8,166 research outputs found
Path Planning for Motion Dependent State Estimation on Micro Aerial Vehicles
Abstract — With navigation algorithms reaching a certain maturity in the field of mobile robots, the community now focuses on more advanced tasks like path planning towards increased autonomy. While the goal is to efficiently compute a path to a target destination, the uncertainty in the robot’s perception cannot be ignored if a realistic path is to be computed. With most state of the art navigation systems providing the uncertainty in motion estimation, here we propose to exploit this information. This leads to a system that can plan safe avoidance of obstacles, and more importantly, it can actively aid navigation by choosing a path that minimizes the uncertainty in the monitored states. Our proposed approach is applicable to systems requiring certain excitations in order to render all their states observable, such as a MAV with visual-inertial based localization. In this work, we propose an approach which takes into account this necessary motion during path planning: by employing Rapidly exploring Random Belief Trees (RRBT), the proposed approach chooses a path to a goal which allows for best estimation of the robot’s states, while inherently avoiding motion in unobservable modes. We discuss our findings within the scenario of vision-based aerial navigation as one of the most challenging navigation problem, requiring sufficient excitation to reach full observability. I
Aerial navigation in obstructed environments with embedded nonlinear model predictive control
We propose a methodology for autonomous aerial navigation and obstacle
avoidance of micro aerial vehicles (MAV) using nonlinear model predictive
control (NMPC) and we demonstrate its effectiveness with laboratory
experiments. The proposed methodology can accommodate obstacles of arbitrary,
potentially non-convex, geometry. The NMPC problem is solved using PANOC: a
fast numerical optimization method which is completely matrix-free, is not
sensitive to ill conditioning, involves only simple algebraic operations and is
suitable for embedded NMPC. A C89 implementation of PANOC solves the NMPC
problem at a rate of 20Hz on board a lab-scale MAV. The MAV performs smooth
maneuvers moving around an obstacle. For increased autonomy, we propose a
simple method to compensate for the reduction of thrust over time, which comes
from the depletion of the MAV's battery, by estimating the thrust constant
Autonomous Recharging and Flight Mission Planning for Battery-operated Autonomous Drones
Autonomous drones (also known as unmanned aerial vehicles) are increasingly
popular for diverse applications of light-weight delivery and as substitutions
of manned operations in remote locations. The computing systems for drones are
becoming a new venue for research in cyber-physical systems. Autonomous drones
require integrated intelligent decision systems to control and manage their
flight missions in the absence of human operators. One of the most crucial
aspects of drone mission control and management is related to the optimization
of battery lifetime. Typical drones are powered by on-board batteries, with
limited capacity. But drones are expected to carry out long missions. Thus, a
fully automated management system that can optimize the operations of
battery-operated autonomous drones to extend their operation time is highly
desirable. This paper presents several contributions to automated management
systems for battery-operated drones: (1) We conduct empirical studies to model
the battery performance of drones, considering various flight scenarios. (2) We
study a joint problem of flight mission planning and recharging optimization
for drones with an objective to complete a tour mission for a set of sites of
interest in the shortest time. This problem captures diverse applications of
delivery and remote operations by drones. (3) We present algorithms for solving
the problem of flight mission planning and recharging optimization. We
implemented our algorithms in a drone management system, which supports
real-time flight path tracking and re-computation in dynamic environments. We
evaluated the results of our algorithms using data from empirical studies. (4)
To allow fully autonomous recharging of drones, we also develop a robotic
charging system prototype that can recharge drones autonomously by our drone
management system
Routing Unmanned Vehicles in GPS-Denied Environments
Most of the routing algorithms for unmanned vehicles, that arise in data
gathering and monitoring applications in the literature, rely on the Global
Positioning System (GPS) information for localization. However, disruption of
GPS signals either intentionally or unintentionally could potentially render
these algorithms not applicable. In this article, we present a novel method to
address this difficulty by combining methods from cooperative localization and
routing. In particular, the article formulates a fundamental combinatorial
optimization problem to plan routes for an unmanned vehicle in a GPS-restricted
environment while enabling localization for the vehicle. We also develop
algorithms to compute optimal paths for the vehicle using the proposed
formulation. Extensive simulation results are also presented to corroborate the
effectiveness and performance of the proposed formulation and algorithms.Comment: Publised in International Conference on Umanned Aerial System
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