2,055 research outputs found
FASTER: Fast and Safe Trajectory Planner for Flights in Unknown Environments
High-speed trajectory planning through unknown environments requires
algorithmic techniques that enable fast reaction times while maintaining safety
as new information about the operating environment is obtained. The requirement
of computational tractability typically leads to optimization problems that do
not include the obstacle constraints (collision checks are done on the
solutions) or use a convex decomposition of the free space and then impose an
ad-hoc time allocation scheme for each interval of the trajectory. Moreover,
safety guarantees are usually obtained by having a local planner that plans a
trajectory with a final "stop" condition in the free-known space. However,
these two decisions typically lead to slow and conservative trajectories. We
propose FASTER (Fast and Safe Trajectory Planner) to overcome these issues.
FASTER obtains high-speed trajectories by enabling the local planner to
optimize in both the free-known and unknown spaces. Safety guarantees are
ensured by always having a feasible, safe back-up trajectory in the free-known
space at the start of each replanning step. Furthermore, we present a Mixed
Integer Quadratic Program formulation in which the solver can choose the
trajectory interval allocation, and where a time allocation heuristic is
computed efficiently using the result of the previous replanning iteration.
This proposed algorithm is tested extensively both in simulation and in real
hardware, showing agile flights in unknown cluttered environments with
velocities up to 3.6 m/s.Comment: IROS 201
Dynamic Motion Planning for Aerial Surveillance on a Fixed-Wing UAV
We present an efficient path planning algorithm for an Unmanned Aerial
Vehicle surveying a cluttered urban landscape. A special emphasis is on
maximizing area surveyed while adhering to constraints of the UAV and partially
known and updating environment. A Voronoi bias is introduced in the
probabilistic roadmap building phase to identify certain critical milestones
for maximal surveillance of the search space. A kinematically feasible but
coarse tour connecting these milestones is generated by the global path
planner. A local path planner then generates smooth motion primitives between
consecutive nodes of the global path based on UAV as a Dubins vehicle and
taking into account any impending obstacles. A Markov Decision Process (MDP)
models the control policy for the UAV and determines the optimal action to be
undertaken for evading the obstacles in the vicinity with minimal deviation
from current path. The efficacy of the proposed algorithm is evaluated in an
updating simulation environment with dynamic and static obstacles.Comment: Accepted at International Conference on Unmanned Aircraft Systems
201
Real-Time Planning with Multi-Fidelity Models for Agile Flights in Unknown Environments
Autonomous navigation through unknown environments is a challenging task that
entails real-time localization, perception, planning, and control. UAVs with
this capability have begun to emerge in the literature with advances in
lightweight sensing and computing. Although the planning methodologies vary
from platform to platform, many algorithms adopt a hierarchical planning
architecture where a slow, low-fidelity global planner guides a fast,
high-fidelity local planner. However, in unknown environments, this approach
can lead to erratic or unstable behavior due to the interaction between the
global planner, whose solution is changing constantly, and the local planner; a
consequence of not capturing higher-order dynamics in the global plan. This
work proposes a planning framework in which multi-fidelity models are used to
reduce the discrepancy between the local and global planner. Our approach uses
high-, medium-, and low-fidelity models to compose a path that captures
higher-order dynamics while remaining computationally tractable. In addition,
we address the interaction between a fast planner and a slower mapper by
considering the sensor data not yet fused into the map during the collision
check. This novel mapping and planning framework for agile flights is validated
in simulation and hardware experiments, showing replanning times of 5-40 ms in
cluttered environments.Comment: ICRA 201
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
Adaptive dynamic path re-planning RRT algorithms with game theory for UAVs
The main aim of this paper is to describe an adaptive re-planning algorithm based on a RRT and Game Theory to produce an efficient collision free obstacle adaptive Mission Path Planner for Search and Rescue (SAR) missions. This will provide UAV autopilots and flight computers with the capability to autonomously avoid static obstacles and No Fly Zones (NFZs) through dynamic adaptive path replanning. The methods and algorithms produce optimal collision free paths and can be integrated on a decision aid tool and UAV autopilots
Motion planning with dynamics awareness for long reach manipulation in aerial robotic systems with two arms
Human activities in maintenance of industrial plants pose elevated risks as well as significant costs due to the required shutdowns of the facility. An aerial robotic system with two arms for long reach manipulation in cluttered environments is presented to alleviate these constraints. The system consists of a multirotor with a long bar extension that incorporates a lightweight dual arm in the tip. This configuration allows aerial manipulation tasks even in hard-to-reach places. The objective of this work is the development of planning strategies to move the aerial robotic system with two arms for long reach manipulation in a safe and efficient way for both navigation and manipulation tasks. The motion planning problem is addressed considering jointly the aerial platform and the dual arm in order to achieve wider operating conditions. Since there exists a strong dynamical coupling between the multirotor and the dual arm, safety in obstacle avoidance will be assured by introducing dynamics awareness in the operation of the planner. On the other hand, the limited maneuverability of the system emphasizes the importance of energy and time efficiency in the generated trajectories. Accordingly, an adapted version of the optimal Rapidly-exploring Random Tree algorithm has been employed to guarantee their optimality. The resulting motion planning strategy has been evaluated through simulation in two realistic industrial scenarios, a riveting application and a chimney repairing task. To this end, the dynamics of the aerial robotic system with two arms for long reach manipulation has been properly modeled, and a distributed control scheme has been derived to complete the test bed. The satisfactory results of the simulations are presented as a first validation of the proposed approach.Unión Europea H2020-644271Ministerio de Ciencia, Innovación y Universidades DPI2014-59383-C2-1-
UAV/UGV Autonomous Cooperation: UAV Assists UGV to Climb a Cliff by Attaching a Tether
This paper proposes a novel cooperative system for an Unmanned Aerial Vehicle
(UAV) and an Unmanned Ground Vehicle (UGV) which utilizes the UAV not only as a
flying sensor but also as a tether attachment device. Two robots are connected
with a tether, allowing the UAV to anchor the tether to a structure located at
the top of a steep terrain, impossible to reach for UGVs. Thus, enhancing the
poor traversability of the UGV by not only providing a wider range of scanning
and mapping from the air, but also by allowing the UGV to climb steep terrains
with the winding of the tether. In addition, we present an autonomous framework
for the collaborative navigation and tether attachment in an unknown
environment. The UAV employs visual inertial navigation with 3D voxel mapping
and obstacle avoidance planning. The UGV makes use of the voxel map and
generates an elevation map to execute path planning based on a traversability
analysis. Furthermore, we compared the pros and cons of possible methods for
the tether anchoring from multiple points of view. To increase the probability
of successful anchoring, we evaluated the anchoring strategy with an
experiment. Finally, the feasibility and capability of our proposed system were
demonstrated by an autonomous mission experiment in the field with an obstacle
and a cliff.Comment: 7 pages, 8 figures, accepted to 2019 International Conference on
Robotics & Automation. Video: https://youtu.be/UzTT8Ckjz1
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