3,403 research outputs found
Towards Monocular Vision based Obstacle Avoidance through Deep Reinforcement Learning
Obstacle avoidance is a fundamental requirement for autonomous robots which
operate in, and interact with, the real world. When perception is limited to
monocular vision avoiding collision becomes significantly more challenging due
to the lack of 3D information. Conventional path planners for obstacle
avoidance require tuning a number of parameters and do not have the ability to
directly benefit from large datasets and continuous use. In this paper, a
dueling architecture based deep double-Q network (D3QN) is proposed for
obstacle avoidance, using only monocular RGB vision. Based on the dueling and
double-Q mechanisms, D3QN can efficiently learn how to avoid obstacles in a
simulator even with very noisy depth information predicted from RGB image.
Extensive experiments show that D3QN enables twofold acceleration on learning
compared with a normal deep Q network and the models trained solely in virtual
environments can be directly transferred to real robots, generalizing well to
various new environments with previously unseen dynamic objects.Comment: Accepted by RSS 2017 workshop New Frontiers for Deep Learning in
Robotic
Neural Network Local Navigation of Mobile Robots in a Moving Obstacles Environment
IF AC Intelligent Components and Instruments for Control Applications, Budapest, Hungary, 1994This paper presents a local navigation method based on generalized predictive control. A modified cost function to avoid moving and static obstacles is presented. An Extended Kaiman Filter is proposed to predict the motions of the obstacles. A Neural Network implementation of this method is analysed. Simulation results are shown.Ministerio de Ciencia y Tecnología TAP93-0408Ministerio de Ciencia y Tecnología TAP93-058
Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning
Developing a safe and efficient collision avoidance policy for multiple
robots is challenging in the decentralized scenarios where each robot generate
its paths without observing other robots' states and intents. While other
distributed multi-robot collision avoidance systems exist, they often require
extracting agent-level features to plan a local collision-free action, which
can be computationally prohibitive and not robust. More importantly, in
practice the performance of these methods are much lower than their centralized
counterparts.
We present a decentralized sensor-level collision avoidance policy for
multi-robot systems, which directly maps raw sensor measurements to an agent's
steering commands in terms of movement velocity. As a first step toward
reducing the performance gap between decentralized and centralized methods, we
present a multi-scenario multi-stage training framework to find an optimal
policy which is trained over a large number of robots on rich, complex
environments simultaneously using a policy gradient based reinforcement
learning algorithm. We validate the learned sensor-level collision avoidance
policy in a variety of simulated scenarios with thorough performance
evaluations and show that the final learned policy is able to find time
efficient, collision-free paths for a large-scale robot system. We also
demonstrate that the learned policy can be well generalized to new scenarios
that do not appear in the entire training period, including navigating a
heterogeneous group of robots and a large-scale scenario with 100 robots.
Videos are available at https://sites.google.com/view/drlmac
Synthesized cooperative strategies for intelligent multi-robots in a real-time distributed environment : a thesis presented in partial fulfillment of the requirements for the degree of Master of Science in Computer Science at Massey University, Albany, New Zealand
In the robot soccer domain, real-time response usually curtails the development of more complex Al-based game strategies, path-planning and team cooperation between intelligent agents. In light of this problem, distributing computationally intensive algorithms between several machines to control, coordinate and dynamically assign roles to a team of robots, and allowing them to communicate via a network gives rise to real-time cooperation in a multi-robotic team. This research presents a myriad of algorithms tested on a distributed system platform that allows for cooperating multi- agents in a dynamic environment. The test bed is an extension of a popular robot simulation system in the public domain developed at Carnegie Mellon University, known as TeamBots. A low-level real-time network game protocol using TCP/IP and UDP were incorporated to allow for a conglomeration of multi-agent to communicate and work cohesively as a team. Intelligent agents were defined to take on roles such as game coach agent, vision agent, and soccer player agents. Further, team cooperation is demonstrated by integrating a real-time fuzzy logic-based ball-passing algorithm and a fuzzy logic algorithm for path planning. Keywords Artificial Intelligence, Ball Passing, the coaching system, Collaborative, Distributed Multi-Agent, Fuzzy Logic, Role Assignmen
Learning with Training Wheels: Speeding up Training with a Simple Controller for Deep Reinforcement Learning
Deep Reinforcement Learning (DRL) has been applied successfully to many
robotic applications. However, the large number of trials needed for training
is a key issue. Most of existing techniques developed to improve training
efficiency (e.g. imitation) target on general tasks rather than being tailored
for robot applications, which have their specific context to benefit from. We
propose a novel framework, Assisted Reinforcement Learning, where a classical
controller (e.g. a PID controller) is used as an alternative, switchable policy
to speed up training of DRL for local planning and navigation problems. The
core idea is that the simple control law allows the robot to rapidly learn
sensible primitives, like driving in a straight line, instead of random
exploration. As the actor network becomes more advanced, it can then take over
to perform more complex actions, like obstacle avoidance. Eventually, the
simple controller can be discarded entirely. We show that not only does this
technique train faster, it also is less sensitive to the structure of the DRL
network and consistently outperforms a standard Deep Deterministic Policy
Gradient network. We demonstrate the results in both simulation and real-world
experiments.Comment: Published in ICRA2018. The code is now available at
https://github.com/xie9187/AsDDP
Wavefront Propagation and Fuzzy Based Autonomous Navigation
Path planning and obstacle avoidance are the two major issues in any
navigation system. Wavefront propagation algorithm, as a good path planner, can
be used to determine an optimal path. Obstacle avoidance can be achieved using
possibility theory. Combining these two functions enable a robot to
autonomously navigate to its destination. This paper presents the approach and
results in implementing an autonomous navigation system for an indoor mobile
robot. The system developed is based on a laser sensor used to retrieve data to
update a two dimensional world model of therobot environment. Waypoints in the
path are incorporated into the obstacle avoidance. Features such as ageing of
objects and smooth motion planning are implemented to enhance efficiency and
also to cater for dynamic environments
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