Adaptation of sensor morphology: an integrative view of perception from biologically inspired robotics perspective

Sensor morphology, the morphology of a sensing mechanism which plays a role of shaping the desired response from physical stimuli from surroundings to generate signals usable as sensory information, is one of the key common aspects of sensing processes. This paper presents a structured review of researches on bioinspired sensor morphology implemented in robotic systems, and discusses the fundamental design principles. Based on literature review, we propose two key arguments: first, owing to its synthetic nature, biologically inspired robotics approach is a unique and powerful methodology to understand the role of sensor morphology and how it can evolve and adapt to its task and environment. Second, a consideration of an integrative view of perception by looking into multidisciplinary and overarching mechanisms of sensor morphology adaptation across biology and engineering enables us to extract relevant design principles that are important to extend our understanding of the unfinished concepts in sensing and perceptionThis study was supported by the European Commission with the RoboSoft CA (A Coordination Action for Soft Robotics, contract #619319). SGN was supported by School of Engineering seed funding (2016), Malaysia Campus, Monash University

Embodiment design of soft continuum robots

This article presents the results of a multidisciplinary project where mechatronic engineers worked alongside biologists to develop a soft robotic arm that captures key features of octopus anatomy and neurophysiology. The concept of embodiment (the dynamic coupling between sensory-motor control, anatomy, materials and environment that allows for the animal to achieve adaptive behaviours) is used as a starting point for the design process but tempered by current engineering technologies and approaches. In this article, the embodied design requirements are first discussed from a robotic viewpoint by taking into account real-life engineering limitations; then, the motor control schemes inspired by octopus nervous system are investigated. Finally, the mechanical and control design of a prototype is presented that appropriately blends bio-inspiration and engineering limitations. Simulated and experimental results show that the developed continuum robotic arm is able to reproduce octopus-like motions for bending, reaching and grasping

Embodied Models and Neurorobotics

Neuroscience has become a very broad field indeed: each year around 30,000 researchers and students from around the ... We trace a path from neuron to cognition via computational neuroscience, but what is computational neuroscience

Supervised Learning of Internal Models for Autonomous Goal-oriented Robot Navigation using Reservoir Computing

In this work we propose a hierarchical architec- ture which constructs internal models of a robot environment for goal-oriented navigation by an imitation learning process. The proposed architecture is based on the Reservoir Computing paradigm for training Recurrent Neural Networks (RNN). It is composed of two randomly generated RNNs (called reservoirs), one for modeling the localization capability and one for learning the navigation skill. The localization module is trained to detect the current and previously visited robot rooms based only on 8 noisy infra-red distance sensors. These predictions together with distance sensors and the desired goal location are used by the navigation network to actually steer the robot through the environment in a goal-oriented manner. The training of this architecture is performed in a supervised way (with examples of trajectories created by a supervisor) using linear regression on the reservoir states. So, the reservoir acts as a temporal kernel projecting the inputs to a rich feature space, whose states are linearly combined to generate the desired outputs. Experimental results on a simulated robot show that the trained system can localize itself within both simple and large unknown environments and navigate successfully to desired goals

Distributed sensing in flexible robotic fins: propulsive force prediction and underwater contact sensing

There is recent biological evidence that the pectoral fins of bluegill sunfish are innervated with nerves that respond to bending, and these fish contact obstacles with their fins. However, it is not known how fin-intrinsic sensing could be used to mediate propulsion and touch in engineered fins. The objective of this thesis is to understand the use of distributed sensing in robotic fins, inspired by bony fish fins, for the prediction of propulsive forces and for the discrimination between fluidic loading and contact loading during underwater touch. The research integrates engineering and biology and builds an understanding of fin-intrinsic sensing through study of swimming fish and robotic models of fish fins and sensors. Multiple studies identify which sensor types, sensor placement locations, and model conditions are best for predicting fin propulsive forces and for predicting the state of contact. Comparisons are made between linear and nonlinear Volterra-series convolution models to represent the mapping from sensory data to forces. Best practices for instrumentation and model selection are extracted for a broad range of swimming conditions on a complex, multi-DOF, flexible fin. This knowledge will guide the development of multi-functional systems to navigate and propel through complex, occluded, underwater environments and for sensing and responding to environmental perturbations and obstacles.Ph.D., Mechanical Engineering and Mechanics -- Drexel University, 201