10,983 research outputs found
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
Online Informative Path Planning for Active Classification on UAVs
We propose an informative path planning (IPP) algorithm for active
classification using an unmanned aerial vehicle (UAV), focusing on weed
detection in precision agriculture. We model the presence of weeds on farmland
using an occupancy grid and generate plans according to information-theoretic
objectives, enabling the UAV to gather data efficiently. We use a combination
of global viewpoint selection and evolutionary optimization to refine the UAV's
trajectory in continuous space while satisfying dynamic constraints. We
validate our approach in simulation by comparing against standard "lawnmower"
coverage, and study the effects of varying objectives and optimization
strategies. We plan to evaluate our algorithm on a real platform in the
immediate future.Comment: 7 pages, 4 figures, submission to International Symposium on
Experimental Robotics 201
Adaptive Information Gathering via Imitation Learning
In the adaptive information gathering problem, a policy is required to select
an informative sensing location using the history of measurements acquired thus
far. While there is an extensive amount of prior work investigating effective
practical approximations using variants of Shannon's entropy, the efficacy of
such policies heavily depends on the geometric distribution of objects in the
world. On the other hand, the principled approach of employing online POMDP
solvers is rendered impractical by the need to explicitly sample online from a
posterior distribution of world maps.
We present a novel data-driven imitation learning framework to efficiently
train information gathering policies. The policy imitates a clairvoyant oracle
- an oracle that at train time has full knowledge about the world map and can
compute maximally informative sensing locations. We analyze the learnt policy
by showing that offline imitation of a clairvoyant oracle is implicitly
equivalent to online oracle execution in conjunction with posterior sampling.
This observation allows us to obtain powerful near-optimality guarantees for
information gathering problems possessing an adaptive sub-modularity property.
As demonstrated on a spectrum of 2D and 3D exploration problems, the trained
policies enjoy the best of both worlds - they adapt to different world map
distributions while being computationally inexpensive to evaluate.Comment: Robotics Science and Systems, 201
Adaptive Informative Path Planning with Multimodal Sensing
Adaptive Informative Path Planning (AIPP) problems model an agent tasked with
obtaining information subject to resource constraints in unknown, partially
observable environments. Existing work on AIPP has focused on representing
observations about the world as a result of agent movement. We formulate the
more general setting where the agent may choose between different sensors at
the cost of some energy, in addition to traversing the environment to gather
information. We call this problem AIPPMS (MS for Multimodal Sensing). AIPPMS
requires reasoning jointly about the effects of sensing and movement in terms
of both energy expended and information gained. We frame AIPPMS as a Partially
Observable Markov Decision Process (POMDP) and solve it with online planning.
Our approach is based on the Partially Observable Monte Carlo Planning
framework with modifications to ensure constraint feasibility and a heuristic
rollout policy tailored for AIPPMS. We evaluate our method on two domains: a
simulated search-and-rescue scenario and a challenging extension to the classic
RockSample problem. We find that our approach outperforms a classic AIPP
algorithm that is modified for AIPPMS, as well as online planning using a
random rollout policy.Comment: First two authors contributed equally; International Conference on
Automated Planning and Scheduling (ICAPS) 202
Information Acquisition with Sensing Robots: Algorithms and Error Bounds
Utilizing the capabilities of configurable sensing systems requires
addressing difficult information gathering problems. Near-optimal approaches
exist for sensing systems without internal states. However, when it comes to
optimizing the trajectories of mobile sensors the solutions are often greedy
and rarely provide performance guarantees. Notably, under linear Gaussian
assumptions, the problem becomes deterministic and can be solved off-line.
Approaches based on submodularity have been applied by ignoring the sensor
dynamics and greedily selecting informative locations in the environment. This
paper presents a non-greedy algorithm with suboptimality guarantees, which does
not rely on submodularity and takes the sensor dynamics into account. Our
method performs provably better than the widely used greedy one. Coupled with
linearization and model predictive control, it can be used to generate adaptive
policies for mobile sensors with non-linear sensing models. Applications in gas
concentration mapping and target tracking are presented.Comment: 9 pages (two-column); 2 figures; Manuscript submitted to the 2014
IEEE International Conference on Robotics and Automatio
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