2,047 research outputs found
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
An ILP Solver for Multi-label MRFs with Connectivity Constraints
Integer Linear Programming (ILP) formulations of Markov random fields (MRFs)
models with global connectivity priors were investigated previously in computer
vision, e.g., \cite{globalinter,globalconn}. In these works, only Linear
Programing (LP) relaxations \cite{globalinter,globalconn} or simplified
versions \cite{graphcutbase} of the problem were solved. This paper
investigates the ILP of multi-label MRF with exact connectivity priors via a
branch-and-cut method, which provably finds globally optimal solutions. The
method enforces connectivity priors iteratively by a cutting plane method, and
provides feasible solutions with a guarantee on sub-optimality even if we
terminate it earlier. The proposed ILP can be applied as a post-processing
method on top of any existing multi-label segmentation approach. As it provides
globally optimal solution, it can be used off-line to generate ground-truth
labeling, which serves as quality check for any fast on-line algorithm.
Furthermore, it can be used to generate ground-truth proposals for weakly
supervised segmentation. We demonstrate the power and usefulness of our model
by several experiments on the BSDS500 and PASCAL image dataset, as well as on
medical images with trained probability maps.Comment: 19 page
Multi-criteria Evolution of Neural Network Topologies: Balancing Experience and Performance in Autonomous Systems
Majority of Artificial Neural Network (ANN) implementations in autonomous
systems use a fixed/user-prescribed network topology, leading to sub-optimal
performance and low portability. The existing neuro-evolution of augmenting
topology or NEAT paradigm offers a powerful alternative by allowing the network
topology and the connection weights to be simultaneously optimized through an
evolutionary process. However, most NEAT implementations allow the
consideration of only a single objective. There also persists the question of
how to tractably introduce topological diversification that mitigates
overfitting to training scenarios. To address these gaps, this paper develops a
multi-objective neuro-evolution algorithm. While adopting the basic elements of
NEAT, important modifications are made to the selection, speciation, and
mutation processes. With the backdrop of small-robot path-planning
applications, an experience-gain criterion is derived to encapsulate the amount
of diverse local environment encountered by the system. This criterion
facilitates the evolution of genes that support exploration, thereby seeking to
generalize from a smaller set of mission scenarios than possible with
performance maximization alone. The effectiveness of the single-objective
(optimizing performance) and the multi-objective (optimizing performance and
experience-gain) neuro-evolution approaches are evaluated on two different
small-robot cases, with ANNs obtained by the multi-objective optimization
observed to provide superior performance in unseen scenarios
Conception of control paradigms for teleoperated driving tasks in urban environments
Development of concepts and computationally efficient motion planning methods for teleoperated drivingEntwicklung von Konzepten und recheneffizienten Bewegungsplanungsmethoden fĂĽr teleoperiertes Fahre
Solving Disjunctive Temporal Networks with Uncertainty under Restricted Time-Based Controllability using Tree Search and Graph Neural Networks
Planning under uncertainty is an area of interest in artificial intelligence.
We present a novel approach based on tree search and graph machine learning for
the scheduling problem known as Disjunctive Temporal Networks with Uncertainty
(DTNU). Dynamic Controllability (DC) of DTNUs seeks a reactive scheduling
strategy to satisfy temporal constraints in response to uncontrollable action
durations. We introduce new semantics for reactive scheduling: Time-based
Dynamic Controllability (TDC) and a restricted subset of TDC, R-TDC. We design
a tree search algorithm to determine whether or not a DTNU is R-TDC. Moreover,
we leverage a graph neural network as a heuristic for tree search guidance.
Finally, we conduct experiments on a known benchmark on which we show R-TDC to
retain significant completeness with regard to DC, while being faster to prove.
This results in the tree search processing fifty percent more DTNU problems in
R-TDC than the state-of-the-art DC solver does in DC with the same time budget.
We also observe that graph neural network search guidance leads to substantial
performance gains on benchmarks of more complex DTNUs, with up to eleven times
more problems solved than the baseline tree search.Comment: Thirty-Sixth AAAI Conference on Artificial Intelligence. This version
includes the technical appendix. arXiv admin note: substantial text overlap
with arXiv:2108.0106
Lightweight Neural Path Planning
Learning-based path planning is becoming a promising robot navigation
methodology due to its adaptability to various environments. However, the
expensive computing and storage associated with networks impose significant
challenges for their deployment on low-cost robots. Motivated by this practical
challenge, we develop a lightweight neural path planning architecture with a
dual input network and a hybrid sampler for resource-constrained robotic
systems. Our architecture is designed with efficient task feature extraction
and fusion modules to translate the given planning instance into a guidance
map. The hybrid sampler is then applied to restrict the planning within the
prospective regions indicated by the guide map. To enable the network training,
we further construct a publicly available dataset with various successful
planning instances. Numerical simulations and physical experiments demonstrate
that, compared with baseline approaches, our approach has nearly an order of
magnitude fewer model size and five times lower computational while achieving
promising performance. Besides, our approach can also accelerate the planning
convergence process with fewer planning iterations compared to sample-based
methods.Comment: 8 page
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