Conference Paper

Multilayered reinforcement learning for complicated collision avoidance problems

RIKEN, Inst. of Phys. & Chem. Res., Saitama
DOI: 10.1109/ROBOT.1998.680648 Conference: Robotics and Automation, 1998. Proceedings. 1998 IEEE International Conference on, Volume: 3
Source: IEEE Xplore

ABSTRACT We have proposed the collision avoidance methods in a multirobot
system based on the information exchanged by the “LOCISS: Locally
Communicable Infrared Sensory System”, which is developed by the
authors. One of the problems in the LOCISS based methods is that the
number of situations which should be considered increases very much when
the number of the robots and stationary obstacles in the working
environment increases. In order to reduce the required computational
power and memory capacity for such a large number of situations, we
propose, in this paper, a multilayered reinforcement learning scheme to
acquire appropriate collision avoidance behaviors. The feasibility and
the performance of the proposed scheme is examined through the
experiment using actual mobile robots

0 Bookmarks
 · 
58 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Navigation consisting of two essential components known as localization and planning is the art of steering a course through a medium. Localization matches an actual position in the real-world to a location inside a map; in other words, each location in the map refers to an actual position in the environment. Planning is finding a short, collision-free path from the starting position towards the predefined ending location. This study is a survey which focuses on introducing classic and heuristic-based path planning approaches and investigates their achievements in search optimization problems. The methods are categorized, their strengths and drawbacks are discussed, and the applications in which they have been utilized are explained.
    International Journal of Advancements in Computing Technology (IJACT). 01/2013; 5(14):1-14.
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper describes a neural network model for the reactive behavioural navigation of an autonomous underwater vehicle (AUV) in which an innovative, neurobiological inspired sensorization control system and a hardware architectures are being implemented. The AUV has been with several types of environmental and oceanographic instruments such as CTD sensors, chlorophyll, turbidity, optical dissolved oxygen (YSI V6600 sonde) and nitrate analyzer (SUNA) together with ADCP, side scan sonar and video camera, in a flexible configuration to provide a water quality monitoring platform with mapping capabilities. This neurobiological inspired control architecture for autonomous intelligent navigation was implemented on an AUV capable of operating during large periods of time for observation and monitoring. In this work, the autonomy of the AUV is evaluated in several scenarios.
    OCEANS, 2011 IEEE - Spain; 07/2011
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Navigation in dynamic and uncertain environments in the absence of reliable environment map is challeng-ing. In this study, we investigate the effectiveness of two variations of Particle Swarm Optimization (PSO) called Area Extended PSO (AEPSO) and Cooperative AEPSO (CAEPSO) in noisy environments in which the noise does not represent random noise originated from a single type of source but the combination of noises originating from different sources located in nearby or faraway positions. Knowledge Transfer and Transfer Learning that represent the use of the expertise and knowledge gained from previous experiments can improve the robots decision making and reduce the number of wrong decisions in such uncertain environments. This study investigates the impact of transfer learning on robots' search in such hostile environment. The results highlight the feasibility of CAEPSO to be used as the movement controller and decision maker of a swarm of robots in the simulated uncertain environment when gained expertise from past trainings is transferred to the robots in the testing phase.
    10th International Conference on Informatics in Control, Automation and Robotics (ICINCO); 07/2013