The four papers encompassing this special section on Robot Mediated Neuro-motor Therapy introduce a new area for robotics in the healthcare industry, that of assisting in the delivery of neuro-motor therapies, for patients recovering from a stroke. As life expectancy increases, so do problems associated with old age. In the paper by Krebs et al. the authors not only summarise the need for action in stroke rehabilitation but also makes a useful distinction between assistive technologies that provide enhanced abilities for the patient, and therapy tools for the clinician, that seek to enhance the patients recovery.

Robot-aids or Rehabilitators are our chosen neologism to name a new class of robotic devices that represent a substantially departure from prior applications of robotics in rehabilitation. Rather than use robotics as an assistive technology for a disabled individual, we envision robots and computers as supporting and enhancing the productivity of clinicians in their efforts to facilitate a disabled individual's recovery. In this paper, we attempt a brief overview of our work in what promises to be a ground breaking field. We discuss the concept of robot-aided neuro-rehabilitation as a means to deliver therapy, measure patient performance, and also as a design tool. To illustrate the broad spectrum of neurological diseases that this technology might impact, we will illustrate each case with a different pathology, namely cerebral vascular accident (CVA – also known as stroke), Parkinson's disease (PD), and cerebral palsy (CP).

Hemiplegia, affecting approximately 75% of all stroke survivors, is a common neurological impairment that results in upper and lower limb sensory and motor deficits. Recovery of coordinated movement of both upper limbs is important for bilateral function and promotes personal independence. This paper describes the philosophy and design of Driver's Simulation Environment for Arm Therapy, a one-degree-of-freedom robotic device that uses a modified Constraint-Induced therapy paradigm to promote coordinated bilateral movement in the upper limbs. Baseline force and tracking data for four neurologically unimpaired subjects who completed bilateral and unilateral steering with the impaired arm using the device are presented.

We are developing robotic devices for locomotion training after spinal cord injury. In this paper, we compare two approaches to controlling and quantifying bipedal stepping of spinal rats with robots. In the first approach, the rats stepped on a physical treadmill with robot arms attached to their lower shanks. In the second, the rats stepped on a virtual treadmill generated by the robots. The rats could step on the virtual treadmill, but stepping was more consistent, step height greater, and interlimb coordination improved on the physical treadmill. Implications for the role of sensory input in the control of locomotion and the design robotic of step trainers are discussed.

The Rehabilitation Technologies Division of Applied Resources Corp. (RTD-ARC) has engaged in a Phase I effort to commercialize a robotic bi-manual therapy machine for use in stroke rehabilitation, in cooperation with the VA Rehabilitation R&D Center in Palo Alto. The robotic therapy device, called ARCMIME here in order to differentiate it from its clinical predecessor, has the potential to improve rehabilitation outcomes significantly for individuals who have upper limb impairments due to stroke and other brain injuries.

This paper describes design considerations and clinical outcomes with regards to the Phase I system. It was found that the kinematically simpler system adequately replicated the data outcomes of the more sophisticated PUMA-based experimental test rig.

This paper intends to contribute to the study of dynamically balanced legged robots. A real-time applicable control algorithm for a planar one-legged robot is developed, which allows for locomotion on an irregular terrain. The simulated model consists of an articulated leg and a body, vertically placed upon the leg. During the stance phase the leg is supported by a massless foot. The algorithm is based on the choice of a number of objective locomotion parameters which can be changed from one hop to another. From a chosen initial configuration the robot is able to transfer to a chosen end configuration, while simultaneously controlling its forward velocity, its step length and its stepping height. The foot is thus being placed exactly on a chosen foothold. To reach this goal, the actuators track polynomial functions. The calculation of these functions is based on the objective parameters, and takes into account the constraints acting on the robot. These constraints result from the fact that during flight the center of gravity of the robot tracks a parabolic trajectory, and that the angular momentum with respect to the center of gravity is conserved. Writing the angular momentum constraint in a Caplygin form is the key to the algorithm. Promising simulation results for the algorithm are shown for two different experiments.

In path planning design, potential fields can introduce force constraints to ensure curvature continuity of trajectories and thus facilitate path-tracking design. The parametric thrift of fractional potentials permits smooth variations of the potential in function of the distance to obstacles without requiring design of geometric charge distribution. In the approach we use, the fractional order of differentiation is the risk coefficient associated to obstacles. A convex danger map towards a target and a convex geodesic distance map are defined. Real-time computation can also lead to the shortest minimum danger trajectory, or to the least dangerous of minimum length trajectories.

This paper presents the implementation of an explicit model reference adaptive control (MRAC) for position tracking of a dynamically unknown robot. An auto regressive exogenous (ARX) model is chosen to define the plant model and the control input is optimised in a H2 norm to reduce computational time and to simplify the algorithm. The theory of MRAC falls into a description of the various forms of controllers and parameter estimation techniques, therefore, applications may require very complicated solution methods depending on the selected laws. However, in this study, the proposed MRAC shows that applications may be as easy as classical control methods, such as PID, by guaranteeing the stability and achieving the convergency of the plant parameters. Despite the selected simple control model, simple optimisation method and drawbacks of the robot the experimental results show that MRAC provides an excellent position tracking compared with conventional control (PID). Many experimental implementations have been done on the robot and one of them is included in the paper.

This paper describes the use of a hybrid system comprising a multi-layer perceptron and a Petri Net model to control the navigation of multiple mobile robots in an unknown and cluttered environment. The multi-layer perceptron is trained with examples representing the typical static obstacles to be encountered by the robots and the evasive actions to be taken. The Petri Net model embodies the rules describing how the robots should move in order to avoid colliding against one another. The hybrid system has been implemented in simulation software as well as in actual mobile robots. The paper presents the results of simulation and practical tests that demonstrate the ability of groups of robots incorporating the proposed navigation system to negotiate various types of obstacles and find targets successfully.

Though many research efforts have been devoted to developing robot controllers with uncertainties in the robot dynamics, most research so far has assumed that the model of actuator is known exactly. In implementing the robot controllers, calibration is necessary to identify the exact parameters of the actuator model. When there is an error in calibrating or modeling, it is unknown whether the stability of the system could still be guaranteed. In addition, the actuator model could change as the temperature varies due to overheating of the motor or changes in the ambient temperature. To overcome this problem, we propose in this paper, a task-space controller for the regulation of the robot with an approximate actuator model. We consider second-order dynamics of electric actuators with negligible armature inductances. Sufficient conditions for choosing the feedback gains are presented to guarantee stability.