This study introduces an innovative autonomous control system for assistive mobile robots, focusing on users with significant physical disabilities. Leveraging vector fields and a modified A* algorithm, the system enhances the traditional approach to robotic wheelchair navigation. The methodology emphasizes autonomous navigation in familiar environments, integrating advanced Human-Machine Interfaces (HMIs) for user interaction and control. Experimental analysis demonstrates the system's superiority over conventional Proportional, Integral and Derivative (PID) controllers in terms of safety, adaptability, and user comfort. The solution effectively addresses the challenges of map maintenance, route planning, and dynamic obstacle avoidance, marking a significant advancement in assistive robotics.

Since the 1980s several works were published proposing solutions for users with severe mobility impairments that prevent them from operating motorized wheelchairs through mechanical joysticks. Such solutions commonly focus on assistive interfaces and wheelchair automation. The ultimate objective is to offer a comfortable and safe conduction no matter the user’s mobility impairments. This paper proposes a path tracking control algorithm for smart wheelchairs that minimizes or even eliminates the negative effects of caster wheels’ misalignments when the wheelchair changes its trajectory. These effects reflect on path deviations from the planned trajectory that demand a series of user interventions for path correction. Such interventions are inhibited when assistive interfaces are employed due to the low rate of commands these interfaces are able to produce. The proposed algorithm can be employed on wheelchairs with conventional designs and requires no installation of expensive sensors and actuators. As a result, issues related to cost, certification, and insurance are minimally impacted by the proposed path tracking solution.

This work presents the Wheelie, a computer program capable of detecting and translating facial expressions into commands to control equipment, such as wheelchairs or assistive robotic vehicles, using 3D technology. Every year, degenerative diseases and traumas put thousands of people into situations that inhibit them to control the joystick of a wheelchair using their hands. Most current technologies are considered invasive and uncomfortable such as those requiring the user to wear some body sensor to control the wheelchair. The Wheelie is a solution that does not require the user to wear body sensors, using instead a 3D camera pointing to the user's face. We call this solution as “the mathematics behind the smile” which is able to classify 9 facial expressions in real-time, such as smiles, kisses, and raised eyebrows that are translated into steering commands to the wheelchair (turn right, go forward, and so on). This work evaluates the use of facial expressions to drive commercially available wheelchairs over real life situations. A series of experiments were conducted in order to assess the efficiency of the command acquisition process and the user experience in driving a wheelchair through facial expressions.

This paper presents the evolution of a software platform for supporting experimentation in mobile robotics as part of teaching and researching activities. Starting with Web-based laboratories (WebLabs) in the early 2000s the platform kept evolving according to the networking and distributed computing trends since then. In addition to the physical resources managed by the platform, the platform now is able to manage a pool of virtual machines as resources for experimentation. This new class of resources brings the processing power as required by many modern mobile robotics applications. Virtual machines can be widespread on a cluster of processors, on a private cloud computing infrastructure, or on a public cloud computing service. Like any other resource managed by the platform the access to the virtual machines is subjected to user authentication and authorization. A mechanism of user authentication and authorization based on federated identities (single-sign-on) allows the sharing resources maintained by different administrative domains. The paper emphasizes the current stage of the platform and a case study in mobile robotics localization. Localization, as many other mobile robotics algorithms, can employ parallelism at the cluster and cloud levels in order to improve speed, reliability, and scaling.

The Internet of Things (IoT) is considered the driving force behind the next Internet revolution, with major technological and cultural impacts. Although the term “Internet of Things” has just recently became widespread, IoT relies on several well-established concepts, such as ad hoc and wireless sensor networks, ubiquitous computing, and Cyber-Physical Systems. However, there is still little consensus about how to turn the IoT vision into reality. This chapter presents a resource-oriented architecture for supporting IoT deployments. Resources (the “things”) are network-addressable objects connected to an IPv4 or IPv6 network core. Border routers in this network can act as gateways to the resources employing non-IP protocols such as wireless sensor protocols based on Bluetooth or ZigBee. The architecture allows resource advertisement and discovering in a totally decentralized way as well as access transparency under resource migration. The architecture relies on OSPF (Open Shortest Path First), a well-established Internet routing protocol, for network-wide resource advertisement. As in ordinary Internet routing, aggregation plays an important role in the proposed architecture. By aggregating resources, scalability and convergence of the resource advertisement process are both favored. A case study in ambient assistive living is presented in order to illustrate the architecture’s major components and functions.

This work presents a platform for the development of a functional prototype for assistive robotic vehicles supporting various control strategies in the context of a smart environment. The implemented framework allows an operator with a disability to interact with a smart environment by means of handfree devices (small movements of the face or limbs through Electromyograph (EMG), or Electroencephalograph (EEG), among others). The present work also details the integration and testing of four control strategies (manual control, shared control, point to go, and fully autonomous), giving the user the opportunity to choose among them based on the structure of the environment, personal preference, or capability. An intelligent assistive agent was integrated into the framework which helps the operator navigating the user interface and interacting with the environment. The controls performances for a common scenario are compared to validate the platform and compare the implemented navigation algorithms, and experimental results are presented and discussed.

This paper is presenting the ongoing work toward a novel driving assistance system of a robotic wheelchair, for people paralyzed from down the neck. The user's head posture is tracked, to accordingly project a colored spot on the ground ahead, with a pan-tilt mounted laser. The laser dot on the ground represents a potential close range destination the operator wants to reach autonomously. The wheelchair is equipped with a low cost depth-camera (Kinect sensor) that models a traversability map in order to define if the designated destination is reachable or not by the chair. If reachable, the red laser dot turns green, and the operator can validate the wheelchair destination via an Electromyogram (EMG) device, detecting a specific group of muscle's contraction. This validating action triggers the calculation of a path toward the laser pointed target, based on the traversability map. The wheelchair is then controlled to follow this path autonomously. In the future, the stream of 3D point cloud acquired during the process will be used to map and self localize the wheelchair in the environment, to be able to correct the estimate of the pose derived from the wheel's encoders.

Patients with neurological disorders are often required to cope with off-putting and longer rehabilitation process and some of them become wheelchair users with severe restriction on the arms and hands skill. Many studies have proposed solutions for autonomous or semi-autonomous powered wheelchairs focusing on navigation problems. In this project, we are interested in bringing up a planning shared control model for wheelchair to be used by people with disabilities or inexpert users to move from a room to another into an indoor place. The main objective is to reduce the effort of the users on controlling the wheelchair on these dedicated paths. This leads us to improve an algorithm of shared control to use other data sources such as agendas, travelled path, pose of the wheelchair, and time of day to infer patterns of conduction, in order to anticipate the conductor's intentions, for example, his/her intended path or final destination and to communicate with the user providing smart suggestions during the process. This "anticipative shared control" is of prime importance when tiring human-machine interfaces such as BCI, eye-tracking or EMG are being employed.

This paper presents a mobile robot planning approach for solving the problem of indoor cleaning tasks.In general, the robot must find its pose first and only then move to the final destination to cleaning out.Our model works with a multi-level planning approach where the mission is treated online only.The user sets up the cleaning or the robot uses an agenda with predefined set of missions.POMDP plans are created for the localization, using the map and the robot specifications.The plans are created offline only once and used indefinitely regardless of missions.We will show the multi-level planning process where the robot finds its pose in the high level of representative rooms and then moves to the lower level to finding its precise pose.We demonstrated the approach with experiments on both simulator and real robot.The multi-level planning allowed the robot to find its pose and fulfill the tasks of the agenda faster while keeping the precision.

The main objective of a brain-computer interface (BCI) is to create alternative communication channels between the brain and a machine using information from cerebral responses. Among the possible paradigms to design a BCI system, this work focuses on Steady-State Visually Evoked Potentials (SSVEP). SSVEP are brain responses synchronized with fast repetitive external visual stimuli. The SSVEP-BCI system is able to meet many of the requirements of a strict BCI, but still needs to reduce the influence of noise on the Electroencephalogram (EEG) signal in order to improve its performance. In this paper, a novel SSVEP-BCI system is presented and analyzed in detail. The system is based on three pillars: spectrum estimation, systematic feature selection - for which different heuristics were proposed here -, and linear classification.

The feature extraction stage is one of the main tasks underlying pattern recognition, and, is particularly important for designing Brain-Computer Interfaces (BCIs), i.e. structures capable of mapping brain signals in commands for external devices. Within one of the most used BCIs paradigms, that based on Steady State Visual Evoked Potentials (SSVEP), such task is classically performed in the spectral domain, albeit it does not necessarily provide the best achievable performance. The aim of this work is to use recurrence-based measures in an attempt to improve the classification performance obtained with a classical spectral approaches for a five-command SSVEP-BCI system. For both recurrence and spectral spaces, features were selected using a cluster measure defined by the Davies-Bouldin index and the classification stage was based on linear discriminant analysis. As the main result, it was found that the threshold e of the recurrence plot, chosen so as to yield a recurrence rate of 2.5 %, defined the key discriminant feature, typically providing a mean classification error of less than 2 % when information from 4 electrodes was used. Such classification performance was significantly better than that attained using spectral features, which strongly indicates that RQA is an efficient feature extraction technique for BCI.

This paper presents a hybrid optimization model that allows a cloud service provider to establish virtual machine (VM) placement strategies for its data centers in such a way that energy efficiency and network quality of service are jointly optimized. Usually, VM placement is an activity not fully integrated with network operations. As such, the VM placement strategy does not take into account the impact it produces on the network performance in terms of quality of service parameters such as packet losses and traffic delays. The proposed strategy allows cloud providers to reach a balance between the energy efficiency of their infrastructures and the network quality of service they offer to their customers. The proposed strategy has high potential for parallelism making it feasible for the energetic optimization of large cloud infrastructures. In addition, the strategy allows network operations practices such as constraint-based routing be incorporated into the VM placement process.

This paper presents a planning approach for solving the global localization problem using an arrangement of rooms to compress the original map. The approach is based on architectural design features of the building such as walls and doors to help the robot on finding the best route to go. Lighter POMDP plans are generated only for representative rooms of the environment, decreasing size of the set of possible states. The plans are created offline only once and used indefinitely regardless of missions combining them online. The plan only requires as input, the environment map and the robot actions and possible observations. We demonstrate the single level approach and the map decomposition with experiments on both V-REP Simulator and the Pioneer 3DX robot. This approach allows the robot to perform both the localization and tasking in a large-scale environment.

Technologies such as Brain-Computer Interface (BCI) and Electromyography (EMG) allow people with limited mobility to interact with devices such as computers, home appliances and mobile robots. However, low cost BCI and EMG have not matured yet. Moreover, these technologies present relative low signal-to-noise ratio and classification accuracy. In case of employing BCI or EMG to manually control a mobile robot, a shared control must be inserted in the loop control to compensate misinterpreted commands. This paper presents a novel shared control approach based on vector fields for the manual navigation of assistive mobile robots. Unlike other approaches which take full control of the robot in certain situations, this technique allows full control to the user. Also, this approach reduces interventions to correct the navigation route caused by wrong classification of commands issued by the user. Results show that it is a simple, fast and effective technique.

A capacidade motora de um individuo, como caminhar, pode ser facilmente comprometida por uma variedade de fatores, como traumatismo na coluna, acidentes vasculares cerebrais, amputação de membros, esclerose múltipla, etc. Algumas tecnologias podem ser utilizadas para auxiliar estes indivíduos melhorando sua mobilidade e qualidade de vida. Estas tecnologias são comumente chamadas de tecnologias assistivas e a robótica móvel se encaixa bem nesta categoria. Este trabalho apresenta uma solução de baixo custo computacional que utiliza sensores sEMG (surface Electromyography) como interface do sistema assistivo onde todo o processamento dos sinais e controle do robô esta embarcado em um smartphone com o sistema operacional Android. O sistema também oferece uma proteção contra colisões simples baseado na detecção de obstáculos

As mobile robots become part of large networked infrastructures, concerns about the integration of robots into distributed applications arise. Due to the predominance of the Web protocols, it is natural mobile robots to employ such protocols for interacting over the network with other applications. Information transfer protocols such as HTTP and SOAP allow parameters to be passed in their messages, leading to a client-server interaction style based on RPC (Remote Procedure Call). As such, many robotic frameworks today employ this interaction style. An alternate interaction style is REST (Representational State Transfer). In this style, instead of focusing on operations (procedures) as in RPC, the focus is on resources. In this paper we present a RESTful (REST compliant) architecture and its implementation for mobile robots. This architecture is compared with a classical RPC-based architecture in order to illustrate the benefits of REST in the field of network robotics based on open standards.

Create alternative communication channels between the brain and a machine using the information of conventional cerebral responses, such as those produced by visual stimuli or motor imagination, is the main objective of the research area known as Brain-Computer Interface (BCI). Some BCI systems is based on Steady-State Visual Evoked Potentials (SSVEP). SSVEP are brain responses that are synchronized with fast repetitive external visual stimuli. The SSVEP-BCI system is able to meet many of the requirements of a strict BCI, but still needs to reduce the influence of noise in order to improve its performance. A SSVEP-BCI system is proposed in this paper, being its performance evaluated with different methods of signal processing.