Receding horizon path planning with implicit safety guarantees

Schouwenaars, Tom; How, Jonathan P.; Féron, Éric

doi:10.23919/acc.2004.1384742

Cited by 146 publications

(122 citation statements)

References 7 publications

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“…For winged UAVs it could perform a continuous flight in a circle of a given radius. A similar concept is used in the form of basis states as hard terminal constraints, which provides implicit safety guarantees to ensure feasibility of a receding horizon path planning scheme [9]. Without loss of generality, in this paper we assume that an emergency maneuver started at t e brings the vehicle to the position which it had at time t e and zero terminal speed, i.e., r e = x te,pos , 0 .…”

Section: Uav Emergency Maneuver and Invariant Setmentioning

confidence: 99%

Collision-free UAV formation flight using decentralized optimization and invariant sets

Borrelli

Keviczky

Balas

2004

2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601)

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Abstract-We consider the problem of formation flight for a set of Unmanned Air Vehicles (UAV). We propose a decentralized control design procedure which guarantees collision avoidance and constraint fulfillment. The control design is based on a decentralized Receding Horizon Control (RHC) scheme [1]. Vehicle collision avoidance is ensured by considering a collision-free emergency maneuver which is implemented when feasibility of the decentralized RHC scheme is lost. Bounds on speed and accelerations are computed offline using simple polyhedral invariant set computations. Such bounds guarantee that the implementation of the emergency maneuver leads to collision-free trajectories.The proposed decentralized control scheme is formulated as mixed-integer linear programs of small sizes which can be translated into equivalent piecewise affine state-feedback controllers. These controllers can be implemented in real-time once the corresponding look-up tables are downloaded to the hardware platform of the UAVs.

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Section: Uav Emergency Maneuver and Invariant Setmentioning

confidence: 99%

Collision-free UAV formation flight using decentralized optimization and invariant sets

Borrelli

Keviczky

Balas

2004

2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601)

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“…4(b), multi-planes are used to approximate the nonlinear space as shown in Eq. (17). In fact, it is a modified version of 2D, 16 which needs to approximate a circle,…”

Section: Constraints On Feasible Regionmentioning

confidence: 99%

“…Unfortunately, it cannot fit three dimensions because the extension needs to define cylindrical or spherical coordinate systems for the problem that is intractable. Schouwenaars et al 17 proposed an MILP formulation with receding horizon strategy, where a minimum velocity and a limited turn rate of aircraft are constrained. However, their results still focused on two dimensions.…”

Section: Introductionmentioning

confidence: 99%

“…But this paper has no intention to discuss the filtering algorithm for achieving the feasible data of on-board sensors. The trajectory from a start to a destination location typically needs to be computed gradually over time in the fashion of receding horizon 17 in which a new waypoint of the total path is computed at each time step by solving an LP problem. The target and obstacles, probably static or dynamic, are modelled by spheres having certain radius for their impact spectrum, and the vehicle is modelled by a mass point.…”

Section: Introductionmentioning

confidence: 99%

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Three-dimensional path planning for unmanned aerial vehicle based on linear programming

2011

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SUMMARYIn this paper, an approach based on linear programming (LP) is proposed for path planning in three-dimensional space, in which an aerial vehicle is requested to pursue a target while avoiding static or dynamic obstacles. This problem is very meaningful for many aerial robots, such as unmanned aerial vehicles. First, the tasks of target-pursuit and obstacle-avoidance are modelled with linear constraints in relative coordination according to LP formulation. Then, two weighted cost functions, representing the optimal velocity resolution, are integrated into the final objective function. This resolution, defined to achieve the optimal velocity, deals with the optimization of a pair of orthogonal vectors. Some constraints, such as boundaries of the vehicle velocity, acceleration, sensor range, and flying height, are considered in this method. A number of simulations, under static and dynamic environments, are carried out to validate the performance of generating optimal trajectory in real time. Compared with ant colony optimization algorithm and genetic algorithm, our method has less parameters to tune and can achieve better performance in real-time application.

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“…This method plans the variables of the linear acceleration and the angular acceleration of a ground vehicle. SCHOUWENAARS, et al, proposed a MILP formulation with receding horizon strategy where a minimum velocity and a limited turn rate of aircraft are constrained [11] . These methods are common in terms that they work in two-dimensional scenarios.…”

mentioning

confidence: 99%

Quadratic programming-based approach for autonomous vehicle path planning in space

Chen

Han

2012

Chin. J. Mech. Eng.

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Path planning for space vehicles is still a challenging problem although considerable progress has been made over the past decades. The major difficulties are that most of existing methods only adapt to static environment instead of dynamic one, and also can not solve the inherent constraints arising from the robot body and the exterior environment. To address these difficulties, this research aims to provide a feasible trajectory based on quadratic programming(QP) for path planning in three-dimensional space where an autonomous vehicle is requested to pursue a target while avoiding static or dynamic obstacles. First, the objective function is derived from the pursuit task which is defined in terms of the relative distance to the target, as well as the angle between the velocity and the position in the relative velocity coordinates(RVCs). The optimization is in quadratic polynomial form according to QP formulation. Then, the avoidance task is modeled with linear constraints in RVCs. Some other constraints, such as kinematics, dynamics, and sensor range, are included. Last, simulations with typical multiple obstacles are carried out, including in static and dynamic environments and one of human-in-the-loop. The results indicate that the optimal trajectories of the autonomous robot in three-dimensional space satisfy the required performances. Therefore, the QP model proposed in this paper not only adapts to dynamic environment with uncertainty, but also can satisfy all kinds of constraints, and it provides an efficient approach to solve the problems of path planning in three-dimensional space.

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Receding horizon path planning with implicit safety guarantees

Cited by 146 publications

References 7 publications

Collision-free UAV formation flight using decentralized optimization and invariant sets

Collision-free UAV formation flight using decentralized optimization and invariant sets

Three-dimensional path planning for unmanned aerial vehicle based on linear programming

Quadratic programming-based approach for autonomous vehicle path planning in space

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