4,181 research outputs found
A biologically inspired meta-control navigation system for the Psikharpax rat robot
A biologically inspired navigation system for the mobile rat-like robot named Psikharpax is presented, allowing for self-localization and autonomous navigation in an initially unknown environment. The ability of parts of the model (e. g. the strategy selection mechanism) to reproduce rat behavioral data in various maze tasks has been validated before in simulations. But the capacity of the model to work on a real robot platform had not been tested. This paper presents our work on the implementation on the Psikharpax robot of two independent navigation strategies (a place-based planning strategy and a cue-guided taxon strategy) and a strategy selection meta-controller. We show how our robot can memorize which was the optimal strategy in each situation, by means of a reinforcement learning algorithm. Moreover, a context detector enables the controller to quickly adapt to changes in the environment-recognized as new contexts-and to restore previously acquired strategy preferences when a previously experienced context is recognized. This produces adaptivity closer to rat behavioral performance and constitutes a computational proposition of the role of the rat prefrontal cortex in strategy shifting. Moreover, such a brain-inspired meta-controller may provide an advancement for learning architectures in robotics
Design of a biologically inspired navigation system for the Psikharpax rodent robot
This work presents the development and implementation of a biologically inspired navigation system on the autonomous Psikharpax rodent robot. Our system comprises two independent navigation strategies: a taxon expert and a planning expert. The presented navigation system allows the robot to learn the optimal strategy in each situation, by relying upon a strategy selection mechanism
Towards an Autonomous Walking Robot for Planetary Surfaces
In this paper, recent progress in the development of
the DLR Crawler - a six-legged, actively compliant walking
robot prototype - is presented. The robot implements
a walking layer with a simple tripod and a more complex
biologically inspired gait. Using a variety of proprioceptive
sensors, different reflexes for reactively crossing obstacles
within the walking height are realised. On top of
the walking layer, a navigation layer provides the ability
to autonomously navigate to a predefined goal point in
unknown rough terrain using a stereo camera. A model
of the environment is created, the terrain traversability is
estimated and an optimal path is planned. The difficulty
of the path can be influenced by behavioral parameters.
Motion commands are sent to the walking layer and the
gait pattern is switched according to the estimated terrain
difficulty. The interaction between walking layer and navigation
layer was tested in different experimental setups
Intelligent Navigation for a Solar Powered Unmanned Underwater Vehicle
In this paper, an intelligent navigation system for
an unmanned underwater vehicle powered by renewable
energy and designed for shadow water inspection in
missions of a long duration is proposed. The system is
composed of an underwater vehicle, which tows a surface
vehicle. The surface vehicle is a small boat with
photovoltaic panels, a methanol fuel cell and
communication equipment, which provides energy and
communication to the underwater vehicle. The underwater
vehicle has sensors to monitor the underwater
environment such as sidescan sonar and a video camera in
a flexible configuration and sensors to measure the
physical and chemical parameters of water quality on
predefined paths for long distances. The underwater
vehicle implements a biologically inspired neural
architecture for autonomous intelligent navigation.
Navigation is carried out by integrating a kinematic
adaptive neuroâcontroller for trajectory tracking and an
obstacle avoidance adaptive neuroâ controller. The
autonomous underwater vehicle is capable of operating
during long periods of observation and monitoring. This
autonomous vehicle is a good tool for observing large areas
of sea, since it operates for long periods of time due to the
contribution of renewable energy. It correlates all sensor
data for time and geodetic position. This vehicle has been
used for monitoring the Mar Menor lagoon.Supported by the Coastal Monitoring
System for the Mar Menor (CMSâ 463.01.08_CLUSTER)
project founded by the Regional Government of Murcia,
by the SICUVA project (Control and Navigation System
for AUV Oceanographic Monitoring Missions. REF:
15357/PI/10) founded by the Seneca Foundation of
Regional Government of Murcia and by the DIVISAMOS
project (Design of an Autonomous Underwater Vehicle
for Inspections and oceanographic missionâUPCT: DPIâ
2009â14744âC03â02) founded by the Spanish Ministry of
Science and Innovation from Spain
An implementation of the path integrator mechanism of head direction cells for bio-mimetic navigation
© 2014 IEEE. Head direction cells are thought to be an integral part of the neural navigation system. These cells track the agent's current head direction irrespective of the host's location. In doing so, they process a combination of inputs: angular velocity and visual inputs are major effectors; to correctly encode the agent's current heading. There are close to fifteen models of head direction cell systems found in literature today. Very few of these models have been implemented for bio-mimetic navigation in robots. In this paper, we describe an implementation of the head direction cell system on the robot operating system (ROS) robotic platform as a first step towards a bio-mimetic navigation system for Willow Garage's personal robot 2 (PR2) robot
A multirobot platform based on autonomous surface and underwater vehicles with bio-inspired neurocontrollers for long-term oil spills monitoring
This paper describes the BUSCAMOS-Oil monitoring system, which is a robotic platform consisting of an autonomous surface vessel combined with an underwater vehicle. The system has been designed for the long-term monitoring of oil spills, including the search for the spill, and transmitting information on its location, extent, direction and speed. Both vehicles are controlled by two different types of bio-inspired neural networks: a Self-Organization Direction Mapping Network for trajectory generation and a Neural Network for Avoidance Behaviour for avoiding obstacles. The systemsâ resilient capabilities are provided by bio-inspired algorithms implemented in a modular software architecture and controlled by redundant devices to give the necessary robustness to operate in the difficult conditions typically found in long-term oil-spill operations. The efficacy of the vehiclesâ adaptive navigation system and long-term mission capabilities are shown in the experimental results.This work was partially supported by the BUSCAMOS Project (ref. 1003211003700) under the program DN8644 COINCIDENTE of the Spanish Defense Ministry, the âResearch Programme for Groups of Scientific Excellence at Region of Murciaâ of the Seneca Foundation (Agency for Science and Technology of the Region of Murcia-19895/GERM/15)â, and the Spanish Governmentâs cDrone (ref. TIN2013-45920-R) and ViSelTR (ref. TIN2012-39279) projects
Slowness and Sparseness Lead to Place, Head-Direction, and Spatial-View Cells
We present a model for the self-organized formation of place cells, head-direction cells, and spatial-view cells in the hippocampal formation based on unsupervised learning on quasi-natural visual stimuli. The model comprises a hierarchy of Slow Feature Analysis (SFA) nodes, which were recently shown to reproduce many properties of complex cells in the early visual system. The system extracts a distributed grid-like representation of position and orientation, which is transcoded into a localized place-field, head-direction, or view representation, by sparse coding. The type of cells that develops depends solely on the relevant input statistics, i.e., the movement pattern of the simulated animal. The numerical simulations are complemented by a mathematical analysis that allows us to accurately predict the output of the top SFA laye
Biologically inspired intensity and depth image edge extraction
In recent years artificial vision research has moved from focusing on the use of only intensity images to include using depth images, or RGB-D combinations due to the recent development of low cost depth cameras. However, depth images require a lot of storage and processing requirements. In addition, it is challenging to extract relevant features from depth images in real-time. Researchers have sought inspiration from biology in order to overcome these challenges resulting in biologically inspired feature extraction methods. By taking inspiration from nature it may be possible to reduce redundancy, extract relevant features, and process an image efficiently by emulating biological visual processes. In this paper, we present a depth and intensity image feature extraction approach that has been inspired by biological vision systems. Through the use of biologically inspired spiking neural networks we emulate functional computational aspects of biological visual systems. Results demonstrate that the proposed bio-inspired artificial vision system has increased performance over existing computer vision feature extraction approaches
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