Robust hybrid control for autonomous vehicle motion planning

By Emilio Frazzoli, Munther A. Dahleh, Eric M. Feron. Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187), 2000

377 citations recorded by the source corpus.

The operation of an autonomous vehicle in an unknown, dynamic environment is a very complex problem, especially when the vehicle is required to use its full maneuvering capabilities, and to react in real time to changes in the operational environment. A possible approach to reduce the computational complexity of the motion planning problem for a nonlinear, high dimensional system, is based on a quantization of the system dynamics, leading to a control architecture based on a hybrid automaton, the states of which represent feasible trajectory primitives for the vehicle. The paper focuses on the feasibility of this approach, in the presence of disturbances and uncertainties in the plant and/or in the environment: the structure of a robust hybrid automaton is defined and its properties are analyzed. In particular, we address the issues of well-posedness, consistency and reachability. For the case of autonomous vehicles, we provide sufficient conditions to guarantee reachability of the automaton.

Subfields: Computational Theory and Mathematics, Computer Vision and Pattern Recognition, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/2e17afecf3253ac777ab6ed9a6dd274f165d0a94

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Citations in the corpus

Autonomous Vehicle Technologies for Small Fixed-Wing UAVs (2003, methodology): “Through analysis of ∞ight data on our UAVs the following have been determined to be decent measurements of roll and pitch: µm = sin i1 µ Axi 0:2Vpq g ¶ ; ( 7 ) `m = sin i1 µ iAy + 0:4Vpr g cosµm ¶ : (8)”
Dynamically-Stable Motion Planning for Humanoid Robots (2002, background): “Recently, randomized motion planning algorithms that account for system dynamics have been developed for 2D and 3D rigid bodies with state spaces of up to twelve dimensions [LK99, LK00], for a 3D helicopter model [ FDF99 ], and for a circular disc in 2D among circular moving obstacles [KHLR00].”
Stable Synchronization of Mechanical System Networks (2008, background): “In [6], an extension to the previous work of [5] on unmanned aerial vehicle motion planning is presented address time-varying and directed communication topologies and/or the presence of leaders in the group.”
Maneuver-based motion planning for nonlinear systems with symmetries (2005, background): “, aircraft) are given in [19], and are not reported here due to space limitations.”
Rapidly-exploring Random Belief Trees for motion planning under uncertainty (2011, background): “For flying vehicles, maneuver primitive approaches are appealing due to their relative computational efficiency [21].”
Temporal logic motion planning for dynamic robots (2009, methodology): “We propose a hierarchical synthesis approach which consists of three components : tracking control using approximate simulation relations [20], robust satisfaction of RTL formulas [12] and hybrid control for motion planning [14,13].”
Planning algorithms (2006, methodology): “For more works on kinodynamic planning, see [205, 239, 291, 358, 362, 614, 783, 999].”
Sigma-point kalman filters for probabilistic inference in dynamic state-space models (2004, methodology): “The current state-of-the-art probabilistic inference system used for such UAV guidance and navigation systems are usually EKF based [56, 58, 42].”
Randomized Kinodynamic Planning (1999, methodology): “Recently, our planner was applied to automating the flight of helicopters in complicated three-dimensional simulations that contain obstacles (Frazzoli, Dahleh, and Feron 1999).”
Symbolic Robustness Analysis (2009, background): “Robustness of hybrid automaton models for control systems have been studied before [9], [10], as have test generation for control software based on symbolic techniques [11], [12].”
Contextual Sequence Prediction with Application to Control Library Optimization (2012, background): “Similarly, recording demonstrated trajectories of experts aggressively flying unmanned aerial vehicles (UAVs) has enabled dynamically feasible trajectories to be quickly generated by concatenating a suitable subset of stored trajectories in the control library [10].”
A perception‐driven autonomous urban vehicle (2008, methodology): “To efficiently generate the path in the dynamic and uncertain environment, several extensions have been made (Frazzoli, 2001) to the standard RRT, as discussed in the subsections below.”
Multipartite RRTs for Rapid Replanning in Dynamic Environments (2007, neutral): “The approach of limiting the number of samples per iteration to a relatively small number while allowing a relatively large maxiumum tree size is an example of receding horizon control, as seen applied to dynamic planning in [16].”