Evaluating the Impact of the Number of Access Points in Mobile Robots Localization Using Artificial Neural Networks

Localization is information of fundamental importance to carry out various tasks in the mobile robotic area. The exact degree of precision required in the localization depends on the nature of the task. The GPS provides global position estimation but is restricted to outdoor environments and has an inherent imprecision of a few meters. In indoor spaces, other sensors like lasers and cameras are commonly used for position estimation, but these require landmarks (or maps) in the environment and a fair amount of computation to process complex algorithms. These sensors also have a limited field of vision. Currently, Wireless Networks (WN) are widely available in indoor environments and can allow efficient global localization that requires relatively low computing resources. However, the inherent instability in the wireless signal prevents it from being used for very accurate position estimation. The growth in the number of Access Points (AP) increases the overlap signals areas and this could be a useful means of improving the precision of the localization. In this paper we evaluate the impact of the number of Access Points in mobile nodes localization using Artificial Neural Networks (ANN). We use three to eight APs as a source signal and show how the ANNs learn and generalize the data. Added to this, we evaluate the robustness of the ANNs and evaluate a heuristic to try to decrease the error in the localization. In order to validate our approach several ANNs topologies have been evaluated in experimental tests that were conducted with a mobile node in an indoor space.

Improving backdrivability in geared rehabilitation robots

Many rehabilitation robots use electric motors with gears. The backdrivability of geared drives is poor due to friction. While it is common practice to use velocity measurements to compensate for kinetic friction, breakaway friction usually cannot be compensated for without the use of an additional force sensor that directly measures the interaction force between the human and the robot. Therefore, in robots without force sensors, subjects must overcome a large breakaway torque to initiate user-driven movements, which are important for motor learning. In this technical note, a new methodology to compensate for both kinetic and breakaway friction is presented. The basic strategy is to take advantage of the fact that, for rehabilitation exercises, the direction of the desired motion is often known. By applying the new method to three implementation examples, including drives with gear reduction ratios 100-435, the peak breakaway torque could be reduced by 60-80%.

A novel paradigm for patient-cooperative control of upper-limb rehabilitation robots

Early intervention and intensive therapy improve the outcome of neuromuscular rehabilitation. There are indications that where a patient is motivated and premeditates their movement, the recovery is more effective. Therefore, a strategy for patient-cooperative control of rehabilitation devices for upper extremities is proposed and evaluated. The strategy is based on the minimal intervention principle allowing an efficient exploitation of task space redundancies and resulting in user-driven movement trajectories. The patient's effort is taken into consideration by enabling the machine to comply with forces exerted by the user. The interaction is enhanced through a multimodal display and a virtually generated environment that includes haptic, visual and sound modalities.