The development of perception and control methods that allow bird-scale flapping-wing robots (a.k.a. ornithopters) to perform autonomously is an under-researched area. This paper presents a fully onboard event-based method for ornithopter robot visual guidance. The method uses event cameras to exploit their fast response and robustness against motion blur in order to feed the ornithopter control loop at high rates (100 Hz). The proposed scheme visually guides the robot using line features extracted in the event image plane and controls the flight by actuating over the horizontal and vertical tail deflections. It has been validated on board a real ornithopter robot with real-time computation in low-cost hardware. The experimental evaluation includes sets of experiments with different maneuvers indoors and outdoors.

Automatic surveillance and monitoring using Unmanned Aerial Systems (UAS) require the development of perception systems that robustly work under different illumination conditions. Event cameras are neuromorphic sensors that capture the illumination changes in the scene with very low latency and high dynamic range. Although recent advances in eventbased vision have explored the use of event cameras onboard UAS, most techniques group events in frames and, therefore, do not fully exploit the sequential and asynchronous nature of the event stream. This paper proposes a fully asynchronous scheme for intruder monitoring using UAS. It employs efficient event clustering and feature tracking modules and includes a sampling mechanism to cope with the computational cost of event-by-event processing adapting to on-board hardware computational constraints. The proposed scheme was tested on a real multirotor in challenging scenarios showing significant accuracy and robustness to lighting conditions.

This paper presents an bio-inspired event-based perception scheme for agile aerial robot maneuvering. It tries to mimic birds, which perform purposeful maneuvers by closing the separation in the retinal image (w.r.t. the goal) to follow time-to-contact trajectories. The proposed approach is based on event cameras, also called artificial retinas, which provide fast response and robustness against motion blur and lighting conditions. Our scheme guides the robot by only adjusting the position of features extracted in the event image plane to their goal positions at a predefined time using smooth time-to-contact trajectories. The proposed scheme is robust, efficient and can be added on top of commonly-used aerial robot velocity controllers. It has been validated on-board a UAV with real-time computation in low-cost hardware during sets of experiments with different descent maneuvers and lighting conditions.

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.

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.