Due to demographic change, health and elderly care systems are facing a shortage of qualified caregivers. This issue can be addressed by introducing welfare robots into people’s homes, hospitals, and care institutions. To provide useful support, such robots must adapt to individual users and smoothly interact with them. From this perspective, we present advances on the development of proactive control for online individual user adaptation in a welfare robot guidance scenario, with the integration of three main modules: 1) navigation control; 2) visual human detection; and 3) temporal error correlation-based neural learning. The proposed control approach can drive a mobile robot to autonomously navigate in relevant indoor environments. At the same time, it can predict human walking speed based on visual information without prior knowledge of personality and preferences (i.e., walking speed). The robot then uses this prediction to continuously adapt its speed to individual users in a proactive online manner. We validate the performance of the proposed proactive robot control in different real-world environments with various users, including an elderly resident of a Danish elderly care center. The results show that the robot successfully and smoothly guided various users of different ages and average walking speeds (e.g., 0.2 m/s, 0.7 m/s, and 1.1 m/s) to target locations over distances of 25–60 m. All in all, this study captures a wide range of research from robot control technology development to technological validity in a relevant environment and system prototype demonstration in an operational environment (i.e., an elderly care center).

Braitenberg vehicles are simple models of animal motion towards, or away from, a stimulus (light, sound, chemicals, etc). They have been widely used in robotics to implement target reaching and avoidance behaviors based on different types of sensors. While the seminal work of Braitenberg used wheeled vehicles to illustrate the principles of animal steering, few attempts have been made at combining these steering level controllers with locomotion mechanism other than actuated wheels. This paper presents the first implementation of this biologically inspired steering controller in a snake-like robot with non-actuated wheels and actuated joints. The sinusoidal gait of the snake is modulated following the principles of the Braitenberg vehicle 3α using two sensors symmetrically located on the head. The effectiveness of this bio-inspired controller is shown through simulations where the snake orients its head and body with the direction of the stimulus gradient, and reaches the stimulus maximum within some range. This paper represents one of the first steps towards connecting bio-inspired sensor-based steering mechanisms and bio-inspired locomotion, and shows that existing theoretical results of Braitenberg vehicles with actuated wheels also apply to a snake-like robot with non-actuated wheels.

A fundamental problem in cooperative HumanRobot Interaction is object handover. Existing works in this area assume the human can reliably grasp the object from the robot hand. However, in some situations the human can produce perturbing forces in the object that are not meant to end in a handover. These perturbations can result in the object being dropped or the robot hand being damaged. This paper addresses this problem and presents a mechanism for reliable robot to human object handover implemented in a Shadow Robot hand endowed with tactile sensing. Given a stable grasping configuration, using BioTAC sensors we are able to estimate the contact forces applied to the object, and provide a feedback signal to a joint effort controller to maintain grasp forces despite perturbations. Our system is able to identify between object pulling forces which should result in an object handover, and other disturbances. Experimental results show that the hand releases the object only when the object is pulled, validating the proposed algorithm.

Braitenberg vehicles have been used in multiple real world robotic implementations of bio-inspired local navigation. While sensor based control strategies - including existing models of Braitenberg vehicles - typically neglect sensor noise, real robot implementations suffer from different levels of noise, especially in the case of low cost robots and highly stochastic environments. This paper presents a novel drift-diffusion model of Braitenberg vehicle 3a - a bio-inspired source seeking controller for non-holonomic robots - accounting for the sensor noise. The stochastic differential equations obtained provide means to accurately simulate the behaviour of this bio-inspired control mechanism. Although these equations do not have analytic solutions in general, under some simplifying assumptions, we obtain the deterministic equations for the average and dispersion of the vehicles while performing source seeking. Moreover, we found an analytic bound for the distribution of the heading direction of the robots. Simulations illustrate and confirm the theoretical results presented.

A fundamental aspect of many human-robot collaborative tasks is object exchange or handover. Several techniques have been proposed to decide when a robot hand or gripper should release an object for a human to receive. However, these techniques typically neglect the reliability of the handover, assuming the process will occur without issue. Based on the fact that humans apply pulling forces in specific directions when receiving an object, this paper presents a recursive procedure enabling a robot to release an object appropriately and timely during a handover. Experiments with naive users showed subject-specific consistent pulling directions during robot-human handovers, highlighting the need for a system to be capable of detecting force directions in relation to objects. The approach reported in this paper shows that through tactile sensing the proposed approach can accurately classify between five different actions impacting an object held by a robot hand. The recursive nature of the system also enables detection of sequences of different actions, enabling the robot to decide to safely release the object only when the pulling performed by the human is in the right direction.

Tactile sensing has recently attracted significant research interest in robotics. Despite the fact that tactile sensors provide temporal sequences of readings, state-of-the-art material recognition approaches are episodic, i.e. a whole sequence of readings is processed to identify the material. Based on vibration frequency response, this work presents an online identification technique using recursive estimation of the probability of identifying a set of materials, i.e. casting the classification problem as a hidden Markov model (HMM) state estimation problem. This allows for faster identification of most materials and does not require several exploratory movements. Our results show that when enough evidence is gathered, the system eventually achieves perfect recognition of our experimental set of 34 materials with an average identification time of ≤ 0.5 seconds. To prove the accuracy of this method, we also conducted a comparative experiment with commonly used machine learning algorithms for material identification such as k-Nearest Neighbour(KNN), an Artificial Neural Network(ANN) and Support Vector Machine(SVM).

Tactile sensing has been used in robotics for object identification, grasping, and material recognition. Most material recognition approaches use vibration signals from a tactile exploration, typically above one second long, to identify the material. This work proposes a tactile multi-modal (vibration and thermal) material identification approach based on recursive Bayesian estimation. Through the frequency response of the vibration induced by the material and thermal features, like an estimate of the thermal power loss of the finger, we show that it is possible to identify materials in less than half a second. Moreover, a comparison between vibration only and multi-modal identification shows that both recognition time and classification errors are reduced by adding thermal information.

Braitenberg vehicles are simple models of the motion of animal towards, or away from, a stimulus. They have been used to implement target reaching and avoidance behaviours in robotics, based, among others, on sound, light, distance, and pressure sensors. When the sensors are accurate enough - when they provide a high signal to noise ratio - variations on the readings can be neglected. Their behaviour, in these applications, can be explained using a non-linear deterministic dynamical system. For noisy sensors, or when the physical interaction between the robot and the measured variable is complex, a deterministic model is not good enough. Some examples include robots with cheap sensors, sensor prototypes, or settings where the interaction with the environment changes the measurements, like odour tracking - as the motion of the robot creates turbulences in the air. This paper presents the first analysis of the behaviour of Braitenberg vehicle 3a with noisy sensors. The mathematical equations of the evolution of the vehicle state probability are derived under white noise assumptions, and a bound for the vehicle state uncertainty is obtained assuming Gaussian probability distributions of the pose. These equations relate the morphological parameters of the vehicle, the noise variance and the function connecting the sensors to the actuators. The non-linear controller simulations illustrate the local validity of the model, and global simulations show how the vehicles converge.

Chaos is an interesting phenomenon displayed by non-linear systems that has been used in robotics and found empirically on real robots' behaviour. This paper presents a formal proof that Braitenberg vehicle 2b, a bio-inspired control mechanism for unicycle robots, behaves as a mass in a potential well. This provides a powerful analogy to understand Braitenberg vehicle 2b as a mechanical system and it implies that its behaviour can be chaotic. Implementing a Braitenberg vehicle in robotics entails defining a stimulus function in the environment. Inspired by previous works and using the obtained theoretical results, this paper proposes the free area perceived by a robot as a stimulus to generate collision free continuous chaotic motion on a bounded space. In non-linear systems chaos appears when a parameter is changed in a family of dynamical systems. Similarly, our implementation on a real robot demonstrates that both, chaotic and non chaotic trajectories can be obtained depending on the stimulus function built. The behaviour is explained through the analogy with the mass in the potential well.

Braitenberg vehicles are well-known models of animal behavior used as steering mechanisms in mobile robotics and artificial life. Because of their simplicity, they are mainly used for teaching robotics, while the lack of a quantitative theory has limited their use for research purposes. This article contributes to our formal understanding of Braitenberg vehicle 3a by presenting the convergence properties of its trajectories under parabolic-shaped stimuli. We show previously unreported features of the motion of the vehicle: the conditional stability, the oscillatory behavior, and the existence of periodic trajectories. The mathematical model used provides a theoretical relation between the environment, the internal control mechanism of the vehicle, and some morphological parameters, a link already found in experimental works. This work provides theoretical support for experimental research using Braitenberg vehicle 3a, and paves the way for further research in biology, robotics, and artificial life.

Autonomous lane keeping is a well studied problem with several good solutions that can be found in the literature. However, naturalistic lane keeping mechanisms, in the sense of imitating human car steering, are not so common. Based on existing knowledge of human driving, this paper analyses several controllers prone to generate human-like lane keeping behavior. Using systems identification, we compare how well the control models fit with real human steering data gathered in a simulator. Experimental results points towards a parsimonious control mechanism where angles relative to the direction of the road are directly used by the driver to steer the car and keep it on the lane. This result can be used to design human like autonomous lane keeping mechanisms or to improve the design of ADAS systems adapting them to individual drivers.

Underwater vehicles do not localise or navigate with respect to the flow, an ability needed for many underwater tasks. In this paper we implement rheotaxis behaviour in a fish robot, a behaviour common to many aquatic species. We use two pressure sensors on the head of the robot to identify the pressure differences on the left and right side and control the heading of the fish robot by turning a servo-motor actuated tail. The controller is inspired by the Braitenberg vehicle 2b, a simple biological model of tropotaxis, that has been used in many robotic applications. The experiments, conducted in a flow pipe with a uniform flow, show that the robot is able to orient itself, and keep the orientation, to the incoming current. Our results demonstrate that guidance of a fish robot relative to a flow can be implemented as a simple rheotaxis behaviour using two sensors and a Braitenberg 2b controller.

Braitenberg vehicles are well known models of animal behaviour prone to be used as a mobile robot controller. Due to the qualitative simplicity of their behaviour they are used for teaching robotics, whilst the lack of a quantitative theory makes its use for research purposes troublesome. This paper contributes to our formal understanding of Braitenberg vehicle 3a by presenting some convergence properties of its trajectories under parabolic shaped stimuli or potential functions. We show new features of the vehicle motion unreported in robotics like, their conditional stability, their oscillatory behaviour and the existence of a preferred convergence direction. Quantitatively identifying the behaviour of Braitenberg vehicles allows to use them in robotics on a sound basis, not just relying on incomplete qualitative understanding as done by earlier works.