3,491 research outputs found
Learning Ground Traversability from Simulations
Mobile ground robots operating on unstructured terrain must predict which
areas of the environment they are able to pass in order to plan feasible paths.
We address traversability estimation as a heightmap classification problem: we
build a convolutional neural network that, given an image representing the
heightmap of a terrain patch, predicts whether the robot will be able to
traverse such patch from left to right. The classifier is trained for a
specific robot model (wheeled, tracked, legged, snake-like) using simulation
data on procedurally generated training terrains; the trained classifier can be
applied to unseen large heightmaps to yield oriented traversability maps, and
then plan traversable paths. We extensively evaluate the approach in simulation
on six real-world elevation datasets, and run a real-robot validation in one
indoor and one outdoor environment.Comment: Webpage: http://romarcg.xyz/traversability_estimation
Behavior Trees in Robotics and AI: An Introduction
A Behavior Tree (BT) is a way to structure the switching between different
tasks in an autonomous agent, such as a robot or a virtual entity in a computer
game. BTs are a very efficient way of creating complex systems that are both
modular and reactive. These properties are crucial in many applications, which
has led to the spread of BT from computer game programming to many branches of
AI and Robotics. In this book, we will first give an introduction to BTs, then
we describe how BTs relate to, and in many cases generalize, earlier switching
structures. These ideas are then used as a foundation for a set of efficient
and easy to use design principles. Properties such as safety, robustness, and
efficiency are important for an autonomous system, and we describe a set of
tools for formally analyzing these using a state space description of BTs. With
the new analysis tools, we can formalize the descriptions of how BTs generalize
earlier approaches. We also show the use of BTs in automated planning and
machine learning. Finally, we describe an extended set of tools to capture the
behavior of Stochastic BTs, where the outcomes of actions are described by
probabilities. These tools enable the computation of both success probabilities
and time to completion
Accelerating decision making under partial observability using learned action priors
Thesis (M.Sc.)--University of the Witwatersrand, Faculty of Science, School of Computer Science and Applied Mathematics, 2017.Partially Observable Markov Decision Processes (POMDPs) provide a principled mathematical
framework allowing a robot to reason about the consequences of actions and
observations with respect to the agent's limited perception of its environment. They
allow an agent to plan and act optimally in uncertain environments. Although they
have been successfully applied to various robotic tasks, they are infamous for their high
computational cost. This thesis demonstrates the use of knowledge transfer, learned
from previous experiences, to accelerate the learning of POMDP tasks. We propose
that in order for an agent to learn to solve these tasks quicker, it must be able to generalise
from past behaviours and transfer knowledge, learned from solving multiple tasks,
between di erent circumstances. We present a method for accelerating this learning
process by learning the statistics of action choices over the lifetime of an agent, known
as action priors. Action priors specify the usefulness of actions in situations and allow
us to bias exploration, which in turn improves the performance of the learning process.
Using navigation domains, we study the degree to which transferring knowledge
between tasks in this way results in a considerable speed up in solution times.
This thesis therefore makes the following contributions. We provide an algorithm
for learning action priors from a set of approximately optimal value functions and two
approaches with which a prior knowledge over actions can be used in a POMDP context.
As such, we show that considerable gains in speed can be achieved in learning subsequent
tasks using prior knowledge rather than learning from scratch. Learning with
action priors can particularly be useful in reducing the cost of exploration in the early
stages of the learning process as the priors can act as mechanism that allows the agent
to select more useful actions given particular circumstances. Thus, we demonstrate how
the initial losses associated with unguided exploration can be alleviated through the
use of action priors which allow for safer exploration. Additionally, we illustrate that
action priors can also improve the computation speeds of learning feasible policies in a
shorter period of time.MT201
Decision-making and problem-solving methods in automation technology
The state of the art in the automation of decision making and problem solving is reviewed. The information upon which the report is based was derived from literature searches, visits to university and government laboratories performing basic research in the area, and a 1980 Langley Research Center sponsored conferences on the subject. It is the contention of the authors that the technology in this area is being generated by research primarily in the three disciplines of Artificial Intelligence, Control Theory, and Operations Research. Under the assumption that the state of the art in decision making and problem solving is reflected in the problems being solved, specific problems and methods of their solution are often discussed to elucidate particular aspects of the subject. Synopses of the following major topic areas comprise most of the report: (1) detection and recognition; (2) planning; and scheduling; (3) learning; (4) theorem proving; (5) distributed systems; (6) knowledge bases; (7) search; (8) heuristics; and (9) evolutionary programming
Reinforcement Learning for Robot Navigation with Adaptive Forward Simulation Time (AFST) in a Semi-Markov Model
Deep reinforcement learning (DRL) algorithms have proven effective in robot
navigation, especially in unknown environments, by directly mapping perception
inputs into robot control commands. However, most existing methods ignore the
local minimum problem in navigation and thereby cannot handle complex unknown
environments. In this paper, we propose the first DRL-based navigation method
modeled by a semi-Markov decision process (SMDP) with continuous action space,
named Adaptive Forward Simulation Time (AFST), to overcome this problem.
Specifically, we reduce the dimensions of the action space and improve the
distributed proximal policy optimization (DPPO) algorithm for the specified
SMDP problem by modifying its GAE to better estimate the policy gradient in
SMDPs. Experiments in various unknown environments demonstrate the
effectiveness of AFST
Human-like arm motion generation: a review
In the last decade, the objectives outlined by the needs of personal robotics have led to the rise of new biologically-inspired techniques for arm motion planning. This paper presents a literature review of the most recent research on the generation of human-like arm movements in humanoid and manipulation robotic systems. Search methods and inclusion criteria are described. The studies are analyzed taking into consideration the sources of publication, the experimental settings, the type of movements, the technical approach, and the human motor principles that have been used to inspire and assess human-likeness. Results show that there is a strong focus on the generation of single-arm reaching movements and biomimetic-based methods. However, there has been poor attention to manipulation, obstacle-avoidance mechanisms, and dual-arm motion generation. For these reasons, human-like arm motion generation may not fully respect human behavioral and neurological key features and may result restricted to specific tasks of human-robot interaction. Limitations and challenges are discussed to provide meaningful directions for future investigations.FCT Project UID/MAT/00013/2013FCT–Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020
Intelligent Navigation Service Robot Working in a Flexible and Dynamic Environment
Numerous sensor fusion techniques have been reported in the literature for a number of robotics applications. These techniques involved the use of different sensors in different configurations. However, in the case of food driving, the possibility of the implementation has been overlooked. In restaurants and food delivery spots, enhancing the food transfer to the correct table is neatly required, without running into other robots or diners or toppling over. In this project, a particular algorithm module has been proposed and implemented to enhance the robot driving methodology and maximize robot functionality, accuracy, and the food transfer experience. The emphasis has been on enhancing movement accuracy to reach the targeted table from the start to the end. Four major elements have been designed to complete this project, including mechanical, electrical, electronics, and programming. Since the floor condition greatly affecting the wheels and turning angle selection, the movement accuracy was improved during the project. The robot was successfully able to receive the command from the restaurant and go to deliver the food to the customers\u27 tables, considering any obstacles on the way to avoid. The robot has equipped with two trays to mount the food with well-configured voices to welcome and greet the customer. The performance has been evaluated and undertaken using a routine robot movement tests. As part of this study, the designed service wheeled robot required to be with a high-performance real-time processor. As long as the processor was adequate, the experimental results showed a highly effective search robot methodology. Having concluded from the study that a minimum number of sensors are needed if they are placed appropriately and used effectively on a robot\u27s body, as navigation could be performed by using a small set of sensors. The Arduino Due has been used to provide a real-time operating system. It has provided a very successful data processing and transfer throughout any regular operation. Furthermore, an easy-to-use application has been developed to improve the user experience, so that the operator can interact directly with the robot via a special setting screen. It is possible, using this feature, to modify advanced settings such as voice commands or IP address without having to return back to the code
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