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.

Opportunistic Routing for Multi-flow Video Dissemination over Flying Ad-Hoc Networks

A reliable and robust routing service for Flying Ad-Hoc Networks (FANETs) must be able to adapt to topology changes. User experience on watching live video sequences must also be satisfactory even in scenarios with buffer overflow and high packet loss ratio. In this paper, we introduce a Cross-layer Link quality and Geographical-aware beaconless opportunistic routing protocol (XLinGO). It enhances the transmission of simultaneous multiple video flows over FANETs by creating and keeping reliable persistent multi-hop routes. XLinGO considers a set of cross-layer and human-related information for routing decisions, as performance metrics and Quality of Experience (QoE). Performance evaluation shows that XLinGO achieves multimedia dissemination with QoE support and robustness in a multi-hop, multi-flow, and mobile network environments.

A Comparative Analysis of Beaconless Opportunistic Routing Protocols for Video Dissemination over Flying Ad-Hoc Networks

A reliable and robust routing service for Flying Ad-Hoc Networks (FANETs) must be able to adapt to topology changes, and also to recover the quality level of the delivered multiple video flows under dynamic network topologies. The user experience on watching live videos must also be satisfactory even in scenarios with network congestion, buffer overflow, and packet loss ratio, as experienced in many FANET multimedia applications. In this paper, we perform a comparative simulation study to assess the robustness, reliability, and quality level of videos transmitted via well-known beaconless opportunistic routing protocols. Simulation results shows that our developed protocol XLinGO achieves multimedia dissemination with Quality of Experience (QoE) support and robustness in a multi-hop, multi-flow, and mobile networks, as required in many multimedia FANET scenarios.

Cross-Layer Optimizations for Multimedia Distribution over Wireless Multimedia Sensor Networks and Flying Ad-Hoc Networks with Quality of Experience Support

The proliferation of multimedia content and the demand for new audio or video services have fostered the development of a new era based on multimedia information, which allowed the evolution of Wireless Multimedia Sensor Networks (WMSNs) and also Flying Ad-Hoc Networks (FANETs). In this way, live multimedia services require real-time video transmissions with a low frame loss rate, tolerable end-to-end delay, and jitter to support video dissemination with Quality of Experience (QoE) support. Hence, a key principle in a QoE-aware approach is the transmission of high priority frames (protect them) with a minimum packet loss ratio, as well as network overhead. Moreover, multimedia content must be transmitted from a given source to the destination via intermediate nodes with high reliability in a large scale scenario. The routing service must cope with dynamic topologies caused by node failure or mobility, as well as wireless channel changes, in order to continue to operate despite dynamic topologies during multimedia transmission. Finally, understanding user satisfaction on watching a video sequence is becoming a key requirement for delivery of multimedia content with QoE support. With this goal in mind, solutions involving multimedia transmissions must take into account the video characteristics to improve video quality delivery. The main research contributions of this thesis are driven by the research question how to provide multimedia distribution with high energy-efficiency, reliability, robustness, scalability, and QoE support over wireless ad hoc networks. The thesis addresses several problem domains with contributions on different layers of the communication stack. At the application layer, we introduce a QoE-aware packet redundancy mechanism to reduce the impact of the unreliable and lossy nature of wireless environment to disseminate live multimedia content. At the network layer, we introduce two routing protocols, namely video-aware Multi-hop and multi-path hierarchical routing protocol for Efficient VIdeo transmission for static WMSN scenarios (MEVI), and cross-layer link quality and geographical-aware beaconless OR protocol for multimedia FANET scenarios (XLinGO). Both protocols enable multimedia dissemination with energy-efficiency, reliability and QoE support. This is achieved by combining multiple cross-layer metrics for routing decision in order to establish reliable routes.

Context-aware Adaptation Mechanism for Video Dissemination over Flying Ad-Hoc Networks

The user experience on watching live video se- quences transmitted over a Flying Ad-Hoc Networks (FANETs) must be considered to drop packets in overloaded queues, in scenarios with high buffer overflow and packet loss rate. In this paper, we introduce a context-aware adaptation mechanism to manage overloaded buffers. More specifically, we propose a utility function to compute the dropping probability of each packet in overloaded queues based on video context information, such as frame importance, packet deadline, and sensing relevance. In this way, the proposed mechanism drops the packet that adds the minimum video distortion. Simulation evaluation shows that the proposed adaptation mechanism provides real-time multimedia dissemination with QoE support in a multi-hop, multi-flow, and mobile network environments.