28 research outputs found
Trajectory Replanning for Quadrotors Using Kinodynamic Search and Elastic Optimization
We focus on a replanning scenario for quadrotors where considering time
efficiency, non-static initial state and dynamical feasibility is of great
significance. We propose a real-time B-spline based kinodynamic (RBK) search
algorithm, which transforms a position-only shortest path search (such as A*
and Dijkstra) into an efficient kinodynamic search, by exploring the properties
of B-spline parameterization. The RBK search is greedy and produces a
dynamically feasible time-parameterized trajectory efficiently, which
facilitates non-static initial state of the quadrotor. To cope with the
limitation of the greedy search and the discretization induced by a grid
structure, we adopt an elastic optimization (EO) approach as a
post-optimization process, to refine the control point placement provided by
the RBK search. The EO approach finds the optimal control point placement
inside an expanded elastic tube which represents the free space, by solving a
Quadratically Constrained Quadratic Programming (QCQP) problem. We design a
receding horizon replanner based on the local control property of B-spline. A
systematic comparison of our method against two state-of-the-art methods is
provided. We integrate our replanning system with a monocular vision-based
quadrotor and validate our performance onboard.Comment: 8 pages. Published in International Conference on Robotics and
Automation (ICRA) 2018. IEEE copyrigh
Survey on Motion Planning for Multirotor Aerial Vehicles in Plan-based Control Paradigm
In general, optimal motion planning can be performed both locally and globally. In such a planning, the choice in favour of either local or global planning technique mainly depends on whether the environmental conditions are dynamic or static. Hence, the most adequate choice is to use local planning or local planning alongside global planning. When designing optimal motion planning both local and global, the key metrics to bear in mind are execution time, asymptotic optimality, and quick reaction to dynamic obstacles. Such planning approaches can address the aforesaid target metrics more efficiently compared to other approaches such as path planning followed by smoothing. Thus, the foremost objective of this study is to analyse related literature in order to understand how the motion planning, especially trajectory planning, problem is formulated, when being applied for generating optimal trajectories in real-time for Multirotor Aerial Vehicles, impacts the listed metrics. As a result of the research, the trajectory planning problem was broken down into a set of subproblems, and the lists of methods for addressing each of the problems were identified and described in detail. Subsequently, the most prominent results from 2010 to 2022 were summarized and presented in the form of a timeline