People with severe disabilities often rely on power wheelchairs for moving around. However, if their driving abilities are affected by their condition, driving a power wheelchair can become very dangerous, both for themselves and the surrounding environment. This paper proposes the use of wearable vibrotactile haptics for wheelchair navigation assistance. We use one or two haptic armbands, each composed of four evenly-spaced vibrotactile actuators, for providing different navigation information to power wheelchair users. With respect to other available solutions, our approach provides rich navigation information while always leaving the patient in control of the wheelchair motion. Moreover, our armbands can be easily adapted for different limbs and can be used by all those patients who are unable to safely maneuver a kinesthetic interface. The results of two human subjects studies show the viability and effectiveness of the proposed technique with respect to not providing any environmental cue. Collisions were reduced by 49% when using the vibrotactile armbands. Moreover, most subjects expressed a preference for receiving haptic feedback and found the armbands comfortable to wear and use.

This article presents progress made towards implementing a shared control framework for a smart wheelchair based upon stochastic dynamic programming. First, we describe the mechanical, electrical and software design process of our instrumented wheelchair platform. Then, we detail a deterministic control-oriented model of the wheelchair motion dynamics using Euler-Lagrange equations. This is followed by the development of a stochastic model of the human driver's intention using Markov chain. Finally, we end our contribution with a discussion of the future implementation and evaluation of our stochastic dynamic programming.

The analysis of chaotic components in movements during hands tremor and more classic analysis of the movements is considered in relation to its application to diagnosis and rehabilitation techniques. Fuzzy methods are used to characterize the movements and to assess their normality. Visual and auditory feedback is provided to help controlling the movements in a rehabilitation system that incorporates elements of virtual reality.

In this paper we focus on the supervisory control problem of a parallel hybrid electric vehicle (HEV): minimize fuel consumption while ensuring self-sustaining State-of-Charge (SoC). We reapply the state of the art methodology by comparing optimal results of Dynamic Programming (DP) against a realtime control candidate. After careful selection, we opted for an Equivalent Consumption Minimization Strategy (ECMS) based approach for the following reasons: (i) results are quite remarkable with less than 5% fuel usage increase when compared to DP; (ii) simple and intuitive tuning of control parameters; (iii) readily usable for code generation (prototyping).Topics that distinguish this article from others in the literature include: (i) the usage of trapezoidal rule of integration implementing DP and ECMS; consequently, the offline simulation results are intended to be more precise and representative when compared against the more common, often used rectangular rule; (ii) a particular post-processing procedure of the recorded driving cycle data based on physical interpretation; it allows consistent offline simulations with quite high sampling period (in the order of seconds); (iii) tuning of control parameters in such a way that control system is robust towards new, unknown, unpredictable but closely resembling driving cycles.In particular, we focus on the supervisory control of a forklift truck. The real-time control is able to compute: (i) the power split (i.e. a balanced usage between an internal combustion engine and a supercapacitor); (ii) the drivetrain control (i.e. automatic gear shifting and clutching). Numerous numerical implementation issues are discussed along our presentation.

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This paper refines a physically-inspired model governing the dynamic motion of a vehicle. We present a method used to perform experimental parameter calibration, and then use this model to build an observer (an extended Kalman filter). Experimental results with a robotic vehicle fitted with a prototype kit focus on recovering the truthful real-world information in the context of systematic errors (a faulty wheel encoder sensor), randomly occurring errors (a faulty ultrasonic sensor) and simplifying model assumptions (e.g. usage of two identical motors). We show that our model-based approach is able to perform reasonably well even under these extreme circumstances.

This paper addresses the problem of safe navigation in an environment with randomly placed static obstacles. We convert a commercial powered wheelchair into a semiautonomous vehicle with limited sight (environment awareness), by instrumenting it using off-the-shelf ultrasonic sensors and associated electronic boards (Arduino). In the continuity of our previous work where we had used stochastic dynamic programming to formulate optimization problems which led to relatively large size look-up tables that can be used as supervisory control, here we propose to extract rules using those results (that data). The advantage of this approach is a lowcomputational cost for future online implementation, and the drawback is a suboptimal policy. The feasibility is assessed by running simulations in a fairly realistic environment (Unity3D).

In this article we design a physically-inspired model-based assist-as-needed semi-autonomous control (ASC) algorithm to address the problem of safely driving a vehicle (a power wheelchair) in an environment with static obstacles. Once implemented online, the proposed algorithm requires limited computing power and relies on pre-computed (offline) maps (look-up tables). These are readily available by implementing policy iteration that minimizes the expected time to termination (safely stopping near an obstacle), by taking into account: (i) the vehicle dynamics; (ii) the drivers' intention modeled as three separate stochastic processes. We call them the expert driver, the naughty child and the blind driver models.A study with healthy participants confirmed that ASC outperforms a baseline rule-based control (a statistically significant result).