José María Cañas Plaza

WiFi localization methods for autonomous robots

This paper compares two methods to estimate the position of a mobile robot in an indoor environment using only odometric calculus and the WiFi energy received from the wireless communication infrastructure. In both cases we use a well-known probabilistic method based on the Bayes rule to accumulate localization probability as the robot moves on with an experimental WiFi map, and with a theoretically built WiFi map. We will show several experiments made in our university building to compare both methods using a Pioneer robot. The two major contributions of the presented work are that the self-localization error achieved with WiFi energy is bounded, and that no significant degradation is observed when the expected WiFi energy at each point is taken from radio propagation model, instead of an a priori experimental intensity map of the environment.

Social robots in advanced dementia.

Aims: Pilot studies applying a humanoid robot (NAO), a pet robot (PARO) and a real animal (DOG) in therapy sessions of patients with dementia in a nursing home and a day care center. Methods:In the nursing home, patients were assigned by living units, based on dementia severity, to one of the three parallel therapeutic arms to compare: CONTROL, PARO and NAO (Phase 1) and CONTROL, PARO, and DOG (Phase 2). In the day care center, all patients received therapy with NAO (Phase 1) and PARO (Phase 2). Therapy sessions were held 2 days per week during 3 months. Evaluation, at baseline and follow-up, was carried out by blind raters using: the Global Deterioration Scale (GDS), the Severe Mini Mental State Examination (sMMSE), the Mini Mental State Examination (MMSE), the Neuropsychiatric Inventory (NPI), the Apathy Scale for Institutionalized Patients with Dementia Nursing Home version (APADEM-NH), the Apathy Inventory (AI) and the Quality of Life Scale (QUALID). Statistical analysis included descriptive statistics and non-parametric tests performed by a blinded investigator. Results: In the nursing home, 101 patients (Phase 1) and 110 patients (Phase 2) were included. There were no significant differences at baseline. The relevant changes at follow-up were: (Phase 1) patients in the robot groups showed an improvement in apathy; patients in NAO group showed a decline in cognition as measured by the MMSE scores, but not the sMMSE; the robot groups showed no significant changes between them; (Phase 2) QUALID scores increased in the PARO group. In the day care center, 20 patients (Phase 1) and 17 patients (Phase 2) were included. The main findings were: (Phase 1) improvement in the NPI irritability and the NPI total score; (Phase 2) no differences were observed at follow-up.

Integrating Behaviors for Mobile Robots: an Ethological Approach

Robots available nowadays in the everyday markets can be divided into two major groups. First, those that are oriented to a single task, as vacuum cleaners (roomba1, robocleaner2, ...), lawn mowers (automower3, robomower4, ...), etc; and second, those oriented to various tasks, as robotic pets (Aibo5, Necoro6,... ), museum guiders, or research platforms (Pioneer, Koala, etc.). In order to get service robots or personal assistants we need to improve the abilities of the second ones. These abilities have to do with their ability to smartly combine their basic skills to obtain behaviors that are more complex. The generation of autonomous behavior is a very complex issue. Within a multi-goal system, like service robots, the focus is more on the integration than on the control of perception algorithms. The organization becomes the critical issue; robustness and flexibility are key features. As animals can do it, we can see no compelling reason why robots could not, but more research on robot architectures is needed to reach an acceptable performance. Several paradigms have been historically proposed for behaviour generation in robots. These paradigms are also known as architectures. Dynamic and uncertain environments forced the evolution from symbolic AI (Nilsson 1984) to reactive and behavior based systems (Brooks 1986; Arkin 1989). The behaviors based systems adapt smoothly. They have no anticipation and no state, but they have shown poor scalability for complex systems. Hybrid architectures have been predominant since mid 90s (Simmons 1994; Konolige 1998; Bonasso 1997), mainly those three-tiered ones that add two layers to behavior based ones, usually a sequencer, and a deliberator. Several architectures have been explored after the hybrid three-tiered architectures became the de facto standard. In particular, many reviews of the hierarchy principle have been proposed in last years (Arkin 2003, Saffiotti 2003, Nicolescu 2002, Behnke 2001, Bryson 2001), trying to overcome subsumption limitations. Our contribution in this area is a novel hierarchical approach named JDE. This new hierarchical approach, ethologically inspired, is based on the selective activation of

Fine 3D Path Following of a Quadcopter

This paper addresses the design and implementation of a path following controlling system for a drone which relies on 3D localization by visual markers. It has been designed only for indoor flights. Special attention is paid to accuracy of the position estimation algorithm, robustness of the path following controller and real time operation. The path following system is composed of two components, one responsible of the image analysis and 3D pose estimation and another responsible of the drone navigation. It has been experimentally validated both in Gazebo simulator and in a real drone.