Magnetic-field-inspired navigation for quadcopter robot in unknown environments

In this paper, a magnetic-field-inspired robot navigation is used to navigate an under-actuated quad-copter towards the desired position amidst previously-unknown arbitrary-shaped convex obstacles. Taking inspiration from the phenomena of magnetic field interaction with charged particles observed in nature, the algorithm outperforms previous reactive navigation algorithms for flying robots found in the literature as it is able to reactively generate motion commands relying only on a local sensory information without prior knowledge of the obstacles' shape or location and without getting trapped in local minima configurations. The application of the algorithm in a dynamic model of quadcopter system and in the realistic model of the commercial AscTec Pelican micro-aerial vehicle confirm the superior performance of the algorithm

A Novel Concept for Safe, Stiffness-Controllable Robot Links

The recent decade has seen an astounding increase of interest and advancement in a new field of robotics, aimed at creating structures specifically for the safe interaction with humans. Softness, flexibility and variable stiffness in robotics have been recognised as highly desirable characteristics for many applications. A number of solutions were proposed ranging from entirely soft robots (such as those composed mainly from soft materials such as silicone), via flexible continuum and snake-like robots, to rigid-link robots enhanced by joints that exhibit an elastic behaviour either implemented in hardware or achieved purely by means of intelligent control. Although these are very good solutions paving the path to safe human-robot interaction, we propose here a new approach which focuses on creating stiffness controllability for the linkages between the robot joints. This paper proposes a replacement for the traditionally rigid robot link – the new link is equipped with an additional capability of stiffness controllability. With this added feature, a robot can accurately carry out manipulation tasks (high stiffness), but can virtually instantaneously reduce its stiffness when a human is nearby or in contact with the robot. The key point of the invention described here is a robot link made of an airtight chamber formed by a soft and flexible, but high-strain resistant combination of a plastic mesh and silicone wall. Inflated with air to a high pressure, the mesh-silicone chamber behaves like a rigid link; reducing the air pressure, softens the link and rendering the robot structure safe. This paper investigates a number of our link prototypes and shows the feasibility of the new concept. Stiffness tests have been performed, showing that a significant level of stiffness can be achieved - up to 40 N reaction force along the axial direction, for a 25 mm diameter sample at 60 kPa, at an axial deformation of 5 mm. The results confirm that this novel concept to linkages for robot manipulators exhibits the beam-like behaviour of traditional rigid links when fully pressurised and significantly reduced stiffness at low pressure. The proposed concept has the potential to easily create safe robots, augmenting traditional robot designs

Neuro-Fuzzy Motion Planning for Robotic Manipulators

On-going research efforts in robotics aim at providing mechanical systems, such as robotic manipulators and mobile robots, with more intelligence so that they can operate autonomously. Advancing in this direction, this thesis proposes and investigates novel manipulator path planning and navigation techniques which have their roots in the field of neural networks and fuzzy logic. Path planning in the configuration space makes necessary a transformation of the workspace into a configuration space. A radial-basis-function neural network is proposed to construct the configuration space by repeatedly mapping individual workspace obstacle points into so-called C-space patterns. The method is extended to compute the transformation for planar manipulators with n links as well as for manipulators with revolute and prismatic joints. A neural-network-based implementation of a computer emulated resistive grid is described and investigated. The grid, which is a collection of nodes laterally connected by weights, carries out global path planning in the manipulator’s configuration space. In response to a specific obstacle constellation, the grid generates an activity distribution whose gradient can be exploited to construct collision-free paths. A novel update algorithm, the To&Fro algorithm, which rapidly spreads the activity distribution over the nodes is proposed. Extensions to the basic grid technique are presented. A novel fuzzy-based system, the fuzzy navigator, is proposed to solve the navigation and obstacle avoidance problem for robotic manipulators. The presented system is divided into separate fuzzy units which individually control each manipulator link. The competing functions of goal following and obstacle avoidance are combined in each unit providing an intelligent behaviour. An on-line reinforcement learning method is introduced which adapts the performance of the fuzzy units continuously to any changes in the environment. All above methods have been tested in different environments on simulated manipulators as well as on a physical manipulator. The results proved these methods to be feasible for real-world applications

Identification of Haptic Based Guiding Using Hard Reins

This paper presents identifications of human-human interaction in which one person with limited auditory and visual perception of the environment (a follower) is guided by an agent with full perceptual capabilities (a guider) via a hard rein along a given path. We investigate several identifications of the interaction between the guider and the follower such as computational models that map states of the follower to actions of the guider and the computational basis of the guider to modulate the force on the rein in response to the trust level of the follower. Based on experimental identification systems on human demonstrations show that the guider and the follower experience learning for an optimal stable state-dependent novel 3rd and 2nd order auto-regressive predictive and reactive control policies respectively. By modeling the follower's dynamics using a time varying virtual damped inertial system, we found that the coefficient of virtual damping is most appropriate to explain the trust level of the follower at any given time. Moreover, we present the stability of the extracted guiding policy when it was implemented on a planar 1-DoF robotic arm. Our findings provide a theoretical basis to design advanced human-robot interaction algorithms applicable to a variety of situations where a human requires the assistance of a robot to perceive the environment

Knock-Knock: Acoustic Object Recognition using Stacked Denoising Autoencoders

This paper presents a successful application of deep learning for object recognition based on acoustic data. The shortcomings of previously employed approaches where handcrafted features describing the acoustic data are being used, include limiting the capability of the found representation to be widely applicable and facing the risk of capturing only insignificant characteristics for a task. In contrast, there is no need to define the feature representation format when using multilayer/deep learning architecture methods: features can be learned from raw sensor data without defining discriminative characteristics a-priori. In this paper, stacked denoising autoencoders are applied to train a deep learning model. Knocking each object in our test set 120 times with a marker pen to obtain the auditory data, thirty different objects were successfully classified in our experiment and each object was knocked 120 times by a marker pen to obtain the auditory data. By employing the proposed deep learning framework, a high accuracy of 91.50% was achieved. A traditional method using handcrafted features with a shallow classifier was taken as a benchmark and the attained recognition rate was only 58.22%. Interestingly, a recognition rate of 82.00% was achieved when using a shallow classifier with raw acoustic data as input. In addition, we could show that the time taken to classify one object using deep learning was far less (by a factor of more than 6) than utilizing the traditional method. It was also explored how different model parameters in our deep architecture affect the recognition performance.Comment: 6 pages, 10 figures, Neurocomputin

Soft fluidic rotary actuator with improved actuation properties

The constantly increasing amount of machines operating in the vicinity of humans makes it necessary to rethink the design approach for such machines to ensure that they are safe when interacting with humans. Traditional mechanisms are rigid and heavy and as such considered unsuitable, even dangerous when a controlled physical contact with humans is desired. A huge improvement in terms of safe human-robot interaction has been achieved by a radically new approach to robotics - soft material robotics. These new robots are made of compliant materials that render them safe when compared to the conventional rigid-link robots. This undeniable advantage of compliance and softness is paired with a number of drawbacks. One of them is that a complex and sophisticated controller is required to move a soft robot into the desired positions or along a desired trajectory, especially with external forces being present. In this paper we propose an improved soft fluidic rotary actuator composed of silicone rubber and fiber-based reinforcement. The actuator is cheap and easily manufactured providing near linear actuation properties when compared to pneumatic actuators presented elsewhere. The paper presents the actuator design, manufacturing process and a mathematical model of the actuator behavior as well as an experimental validation of the model. Four different actuator types are compared including a square-shaped and three differently reinforced cylindrical actuators

Soft Robot-Assisted Minimally Invasive Surgery and Interventions: Advances and Outlook

Since the emergence of soft robotics around two decades ago, research interest in the field has escalated at a pace. It is fuelled by the industry's appreciation of the wide range of soft materials available that can be used to create highly dexterous robots with adaptability characteristics far beyond that which can be achieved with rigid component devices. The ability, inherent in soft robots, to compliantly adapt to the environment, has significantly sparked interest from the surgical robotics community. This article provides an in-depth overview of recent progress and outlines the remaining challenges in the development of soft robotics for minimally invasive surgery