2,307 research outputs found
Intelligent Surveillance Robot with Obstacle Avoidance Capabilities Using Neural Network
For specific purpose, vision-based surveillance robot that can be run autonomously and able to acquire images from its dynamic environment is very important, for example, in rescuing disaster victims in Indonesia. In this paper, we propose architecture for intelligent surveillance robot that is able to avoid obstacles using 3 ultrasonic distance sensors based on backpropagation neural network and a camera for face recognition. 2.4 GHz transmitter for transmitting video is used by the operator/user to direct the robot to the desired area. Results show the effectiveness of our method and we evaluate the performance of the system
Mobile robot controller using novel hybrid system
Hybrid neuro-fuzzy controller is one of the techniques that is used as a tool to control a mobile robot in unstructured environment. In this paper a novel neuro-fuzzy technique is proposed in order to tackle the problem of mobile robot autonomous navigation in unstructured environment. Obstacle avoidance is an important task in the field of robotics, since the goal of autonomous robot is to reach the destination without collision. The objective is to make the robot move along a collision free trajectory until it reaches its target. The proposed approach uses the artificial neural network instead of the fuzzified engine then the output from it is processed using adaptive inference engine and defuzzification engine. In this approach, the real processing time is reduce that is increase the mobile robot response. The proposed neuro-fuzzy controller is evaluated subjectively and objectively with other approaches and also the processing time is taken in consideration
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 neuro‐controller for trajectory tracking and an
obstacle avoidance adaptive neuro‐ 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 (CMS‐ 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 mission‐UPCT: DPI‐
2009‐14744‐C03‐02) founded by the Spanish Ministry of
Science and Innovation from Spain
Embodied Evolution in Collective Robotics: A Review
This paper provides an overview of evolutionary robotics techniques applied
to on-line distributed evolution for robot collectives -- namely, embodied
evolution. It provides a definition of embodied evolution as well as a thorough
description of the underlying concepts and mechanisms. The paper also presents
a comprehensive summary of research published in the field since its inception
(1999-2017), providing various perspectives to identify the major trends. In
particular, we identify a shift from considering embodied evolution as a
parallel search method within small robot collectives (fewer than 10 robots) to
embodied evolution as an on-line distributed learning method for designing
collective behaviours in swarm-like collectives. The paper concludes with a
discussion of applications and open questions, providing a milestone for past
and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl
Intelligent Escape of Robotic Systems: A Survey of Methodologies, Applications, and Challenges
Intelligent escape is an interdisciplinary field that employs artificial
intelligence (AI) techniques to enable robots with the capacity to
intelligently react to potential dangers in dynamic, intricate, and
unpredictable scenarios. As the emphasis on safety becomes increasingly
paramount and advancements in robotic technologies continue to advance, a wide
range of intelligent escape methodologies has been developed in recent years.
This paper presents a comprehensive survey of state-of-the-art research work on
intelligent escape of robotic systems. Four main methods of intelligent escape
are reviewed, including planning-based methodologies, partitioning-based
methodologies, learning-based methodologies, and bio-inspired methodologies.
The strengths and limitations of existing methods are summarized. In addition,
potential applications of intelligent escape are discussed in various domains,
such as search and rescue, evacuation, military security, and healthcare. In an
effort to develop new approaches to intelligent escape, this survey identifies
current research challenges and provides insights into future research trends
in intelligent escape.Comment: This paper is accepted by Journal of Intelligent and Robotic System
A Systematic Literature Review of Path-Planning Strategies for Robot Navigation in Unknown Environment
The Many industries, including ports, space, surveillance, military, medicine and agriculture have benefited greatly from mobile robot technology. An autonomous mobile robot navigates in situations that are both static and dynamic. As a result, robotics experts have proposed a range of strategies. Perception, localization, path planning, and motion control are all required for mobile robot navigation. However, Path planning is a critical component of a quick and secure navigation. Over the previous few decades, many path-planning algorithms have been developed. Despite the fact that the majority of mobile robot applications take place in static environments, there is a scarcity of algorithms capable of guiding robots in dynamic contexts. This review compares qualitatively mobile robot path-planning systems capable of navigating robots in static and dynamic situations. Artificial potential fields, fuzzy logic, genetic algorithms, neural networks, particle swarm optimization, artificial bee colonies, bacterial foraging optimization, and ant-colony are all discussed in the paper. Each method's application domain, navigation technique and validation context are discussed and commonly utilized cutting-edge methods are analyzed. This research will help researchers choose appropriate path-planning approaches for various applications including robotic cranes at the sea ports as well as discover gaps for optimization
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