8,629 research outputs found
Real-Time Planning with Primitives for Dynamic Walking over Uneven Terrain
We present an algorithm for receding-horizon motion planning using a finite
family of motion primitives for underactuated dynamic walking over uneven
terrain. The motion primitives are defined as virtual holonomic constraints,
and the special structure of underactuated mechanical systems operating subject
to virtual constraints is used to construct closed-form solutions and a special
binary search tree that dramatically speed up motion planning. We propose a
greedy depth-first search and discuss improvement using energy-based
heuristics. The resulting algorithm can plan several footsteps ahead in a
fraction of a second for both the compass-gait walker and a planar
7-Degree-of-freedom/five-link walker.Comment: Conference submissio
Safe Policy Synthesis in Multi-Agent POMDPs via Discrete-Time Barrier Functions
A multi-agent partially observable Markov decision process (MPOMDP) is a
modeling paradigm used for high-level planning of heterogeneous autonomous
agents subject to uncertainty and partial observation. Despite their modeling
efficiency, MPOMDPs have not received significant attention in safety-critical
settings. In this paper, we use barrier functions to design policies for
MPOMDPs that ensure safety. Notably, our method does not rely on discretization
of the belief space, or finite memory. To this end, we formulate sufficient and
necessary conditions for the safety of a given set based on discrete-time
barrier functions (DTBFs) and we demonstrate that our formulation also allows
for Boolean compositions of DTBFs for representing more complicated safe sets.
We show that the proposed method can be implemented online by a sequence of
one-step greedy algorithms as a standalone safe controller or as a
safety-filter given a nominal planning policy. We illustrate the efficiency of
the proposed methodology based on DTBFs using a high-fidelity simulation of
heterogeneous robots.Comment: 8 pages and 4 figure
Dictionary-based Tensor Canonical Polyadic Decomposition
To ensure interpretability of extracted sources in tensor decomposition, we
introduce in this paper a dictionary-based tensor canonical polyadic
decomposition which enforces one factor to belong exactly to a known
dictionary. A new formulation of sparse coding is proposed which enables high
dimensional tensors dictionary-based canonical polyadic decomposition. The
benefits of using a dictionary in tensor decomposition models are explored both
in terms of parameter identifiability and estimation accuracy. Performances of
the proposed algorithms are evaluated on the decomposition of simulated data
and the unmixing of hyperspectral images
Self-Dictionary Sparse Regression for Hyperspectral Unmixing: Greedy Pursuit and Pure Pixel Search are Related
This paper considers a recently emerged hyperspectral unmixing formulation
based on sparse regression of a self-dictionary multiple measurement vector
(SD-MMV) model, wherein the measured hyperspectral pixels are used as the
dictionary. Operating under the pure pixel assumption, this SD-MMV formalism is
special in that it allows simultaneous identification of the endmember spectral
signatures and the number of endmembers. Previous SD-MMV studies mainly focus
on convex relaxations. In this study, we explore the alternative of greedy
pursuit, which generally provides efficient and simple algorithms. In
particular, we design a greedy SD-MMV algorithm using simultaneous orthogonal
matching pursuit. Intriguingly, the proposed greedy algorithm is shown to be
closely related to some existing pure pixel search algorithms, especially, the
successive projection algorithm (SPA). Thus, a link between SD-MMV and pure
pixel search is revealed. We then perform exact recovery analyses, and prove
that the proposed greedy algorithm is robust to noise---including its
identification of the (unknown) number of endmembers---under a sufficiently low
noise level. The identification performance of the proposed greedy algorithm is
demonstrated through both synthetic and real-data experiments
Obstacle-aware Adaptive Informative Path Planning for UAV-based Target Search
Target search with unmanned aerial vehicles (UAVs) is relevant problem to
many scenarios, e.g., search and rescue (SaR). However, a key challenge is
planning paths for maximal search efficiency given flight time constraints. To
address this, we propose the Obstacle-aware Adaptive Informative Path Planning
(OA-IPP) algorithm for target search in cluttered environments using UAVs. Our
approach leverages a layered planning strategy using a Gaussian Process
(GP)-based model of target occupancy to generate informative paths in
continuous 3D space. Within this framework, we introduce an adaptive replanning
scheme which allows us to trade off between information gain, field coverage,
sensor performance, and collision avoidance for efficient target detection.
Extensive simulations show that our OA-IPP method performs better than
state-of-the-art planners, and we demonstrate its application in a realistic
urban SaR scenario.Comment: Paper accepted for International Conference on Robotics and
Automation (ICRA-2019) to be held at Montreal, Canad
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