The obstacle avoidance motion planning problem for autonomous vehicles: A low-demanding receding horizon control scheme

Franzè, Giuseppe; Lúcia, Walter

doi:10.1016/j.sysconle.2014.12.007

Cited by 29 publications

(7 citation statements)

References 30 publications

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“…In AV, the driving environment perception, cognition map, path planning and strategy control are the equivalent important task in AV [42][43][44]. How to drive like human beings is the most important task.…”

Section: Key Technologies In Avmentioning

confidence: 99%

Survey on Artificial Intelligence for Vehicles

Cheng

Guo

et al. 2018

Automot. Innov.

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With the ever-increasing demand in urban mobility and modern logistics sector, the vehicle population has been steadily growing over the past several decades. One natural consequence of the vehicle population growth is the increase in traffic congestion. Almost all (metropolitan) cities including the major ones, like Los Angeles, Beijing, New York, are suffering from heavy traffic congestion. Statistics show that, in 2015, 43 cities in China are suffering a prolonged travel time of more than 1.5 h every day during rush hours. In the meanwhile, traffic accidents are plaguing the economic development as well.

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Section: Key Technologies In Avmentioning

confidence: 99%

Survey on Artificial Intelligence for Vehicles

Cheng

Guo

et al. 2018

Automot. Innov.

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“…Our corner cutting avoidance procedure is part of an online scheme where collision avoidance is dealt with "as it comes." Alternatives popular in the community consider a priori path planning followed by subsequent tracking (eg, via command governors 22,23 or dynamic constraint activation 24 ). These approaches have the advantage of guaranteed feasibility (under certain assumptions) but may also make conservative restrictions on the agents' allowed positions.…”

Section: Study Case: An Mpc Problem With Corner Cutting Avoidancementioning

confidence: 99%

Exact and overapproximated guarantees for corner cutting avoidance in a multiobstacle environment

Stoican

Prodan

Grøtli

2018

Intl J Robust & Nonlinear

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The corner cutting avoidance problem is an important but often overlooked part of motion planning strategies. Obstacle and collision avoidance constraints are usually imposed at the sampling time without regards to the intrasample behavior of the agent. Hence, it is possible for an agent to "cut the corner" of an obstacle while apparently respecting the constraints. This paper improves upon state of the art by providing exact and overapproximated descriptions of the undershadow (and of its complement, the visible) region generated by an agent against obstacles. We employ a hyperplane arrangement construction to handle multiple obstacles simultaneously and provide piecewise descriptions of the regions of interest and parametrizations of the corner cutting conditions (useful, eg, in finite horizon optimization problems). Mixed-integer representations are used to describe the regions of interest, leading in the overapproximated case to binary-only constraints. Illustrative proofs of concept, comparisons with the state of the art, and simulations over a standard multiobstacle avoidance problem showcase the benefits of the proposed approach. KEYWORDS corner cutting problem, hyperplane arrangement, mixed-integer programming (MIP), model predictive control (MPC), multiobstacle avoidanceRecent advances in computational resources and the proliferation of (semi)autonomous vehicles has lent new interest to the topic of obstacle and collision avoidance in motion planning strategies. One of the major issues is that avoidance constraints lead to a nonconvex feasible domain. It is worth underlining that such formulations are intrinsic to the problem and cannot be avoided. [1][2][3] This paper concentrates on the "corner cutting" issue, ie, avoidance constraints are checked at each sampling time, but the control input is ultimately applied (eg, via a zero-order hold block) to continuous dynamics. Hence, the intrasample behavior of the agent (the autonomous vehicle to be steered) cannot be overlooked. While alluring, obstacle enlargement techniques 4 or sampling time reduction are not always appropriate. The former increases the conservatism of the formulation (potentially leading to infeasibility), and the latter reduces the time available for computing the input.The computational limitations are particularly troublesome since modeling the associated optimization problem is often done via the mixed-integer (MI) programming framework, 5,6 which scales badly with problem size and complexity (ie, number of obstacles). 4528

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“…In practice, especially in electronics, there are many circumstances in which various parameters must maintain distinct values, such as, for example, switching time settings for the transistors of an H‐bridge, whose coincidence would result in a short circuit. Another, more intuitive example is in automatic collision avoidance between autonomous vehicles, or between vehicles and obstacles . Here, we briefly consider the application of Theorem for collision avoidance between multiple parameters.…”

Section: D Angular Velocity Actuated Vehiclementioning

confidence: 99%

Bounded extremum seeking for angular velocity actuated control of nonholonomic unicycle

Scheinker

2016

Optim. Control Appl. Meth.

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Summary We study control of the angular‐velocity actuated nonholonomic unicycle, via a simple, bounded extremum seeking controller which is robust to external disturbances and measurement noise. The vehicle performs source seeking despite not having any position information about itself or the source, able only to sense a noise corrupted scalar value whose extremum coincides with the unknown source location. In order to control the angular velocity, rather than the angular heading directly, a controller is developed such that the closed loop system exhibits multiple time scales and requires an analysis approach expanding the previous work of Kurzweil, Jarnik, Sussmann, and Liu, utilizing weak limits. We provide analytic proof of stability and demonstrate how this simple scheme can be extended to include position‐independent source seeking, tracking, and collision avoidance of groups on autonomous vehicles in GPS‐denied environments, based only on a measure of distance to an obstacle, which is an especially important feature for an autonomous agent. Copyright © 2016 John Wiley & Sons, Ltd.

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The obstacle avoidance motion planning problem for autonomous vehicles: A low-demanding receding horizon control scheme

Cited by 29 publications

References 30 publications

Survey on Artificial Intelligence for Vehicles

Survey on Artificial Intelligence for Vehicles

Exact and overapproximated guarantees for corner cutting avoidance in a multiobstacle environment

Bounded extremum seeking for angular velocity actuated control of nonholonomic unicycle

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