal with such limitations by automatically avoiding them while considering the users intentions. The framework developed to manage several limitations occurring simultaneously in three-dimensional space is first presented. The algorithm is then presented and detailed for each individual limitation of a spatial RRR serial robot. Finally, experiments are performed in order to assess the performance of the algorithm.