Assistive devices are to be designed with the objective of use in daily-life as well as broad adoption by end users. In this context, it is necessary to tackle usability challenges by properly detecting and acting in accordance to user intents while minimizing the device installation complexity as well. In the case of physical assistive devices, using force/torque sensors is advantageous to detect user intent compared to EMG interfaces, but it remains difficult to correctly translate the detected intent into actuator motions. Focusing on upper-limb assistive robots, the user voluntary force is commonly used with a controller based on an admittance approach which leads to relatively poor reactivity and requires the user to develop force throughout the movement which can lead to fatigue, particularly for people with upper-limb impairments. This work proposes a Force-Triggered (FT) controller which can initiate and maintain movement only from short force impulses. The user voluntary forces are retrieved from total interaction forces by subtracting the passive component measured beforehand during a calibration phase. This paper presents the design of the proposed FT controller and its preliminary testing on pick-and-place tasks compared to an admittance strategy. This experiment was performed with one participant without impairment, equipped with an upper-limb exoskeleton prototype designed from recommendations of physical medicine therapists. This preliminary work highlights the potential of the proposed FT controller. Also, it provides directions for future work and clinical trials with end-users to assess the proposed FT approach usability while used alone or in the form of an hybrid controller between FT and admittance strategies.

This paper presents a generic power wheelchair dynamic model. As a first contribution, this paper proposes to use a generic model composed of a geometric model and a lumped model in order to be compliant with a wide range of existing commercially available wheelchairs. In this model, a set of essential parameters are enough to accurately replicate the dynamic behavior of a wheelchair. As a second contribution, this paper presents an identification method of a n-wheel type power wheelchair. The presented model is restricted to the sagittal plane only, which is sufficient to study the reliability of the identification and validation methods. Moreover, a Motion Cueing Algorithm based on the proposed model controls a simulator mechanical platform. The generic model has been then validated through a user study with 18 able-bodied participants evaluating the self-motion perception with our multisensory power wheelchair driving simulator. Results show that the simplified model is sufficient to provide accurate sensations to the user with respect to their experience while driving a power wheelchair.

Smart powered wheelchairs can increase mobility and independence for people with disability by providing navigation support. This support can be supplied in the form of autonomous or semi-autonomous obstacle avoidance systems. However, for rehabilitation or learning purposes, it would be of great benefit for wheelchair users to have a better understanding of the surrounding environment while driving. Therefore, another way of providing navigation support is to communicate information through a dedicated and adapted feedback interface. We here propose a framework in which feedback is provided by sending forces through the wheelchair controller as the user steers the wheelchair. This solution is based on a low complex optimization framework able to perform smooth trajectory correction and to provide obstacle avoidance. The impact of the proposed haptic guidance solution on user driving performance was assessed during this pilot study for validation purposes through an experiment with 4 able-bodied participants. They were asked to drive a power wheelchair on an obstacle course with and without activation of the force feedback. Results of this pilot study showed that the number of collisions significantly decreased while force feedback was activated, thus validating the proposed framework.

This paper presents closed-form solutions for the inverse and direct dynamic models of the Gough-Stewart parallel robot. The models are obtained in terms of the Cartesian dynamic model elements of the legs and of the Newton-Euler equation of the platform. The final form has an interesting and intuitive physical interpretation. The base inertial parameters of the robot, which constitute the minimum number of inertial parameters, are explicitly determined. The number of operations to compute the inverse and direct dynamic models are given.

This paper presents the experimental identification of the dynamic parameters of the Orthoglide, a 3-DOF parallel mechanism. The dynamic identification model is based on the inverse dynamic model, which is linear in the parameters. The model is computed in a closed form in terms of the Cartesian dynamic model elements of the legs and of the Newton-Euler equation of the platform. The base inertial parameters of the robot, which constitute the identifiable parameters, are given.

Presents a closed form solution for the inverse and direct dynamic models of a Gough-Stewart platform. The models are calculated in terms of the Cartesian dynamic model elements of the legs and of the Newton-Euler equation of the platform. The final form has an interesting physical interpretation, which can be used to derive the dynamic models of other parallel structures.