Mobility loss has a major impact on autonomy. Smart rollators have been proposed to enhance human abilities when conventional devices are not enough. Many human–robot interaction systems have been proposed in the last decade in this area. Comparative analysis shows that mechanical issues aside, they mainly differ in first, equipped sensors and actuators; second, input interface; third, operation modes, and fourth adaptation capabilities. This article presents a review and a tentative taxonomy of approaches during the last 6 years. In total, 92 papers have been reviewed. We have discarded works not focused on humanrobot interaction or focused only on mechanical adaptation. A critical analysis is provided after the review and classification, highlighting systems tested with their target population.

Telepresence robots have been recently used for Comprehensive Geriatric Assessment (CGA). Since the robot can not track a person continuously, there are several strategies to decide when to check them, from cyclic checks to simple requests from users and/or caregivers. In order to adapt to the user needs and condition, it is preferable to perform CGA as soon as regularities appear. However, this requires detection of potential issues in users to offer immediate service. In this work we propose a new low cost force sensor system to detect user's condition and attract attention of CGA robots, so they can perform a full examination on a need basis. The main advantages of this system are: i) it can be attached to any standard commercial cane; ii) its power consumption is very reduced; and iii) it provides continuous information as long as the user walks. It has been tested with several elderly volunteers in care facilities. Results have proven that the sensor readings are indeed correlated with the users' condition.

Unmanned aerial vehicles (UAVs) are a relatively new technology. Their application can often involve complex and unseen problems. For instance, they can work in a cooperative-based environment under the supervision of a ground station to speed up critical decision-making processes. However, the amount of information exchanged among the aircraft and ground station is limited by high distances, low bandwidth size, restricted processing capability, and energy constraints. These drawbacks restrain large-scale operations such as large area inspections. New distributed state-of-the-art processing architectures, such as fog computing, can improve latency, scalability, and efficiency to meet time constraints via data acquisition, processing, and storage at different levels. Under these amendments, this research work proposes a mathematical model to analyze distribution-based UAVs topologies and a fog-cloud computing framework for large-scale mission and search operations. The tests have successfully predicted latency and other operational constraints, allowing the analysis of fog-computing advantages over traditional cloud-computing architectures.

Fall risk assessments provide a useful tool to prevent morbidity and mortality provoked by falls. Nowadays, these assessments are usually performed manually by the medical staff. This approach has three main drawbacks: (i) it is time consuming, so it is only performed a few times per volunteer during their rehabilitation process; (ii) it requires supervision by medical staff, so assessment at home or preferred environments is not feasible; and (iii) fall risk is evaluated in a global way, so imminent fall risk is not available for decision making in assistive navigation. In this paper we propose an imminent fall risk estimator for rollator's users that can be automatically obtained on the fly. Its main advantages are: (i) it can be used in everyday conditions in any environment; (ii) it does not require assistance of medical staff; and (iii) it is suitable for a variety of users with minimal configuration changes. We have validated our estimator with a set of volunteers (n=10) presenting different physical and cognitive disabilities. Although the number of volunteer is limited, results show that our estimator is coherent to two traditional, well accepted assessments: the Tinetti Mobility Test and the walking speed.

Shared control is a strategy used in assistive platforms to combine human and robot orders to achieve a goal. Collaborative control is a specific shared control approach, in which user's and robot's commands are merged into an emergent one in a continuous way. Robot commands tend to improve efficiency and safety. However, sometimes, assistance can be rejected by users when their commands are too altered. This provokes frustration and stress and, usually, decreases emergent efficiency. To improve acceptance, robot navigation algorithms can be adapted to mimic human behavior when possible. We propose a novel variation of the well-known dynamic window approach (DWA) that we call biomimetical DWA (BDWA). The BDWA relies on a reward function extracted from real traces from volunteers presenting different motor disabilities navigating in a hospital environment using a rollator for support. We have compared the BDWA with other reactive algorithms in terms of similarity to paths completed by people with disabilities using a robotic rollator in a rehabilitation hospital unit. The BDWA outperforms all tested algorithms in terms of likeness to human paths and success rate.

The assistance to people during rehabilitation has to be adapted to their needs. Too little help can lead to frustration and stress in the user; an excess of help may lead to low participation and loss of residual skills. Robotic rollators may adapt assistance. The main challenge to cope with this issue is to estimate how much help is needed on the fly, because it depends not only on the person condition, but also on the specific situation that they are negotiating. Clinical scales provide a global condition based estimation, but no local estimator based on punctual needs. Condition also changes in time, so clinical scales need to be recalculated again and again. In this paper we propose a novel approach to estimate users' condition in a continuous way via a robotic rollator. Our work focuses on predicting the value of the well known Tinetti Mobility test from spatiotemporal gait parameters obtained from our platform while users walk. This prediction provides continuous insight on the condition of the user and could be used to modify the amount of help provided. The proposed method has been validated with 19 volunteers at a local hospital that use a rollator for rehabilitation. All volunteers presented some physical or mental disabilities. Our results successfully show a high correlation of spatiotemporal gait parameters with Tinetti Mobility test gait (R2 = 0.7) and Tinetti Mobility test balance (R2 = 0.6).

Gait analysis provides insightful information about people condition, progress of rehabilitation treatments and fall risk. This analysis is frequently performed in very specific conditions, because it often implies the use of treadmills, cameras and/or wearable sensors. Alternatively, gait can be analyzed using a smart rollator, equipped with a basic set of sensors. This work proposes a methodology to obtain relevant gait parameters using a simple, cheap source of information: wheel odometry and handlebar force sensors. The main advantages of this approach are that users are not bothered with extra equipment. Besides, monitoring can be performed anywhere, even during everyday conditions and for extended periods of time. We have tested our system with a set of rehabilitation patients with different disabilities. Preliminary results have successfully proven that extracted parameters are coherent with reported effects of their specific condition.

Multi-robot systems (MRS) are a very active and important research topic nowadays. One of the main problems of these systems is the large number of variables to take into account. Due to this, robot behaviors are sometimes learnt instead of calculated via analytical expressions. A typical learning mechanism, specially for biomimetic robots, is Learning from demonstration (LfD). This paper proposes a LfD approach for implicit coordinated navigation using combination of Case-Based Reasoning (CBR) behaviors. During a training stage, CBR is used to learn simple behaviors that associate positions of other robots and/or objects to motion commands for each robot. Thus, human operators only need to concentrate on achieving their robot's goal as efficiently as possible in the operating conditions. Then, in running stage, each robot will achieve a different coordinate navigation strategy depending on the triggered behaviors. This system has been successfully tested with three Aibo-ERS7 robots in a RobCup-like environment.

This work presents a new approach to proactive collaborative wheelchair control. The system is based on estimating how much help the person needs at each situation and providing just the correct amount. This is achieved by combining robot and human control commands in a reactive fashion after weighting them by their respective local efficiency. Thus, the better the person drives, the more control he/she is awarded with. In order to predict how much help users may need in advance rather than waiting for them to decrease in efficiency, their skills to deal with each situation are estimated with respect to a baseline driver profile to increase assistance when needed. Situations are characterized at reactive level to keep a reduced set. This profile has been extracted from real traces of more than 70 inpatients presenting different physical and cognitive skills via clustering. The approach has been successfully tested at Fondazione Santa Lucia (FSL) in Rome.

Localization in multi-robot systems is a key problem in multi agent systems. In many cases, specially involving legged robots, like the Robocup soccer competition, it relies on predefined landmarks at known locations. However, when several objects are in motion, vision occlusions due to agents in the field of view make this kind of localization unfeasible for mild time periods. This also happens if landmarks are not within the field of view. This paper presents a technique to let a robot estimate its position with respect to objects or robots by sharing whatever visual data it has with its teammates. Shared data is used to estimate the relative positions of robots watching the same object via stereoscopy. The rest of the robots can be localized via triangulation and acquire information on the position of hidden or unknown objects that other members of the team can see. The system has been tested with two Aibo ERS 7 robots from Sony and an Aiball.

This work presents a new approach to shared control to assist wheelchair driving. Rather than swapping control from human to robot either by request or on a need basis, the system estimates how much help is needed in a reactive fashion and continuously produces an emergent motory command in combination with human input. To provide time stability and integration, instant commands are modulated by a factor depending on human efficiency in a shifting time window. Thus, the better the person drives, the more control he/she is awarded with. The approach has been tested at Fondazione Santa Lucia (FSL) in Rome with volunteers presenting different disabilities. All volunteers managed to finish a mildly complicated trajectory with door crossing and major turns and the proposed system increased efficiency in all cases.

In this contribution, an automatic system of song edition for Guitar Hero/Frets on Fire is presented. The system performs three fundamental stages: time analysis, frequency analysis and button allocation. The temporal analysis of the musical signal is used to obtain the rhythmic pattern of the song. The frequency analysis is perform on the basis of a Mel filter bank because these filters provide an approximation to the human perception of low and high frequency sounds. The allocation of buttons is done using the rhythmic pattern extracted, the Mel filters outputs and, also, taking into account a selectable difficulty level. The distributions of the buttons obtained with the designed system are similar to the ones found in the predetermined songs of the games.

Autonomous robots are capable of navigating on their own. Shared control approaches, however, allow humans to make some navigation decisions. This is typically executed either by overriding the human or the robot control at some specific situations. In this paper, we propose a method to allow cooperation between humans and robots at each point of any given trajectory so that both have some weight in the emergent behavior of the mobile robot. This is achieved by evaluating their efficiencies at each time instant and combining their commands into a single order. In order to achieve a seamless combination, this procedure is integrated into a bottom-up architecture via a reactive layer. We have tested the proposed method using a real robot and several volunteers, and results have been satisfactory both from a quantitative and qualitative point of view.

This paper presents a new CBR navigation scheme based on three behaviours: wall following, corridor following and door crossing. The system switches among CBR behaviours depending on a local grid built around the robot from sonar sensor readings. The main advantage of the proposed system is that it can easily adapt to any robot and environment, learning, both by training and by its own experience. The method has been successfully tested in real environments with a Pioneer robot equipped with 8 frontal sonar sensors.

Autonomous robots are capable of navigating on their own. In some cases, though, it is interesting to allow humans to influence navigation. Shared control is typically achieved either by giving control to the human or the robot at some specific situations. In this work, we propose a method to share control between humans and robots at each point of a given trajectory, so that both have weight in the resulting behavior of the mobile. This is achieved by estimating their respective local efficiencies at each time instant and combining their commands into a single order. In order to achieve a seamless combination, this procedure is integrated into a bottom-up architecture via a reactive layer. The system is meant to be used in wheelchair control for people with disabilities. Thus far, we have tested the proposed method using a Pioneer AT robot driven by several volunteers. Results have been satisfactory both from a quantitative and qualitative point of view.

This paper presents a hybrid layered architecture for intelligent transportation. The system relies on integrating odometry, DGPS and compass readings for localization and deliberative planning, whereas the path is tracked at low level by means of a CBR based reactive navigation algorithm capable of adapting to the vehicle kinematics and environment conditions through learning. The system has been successfully tested using a 4-wheeled outdoors robot Pioneer AT

Research in mobile robot navigation has been based in approaches that integrate the metric and topological paradigms for mapping indoor environments. While metric methods produce accurate environment representations, their huge data volume and time complexity often prohibits efficient planning in large-scale indoor environments. On the other hand, topological maps can be used in a more efficient way, but accurate and coherent topological maps are often difficult to learn and maintain in large-scale environments. The paper describes an approach that integrates both paradigms: metric and topological. The metric map is learnt using a very simple scheme based on the works of Moravec and Elfes (1988). Then, a topological map is generated on top of the metric map by using a hierarchical structure. The main advantage of the proposed structure is that the partitioning of the metric map into coherent regions is achieved in an unsupervised manner with a low computational time. The paper gives results for autonomous exploration, mapping and planning of a Nomad200 mobile robot in large indoor environments.

This paper presents both a new sonar based place learning method and a global localisation algorithm based on finite Markov chains for autonomous robots. Landmarks are calculated by projecting the Fourier transform of the depth map obtained from a ring of equally spaced sonar sensors onto a bidimensional base of its vectorial subspace. Resulting landmarks can be acquired at any position of the environment and they do not depend on the robot orientation. Localisation relies on segmenting available landmarks into homogeneous regions and calculating transition matrices between them. Then, the position of the robot is estimated according to finite Markovian chains, but the probability of occupying a given region is modulated by the most recently acquired landmark. The method is valid for complex unstructured environments and it has experimentally proven to be fast, reliable and computationally cheap.

Presents a fast localisation algorithm for autonomous mobile agents in dynamic environments based on the definition of a very small sized landmark type. These landmarks are calculated by obtaining the coordinates of the circular depth function obtained from a ring of equally spaced sonar sensors projected on a bi-dimensional base of a vectorial space. Finally, a pyramidal structure is used to enhance and fasten the performance of the localisation algorithm.