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Non-Uniform Offsetting and its Applications in Laser Path Planning of Sterolithography Machine
Laser path planning is an important step in solid freeform fabrication processes such as
Stereolithography (SLA). An important consideration in the laser path planning is to compensate
the shape of laser beam. Currently the compensation is divided into two steps, Z-compensation
and X-Y compensation, and the shape of laser beam is assumed to be uniform for the whole
platform. In this research, we present a sampling based non-uniform offsetting method which
accounts for the different shapes of laser beam at various locations. We discuss the related steps
and algorithms. We demonstrate its effectiveness by using various test cases. Besides
improving the accuracy of SLA machine, non-uniform offsetting can also be applied to address
other accuracy issues caused by thermal and structural variationsMechanical Engineerin
Simulation of Rapidly-Exploring Random Trees in Membrane Computing with P-Lingua and Automatic Programming
Methods based on Rapidly-exploring Random Trees (RRTs) have been
widely used in robotics to solve motion planning problems. On the other hand, in the
membrane computing framework, models based on Enzymatic Numerical P systems
(ENPS) have been applied to robot controllers, but today there is a lack of planning
algorithms based on membrane computing for robotics. With this motivation, we
provide a variant of ENPS called Random Enzymatic Numerical P systems with
Proteins and Shared Memory (RENPSM) addressed to implement RRT algorithms
and we illustrate it by simulating the bidirectional RRT algorithm. This paper is an
extension of [21]a. The software presented in [21] was an ad-hoc simulator, i.e, a tool
for simulating computations of one and only one model that has been hard-coded.
The main contribution of this paper with respect to [21] is the introduction of a novel
solution for membrane computing simulators based on automatic programming. First,
we have extended the P-Lingua syntax –a language to define membrane computing
models– to write RENPSM models. Second, we have implemented a new parser based
on Flex and Bison to read RENPSM models and produce source code in C language
for multicore processors with OpenMP. Finally, additional experiments are presented.Ministerio de EconomĂa, Industria y Competitividad TIN2017-89842-
Automated sequence and motion planning for robotic spatial extrusion of 3D trusses
While robotic spatial extrusion has demonstrated a new and efficient means to
fabricate 3D truss structures in architectural scale, a major challenge remains
in automatically planning extrusion sequence and robotic motion for trusses
with unconstrained topologies. This paper presents the first attempt in the
field to rigorously formulate the extrusion sequence and motion planning (SAMP)
problem, using a CSP encoding. Furthermore, this research proposes a new
hierarchical planning framework to solve the extrusion SAMP problems that
usually have a long planning horizon and 3D configuration complexity. By
decoupling sequence and motion planning, the planning framework is able to
efficiently solve the extrusion sequence, end-effector poses, joint
configurations, and transition trajectories for spatial trusses with
nonstandard topologies. This paper also presents the first detailed computation
data to reveal the runtime bottleneck on solving SAMP problems, which provides
insight and comparing baseline for future algorithmic development. Together
with the algorithmic results, this paper also presents an open-source and
modularized software implementation called Choreo that is machine-agnostic. To
demonstrate the power of this algorithmic framework, three case studies,
including real fabrication and simulation results, are presented.Comment: 24 pages, 16 figure
Reset-free Trial-and-Error Learning for Robot Damage Recovery
The high probability of hardware failures prevents many advanced robots
(e.g., legged robots) from being confidently deployed in real-world situations
(e.g., post-disaster rescue). Instead of attempting to diagnose the failures,
robots could adapt by trial-and-error in order to be able to complete their
tasks. In this situation, damage recovery can be seen as a Reinforcement
Learning (RL) problem. However, the best RL algorithms for robotics require the
robot and the environment to be reset to an initial state after each episode,
that is, the robot is not learning autonomously. In addition, most of the RL
methods for robotics do not scale well with complex robots (e.g., walking
robots) and either cannot be used at all or take too long to converge to a
solution (e.g., hours of learning). In this paper, we introduce a novel
learning algorithm called "Reset-free Trial-and-Error" (RTE) that (1) breaks
the complexity by pre-generating hundreds of possible behaviors with a dynamics
simulator of the intact robot, and (2) allows complex robots to quickly recover
from damage while completing their tasks and taking the environment into
account. We evaluate our algorithm on a simulated wheeled robot, a simulated
six-legged robot, and a real six-legged walking robot that are damaged in
several ways (e.g., a missing leg, a shortened leg, faulty motor, etc.) and
whose objective is to reach a sequence of targets in an arena. Our experiments
show that the robots can recover most of their locomotion abilities in an
environment with obstacles, and without any human intervention.Comment: 18 pages, 16 figures, 3 tables, 6 pseudocodes/algorithms, video at
https://youtu.be/IqtyHFrb3BU, code at
https://github.com/resibots/chatzilygeroudis_2018_rt
Neural network controller against environment: A coevolutive approach to generalize robot navigation behavior
In this paper, a new coevolutive method, called Uniform Coevolution, is introduced to learn weights of a neural network controller in autonomous robots. An evolutionary strategy is used to learn high-performance reactive behavior for navigation and collisions avoidance. The introduction of coevolutive over evolutionary strategies allows evolving the environment, to learn a general behavior able to solve the problem in different environments. Using a traditional evolutionary strategy method, without coevolution, the learning process obtains a specialized behavior. All the behaviors obtained, with/without coevolution have been tested in a set of environments and the capability of generalization is shown for each learned behavior. A simulator based on a mini-robot Khepera has been used to learn each behavior. The results show that Uniform Coevolution obtains better generalized solutions to examples-based problems.Publicad
Cellular Automata Applications in Shortest Path Problem
Cellular Automata (CAs) are computational models that can capture the
essential features of systems in which global behavior emerges from the
collective effect of simple components, which interact locally. During the last
decades, CAs have been extensively used for mimicking several natural processes
and systems to find fine solutions in many complex hard to solve computer
science and engineering problems. Among them, the shortest path problem is one
of the most pronounced and highly studied problems that scientists have been
trying to tackle by using a plethora of methodologies and even unconventional
approaches. The proposed solutions are mainly justified by their ability to
provide a correct solution in a better time complexity than the renowned
Dijkstra's algorithm. Although there is a wide variety regarding the
algorithmic complexity of the algorithms suggested, spanning from simplistic
graph traversal algorithms to complex nature inspired and bio-mimicking
algorithms, in this chapter we focus on the successful application of CAs to
shortest path problem as found in various diverse disciplines like computer
science, swarm robotics, computer networks, decision science and biomimicking
of biological organisms' behaviour. In particular, an introduction on the first
CA-based algorithm tackling the shortest path problem is provided in detail.
After the short presentation of shortest path algorithms arriving from the
relaxization of the CAs principles, the application of the CA-based shortest
path definition on the coordinated motion of swarm robotics is also introduced.
Moreover, the CA based application of shortest path finding in computer
networks is presented in brief. Finally, a CA that models exactly the behavior
of a biological organism, namely the Physarum's behavior, finding the
minimum-length path between two points in a labyrinth is given.Comment: To appear in the book: Adamatzky, A (Ed.) Shortest path solvers. From
software to wetware. Springer, 201
Active Image-based Modeling with a Toy Drone
Image-based modeling techniques can now generate photo-realistic 3D models
from images. But it is up to users to provide high quality images with good
coverage and view overlap, which makes the data capturing process tedious and
time consuming. We seek to automate data capturing for image-based modeling.
The core of our system is an iterative linear method to solve the multi-view
stereo (MVS) problem quickly and plan the Next-Best-View (NBV) effectively. Our
fast MVS algorithm enables online model reconstruction and quality assessment
to determine the NBVs on the fly. We test our system with a toy unmanned aerial
vehicle (UAV) in simulated, indoor and outdoor experiments. Results show that
our system improves the efficiency of data acquisition and ensures the
completeness of the final model.Comment: To be published on International Conference on Robotics and
Automation 2018, Brisbane, Australia. Project Page:
https://huangrui815.github.io/active-image-based-modeling/ The author's
personal page: http://www.sfu.ca/~rha55
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