16,856 research outputs found
When to Replan? An Adaptive Replanning Strategy for Autonomous Navigation using Deep Reinforcement Learning
The hierarchy of global and local planners is one of the most commonly
utilized system designs in autonomous robot navigation. While the global
planner generates a reference path from the current to goal locations based on
the pre-built static map, the local planner produces a kinodynamic trajectory
to follow the reference path while avoiding perceived obstacles. To account for
unforeseen or dynamic obstacles not present on the pre-built map, ``when to
replan'' the reference path is critical for the success of safe and efficient
navigation. However, determining the ideal timing to execute replanning in such
partially unknown environments still remains an open question. In this work, we
first conduct an extensive simulation experiment to compare several common
replanning strategies and confirm that effective strategies are highly
dependent on the environment as well as the global and local planners. Based on
this insight, we derive a new adaptive replanning strategy based on deep
reinforcement learning, which can learn from experience to decide appropriate
replanning timings in the given environment and planning setups. Our
experimental results demonstrate that the proposed replanner can perform on par
or even better than the current best-performing strategies in multiple
situations regarding navigation robustness and efficiency.Comment: 7 pages, 3 figure
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, interactive user guidance for high-dimensional, constrained motion planning
We consider the problem of planning a collision-free path for a
high-dimensional robot. Specifically, we suggest a planning framework where a
motion-planning algorithm can obtain guidance from a user. In contrast to
existing approaches that try to speed up planning by incorporating experiences
or demonstrations ahead of planning, we suggest to seek user guidance only when
the planner identifies that it ceases to make significant progress towards the
goal. Guidance is provided in the form of an intermediate configuration
, which is used to bias the planner to go through . We
demonstrate our approach for the case where the planning algorithm is
Multi-Heuristic A* (MHA*) and the robot is a 34-DOF humanoid. We show that our
approach allows to compute highly-constrained paths with little domain
knowledge. Without our approach, solving such problems requires
carefully-crafting domain-dependent heuristics
Online, interactive user guidance for high-dimensional, constrained motion planning
We consider the problem of planning a collision-free path for a
high-dimensional robot. Specifically, we suggest a planning framework where a
motion-planning algorithm can obtain guidance from a user. In contrast to
existing approaches that try to speed up planning by incorporating experiences
or demonstrations ahead of planning, we suggest to seek user guidance only when
the planner identifies that it ceases to make significant progress towards the
goal. Guidance is provided in the form of an intermediate configuration
, which is used to bias the planner to go through . We
demonstrate our approach for the case where the planning algorithm is
Multi-Heuristic A* (MHA*) and the robot is a 34-DOF humanoid. We show that our
approach allows to compute highly-constrained paths with little domain
knowledge. Without our approach, solving such problems requires
carefully-crafting domain-dependent heuristics
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