Abstract-Operating micro aerial vehicles (MAVs) outside of the bounds of a rigidly controlled lab environment, specifically one that is unstructured and contains unknown obstacles, poses a number of challenges. One of these challenges is that of quickly determining an optimal (or nearly so) path from the MAVs current position to a designated goal state. Past work in this area using full-size unmanned aerial vehicles (UAVs) has predominantly been performed in benign environments. However, due to their small size, MAVs are capable of operating in indoor environments which are more cluttered. This requires planners to account for the vehicle heading in addition to its spatial position in order to successfully navigate. In addition, due to the short flight times of MAVs along with the inherent hazards of operating in close proximity to obstacles, we desire the trajectories to be as cost-optimal as possible. Our approach uses an anytime planner based on A that performs a graph search on a four-dimensional (4-D) (x,y,z,heading) lattice. This allows for the generation of close-to-optimal trajectories based on a set of precomputed motion primitives along with the capability to provide trajectories in real-time allowing for onthe-fly re-planning as new sensor data is received. We also account for arbitrary vehicle shapes, permitting the use of a noncircular footprint during the planning process. By not using the overly conservative circumscribed circle for collision checking, we are capable of successfully finding optimal paths through cluttered environments including those with narrow hallways. Analytically, we show that our planner provides bounds on the sub-optimality of the solution it finds. Experimentally, we show that the planner can operate in real-time in both a simulated and real-world cluttered environments.
Abstract-State lattice-based planning has been used in navigation for ground, water, aerial and space robots. State lattices are typically constructed of simple motion primitives connecting one state to another. There are situations where these metric motions may not be available, such as in GPSdenied areas. In many of these cases, however, the robot may have some additional sensing capability that is not being fully utilized by the planner. For example, if the robot has a camera it may be able to use simple visual servoing techniques to navigate through a GPS-denied region. Likewise, a LIDAR may allow the robot to skirt along an environmental feature even if there is not enough information to generate an accurate pose estimate. In this paper we present an expansion of the state lattice framework that allows us to incorporate controller-based motion primitives and external perceptual triggers directly into the planning process. We provide a formal description of our method of constructing the search graph in these cases as well as presenting real-world and simulated testing data showing the practical application of this approach.
Autonomous flight of unmanned full‐size rotor‐craft has the potential to enable many new applications. However, the dynamics of these aircraft, prevailing wind conditions, the need to operate over a variety of speeds and stringent safety requirements make it difficult to generate safe plans for these systems. Prior work has shown results for only parts of the problem. Here we present the first comprehensive approach to planning safe trajectories for autonomous helicopters from takeoff to landing. Our approach is based on two key insights. First, we compose an approximate solution by cascading various modules that can efficiently solve different relaxations of the planning problem. Our framework invokes a long‐term route optimizer, which feeds a receding‐horizon planner which in turn feeds a high‐fidelity safety executive. Secondly, to deal with the diverse planning scenarios that may arise, we hedge our bets with an ensemble of planners. We use a data‐driven approach that maps a planning context to a diverse list of planning algorithms that maximize the likelihood of success. Our approach was extensively evaluated in simulation and in real‐world flight tests on three different helicopter systems for duration of more than 109 autonomous hours and 590 pilot‐in‐the‐loop hours. We provide an in‐depth analysis and discuss the various tradeoffs of decoupling the problem, using approximations and leveraging statistical techniques. We summarize the insights with the hope that it generalizes to other platforms and applications.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.