Open AccessPosted Content
Geometrically Constrained Trajectory Optimization for Multicopters.
TL;DR: In this article, an optimization-based framework for multicopter trajectory planning subject to geometrical spatial constraints and user-defined dynamic constraints is presented, which is a novel trajectory representation built upon the novel optimality conditions for unconstrained control effort minimization.
read more
Abstract: We present an optimization-based framework for multicopter trajectory planning subject to geometrical spatial constraints and user-defined dynamic constraints. The basis of the framework is a novel trajectory representation built upon our novel optimality conditions for unconstrained control effort minimization. We design linear-complexity operations on this representation to conduct spatial-temporal deformation under various planning requirements. Smooth maps are utilized to exactly eliminate geometrical constraints in a lightweight fashion. A wide range of state-input constraints are supported by the decoupling of dense constraint evaluation from sparse parameterization, and backward differentiation of flatness map. As a result, the proposed framework transforms a generally constrained multicopter planning problem into an unconstrained optimization that can be solved reliably and efficiently. Our framework bridges the gaps among solution quality, planning frequency and constraint fidelity for a multicopter with limited resources and maneuvering capability. Its generality and robustness are both demonstrated by applications and experiments for different tasks. Extensive simulations and benchmarks are also conducted to show its capability of generating high-quality solutions while retaining the computation speed against other specialized methods by orders of magnitudes. Details and source code of our framework will be freely available at: this http URL.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Swarm of micro flying robots in the wild
Xin Zhou,Xiangyong Wen,Zhepei Wang,Yuman Gao,Haojia Li,Qianhao Wang,Tiankai Yang,Haojian Lu,Yanjun Cao,Chao Xu,Fei Gao +10 more
TL;DR: This work develops miniature but fully autonomous drones with a trajectory planner that can function in a timely and accurate manner based on limited information from onboard sensors and is integrated into the developed palm-sized swarm platform with onboard perception, localization, and control.
337
Fast-Racing: An Open-Source Strong Baseline for $\mathrm{SE}(3)$ Planning in Autonomous Drone Racing
Zhichao Han,Zhepei Wang,Neng Pan,Yi Lin,Chao Xu,Fei Gao +5 more
- 21 Sep 2021
TL;DR: In this article, the authors propose an open-source baseline for drone racing, which includes a high-performance trajectory planner and a challenging simulation platform tailored for the purpose of drone racing.
33
Bubble Planner: Planning High-speed Smooth Quadrotor Trajectories using Receding Corridors
23 Oct 2022
TL;DR: In this article , a motion planning algorithm based on the corridor-constrained minimum control effort trajectory optimization (MINCO) framework is proposed to plan high-speed quadrotor trajectories in real-time.
Autonomous Drone Racing: A Survey
Drew Hanover,Antonio Loquercio,Leonard Bauersfeld,Angel Romero,Robert Pěnička,Yi Song,Giovanni Cioffi,Elia Kaufmann,Davide Scaramuzza +8 more
TL;DR: Autonomous drone racing necessitates robust, safety-critical algorithms for high-speed navigation in complex and uncertain environments. The task involves extreme speeds and accelerations, raising challenges across perception, planning, control, and state estimation.
28
•Posted Content
FAST-Dynamic-Vision: Detection and Tracking Dynamic Objects with Event and Depth Sensing.
TL;DR: In this paper, a complete perception system including ego-motion compensation, object detection, and trajectory prediction for fast-moving dynamic objects with low latency and high precision is presented, and an efficient regression algorithm is designed for dynamic object detection.
23
References
•Book
Dynamic Programming and Optimal Control
Dimitri P. Bertsekas
- 01 May 1995
TL;DR: The leading and most up-to-date textbook on the far-ranging algorithmic methododogy of Dynamic Programming, which can be used for optimal control, Markovian decision problems, planning and sequential decision making under uncertainty, and discrete/combinatorial optimization.
On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming
TL;DR: A comprehensive description of the primal-dual interior-point algorithm with a filter line-search method for nonlinear programming is provided, including the feasibility restoration phase for the filter method, second-order corrections, and inertia correction of the KKT matrix.
On the limited memory BFGS method for large scale optimization
Dong C. Liu,Jorge Nocedal +1 more
TL;DR: The numerical tests indicate that the L-BFGS method is faster than the method of Buckley and LeNir, and is better able to use additional storage to accelerate convergence, and the convergence properties are studied to prove global convergence on uniformly convex problems.
Probabilistic roadmaps for path planning in high-dimensional configuration spaces
Lydia E. Kavraki,P. Svestka,Jean-Claude Latombe,Mark H. Overmars +3 more
- 01 Aug 1996
TL;DR: Experimental results show that path planning can be done in a fraction of a second on a contemporary workstation (/spl ap/150 MIPS), after learning for relatively short periods of time (a few dozen seconds).
The quickhull algorithm for convex hulls
TL;DR: This article presents a practical convex hull algorithm that combines the two-dimensional Quickhull algorithm with the general-dimension Beneath-Beyond Algorithm, and provides empirical evidence that the algorithm runs faster when the input contains nonextreme points and that it used less memory.