572 research outputs found
Balancing Global Exploration and Local-connectivity Exploitation with Rapidly-exploring Random disjointed-Trees
Sampling efficiency in a highly constrained environment has long been a major
challenge for sampling-based planners. In this work, we propose
Rapidly-exploring Random disjointed-Trees* (RRdT*), an incremental optimal
multi-query planner. RRdT* uses multiple disjointed-trees to exploit
local-connectivity of spaces via Markov Chain random sampling, which utilises
neighbourhood information derived from previous successful and failed samples.
To balance local exploitation, RRdT* actively explore unseen global spaces when
local-connectivity exploitation is unsuccessful. The active trade-off between
local exploitation and global exploration is formulated as a multi-armed bandit
problem. We argue that the active balancing of global exploration and local
exploitation is the key to improving sample efficient in sampling-based motion
planners. We provide rigorous proofs of completeness and optimal convergence
for this novel approach. Furthermore, we demonstrate experimentally the
effectiveness of RRdT*'s locally exploring trees in granting improved
visibility for planning. Consequently, RRdT* outperforms existing
state-of-the-art incremental planners, especially in highly constrained
environments.Comment: Submitted to IEEE International Conference on Robotics and Automation
(ICRA) 201
Multi-Risk-RRT: An Efficient Motion Planning Algorithm for Robotic Autonomous Luggage Trolley Collection at Airports
Robots have become increasingly prevalent in dynamic and crowded environments
such as airports and shopping malls. In these scenarios, the critical
challenges for robot navigation are reliability and timely arrival at
predetermined destinations. While existing risk-based motion planning
algorithms effectively reduce collision risks with static and dynamic
obstacles, there is still a need for significant performance improvements.
Specifically, the dynamic environments demand more rapid responses and robust
planning. To address this gap, we introduce a novel risk-based
multi-directional sampling algorithm, Multi-directional Risk-based
Rapidly-exploring Random Tree (Multi-Risk-RRT). Unlike traditional algorithms
that solely rely on a rooted tree or double trees for state space exploration,
our approach incorporates multiple sub-trees. Each sub-tree independently
explores its surrounding environment. At the same time, the primary rooted tree
collects the heuristic information from these sub-trees, facilitating rapid
progress toward the goal state. Our evaluations, including simulation and
real-world environmental studies, demonstrate that Multi-Risk-RRT outperforms
existing unidirectional and bi-directional risk-based algorithms in planning
efficiency and robustness
Asymptotically Optimal Sampling-Based Motion Planning Methods
Motion planning is a fundamental problem in autonomous robotics that requires
finding a path to a specified goal that avoids obstacles and takes into account
a robot's limitations and constraints. It is often desirable for this path to
also optimize a cost function, such as path length.
Formal path-quality guarantees for continuously valued search spaces are an
active area of research interest. Recent results have proven that some
sampling-based planning methods probabilistically converge toward the optimal
solution as computational effort approaches infinity. This survey summarizes
the assumptions behind these popular asymptotically optimal techniques and
provides an introduction to the significant ongoing research on this topic.Comment: Posted with permission from the Annual Review of Control, Robotics,
and Autonomous Systems, Volume 4. Copyright 2021 by Annual Reviews,
https://www.annualreviews.org/. 25 pages. 2 figure
Planning and Learning: Path-Planning for Autonomous Vehicles, a Review of the Literature
This short review aims to make the reader familiar with state-of-the-art
works relating to planning, scheduling and learning. First, we study
state-of-the-art planning algorithms. We give a brief introduction of neural
networks. Then we explore in more detail graph neural networks, a recent
variant of neural networks suited for processing graph-structured inputs. We
describe briefly the concept of reinforcement learning algorithms and some
approaches designed to date. Next, we study some successful approaches
combining neural networks for path-planning. Lastly, we focus on temporal
planning problems with uncertainty.Comment: AAAI-format & update
Quantum Search Approaches to Sampling-Based Motion Planning
In this paper, we present a novel formulation of traditional sampling-based
motion planners as database-oracle structures that can be solved via quantum
search algorithms. We consider two complementary scenarios: for simpler sparse
environments, we formulate the Quantum Full Path Search Algorithm (q-FPS),
which creates a superposition of full random path solutions, manipulates
probability amplitudes with Quantum Amplitude Amplification (QAA), and quantum
measures a single obstacle free full path solution. For dense unstructured
environments, we formulate the Quantum Rapidly Exploring Random Tree algorithm,
q-RRT, that creates quantum superpositions of possible parent-child
connections, manipulates probability amplitudes with QAA, and quantum measures
a single reachable state, which is added to a tree. As performance depends on
the number of oracle calls and the probability of measuring good quantum
states, we quantify how these errors factor into the probabilistic completeness
properties of the algorithm. We then numerically estimate the expected number
of database solutions to provide an approximation of the optimal number of
oracle calls in the algorithm. We compare the q-RRT algorithm with a classical
implementation and verify quadratic run-time speedup in the largest connected
component of a 2D dense random lattice. We conclude by evaluating a proposed
approach to limit the expected number of database solutions and thus limit the
optimal number of oracle calls to a given number.Comment: 12 pages, 11 figure
Junk-s_pace city : landscape, ecology, secrecy, botanical : K-206: The Institute Political of Economy
M.Tech. (Architectural Technology)This dissertation is structured to mimic or represent my design project, which was developed in conjunction with theoretical readings and research. The aim is to provide a comprehensive representation of the project’s development from theoretical beginnings to architectural detail. This dissertation attempts to uncover, explore and understand the fragile relationship between man, nature and architecture, stemming from a deeply personal interest in the rehabilitation of political wastelands through ecological restoration. The aim of this study is to pursue a holistic design approach, which understands the architectural discipline as an interrelated profession in which buildings cannot be designed in isolation. Broken down into three main parts, this document illustrates the development of my theoretical and contextual interests into design. These papers are presented in their entirety of this document as Part A (starting point and research influence), Part B (process and discovery) and Part C which introduces a narrative program developed for the architectural discovery of K-206 (nestled within ‘jungle’ in Alexandra). A series of abstract drawings and model building experiments initially framed the investigation, allowing for chance, exploration and the unexpected, valuable experiments in the development of an architectural language and vocabulary which were refined over the course of the yea
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