UAV Optimal Cooperative Target Tracking and Collision Avoidance of Moving Objects

Prévost, Carole Gabrielle; Desbiens, André; Gagnon, Éric; Hodouin, D.

doi:10.3182/20080706-5-kr-1001.00965

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…This paper described a semi-autonomous hazard avoidance framework that performs trajectory planning, threat assessment, and shared-adaptive control among both static and dynamic hazards. While accounting for hazard motion is not particularly new or novel (unmanned aerial vehicle research has been accounting for hazard motion for years) [21], the integration of multiple static and dynamic obstacles, together with stability considerations and dynamic constraints into a compact corridor representation is relatively unexplored, particularly in the semiautonomous ground vehicle applications. Moving hazards were factored into the constrained optimal control problem by predicting their position at intersection with the host vehicle and mapping this region into corridor-like constraints on the host vehicle's lateral position.…”

Section: Resultsmentioning

confidence: 99%

Semi-Autonomous Avoidance of Moving Hazards for Passenger Vehicles

Anderson

Peters

Pilutti

et al. 2010

ASME 2010 Dynamic Systems and Control Conference, Volume 1

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This paper presents a method for semi-autonomous hazard avoidance in the presence of unknown moving obstacles and unpredictable driver inputs. This method iteratively predicts the motion and anticipated intersection of the host vehicle with both static and dynamic hazards and excludes projected collision states from a traversable corridor. A model predictive controller iteratively replans a stability-optimal trajectory through the navigable region of the environment while a threat assessor and semi-autonomous control law modulate driver and controller inputs to maintain stability, preserve controllability, and ensure safe hazard avoidance. The efficacy of this approach is demonstrated through both simulated and experimental results using a semi-autonomously controlled Jaguar S-Type.

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Section: Resultsmentioning

confidence: 99%

Semi-Autonomous Avoidance of Moving Hazards for Passenger Vehicles

Anderson

Peters

Pilutti

et al. 2010

ASME 2010 Dynamic Systems and Control Conference, Volume 1

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“…In this work we implemented the so-called extended Kalman filter (EKF for short) (see e.g. [6,42,38]) which is well-known for its robustness w.r.t. the noise.…”

Section: Description Of the Algorithmmentioning

confidence: 99%

Lyapunov and Minimum-Time Path Planning for Drones

et al. 2014

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In this paper, we study the problem of controlling an unmanned aerial vehicle (UAV) to provide a target supervision and/or to provide convoy protection to ground vehicles.We first present a control strategy based upon a Lyapunov-LaSalle stabilization method to provide supervision of a stationary target. The UAV is expected to join a pre-designed admissible circular trajectory around the target which is itself a fixed point in the space.Our strategy is presented for both HALE (High Altitude Long Endurance) and MALE (Medium Altitude Long Endurance) types UAVs. A UAV flying at a constant altitude (HALE type) is modeled as a Dubins vehicle (i.e. a planar vehicle with constrained turning radius and constant forward velocity). For a UAV that might change its altitude (MALE type), we use the general kinematic model of a rigid body evolving in R 3 . Both control strategies presented are smooth and unlike what is usually proposed in the literature these strategies asymptotically track a circular trajectory of exact minimum turning radius.We also present the time-optimal control synthesis for tracking a circle by a Dubins vehicle. This optimal strategy, although much simpler than the point-to-point time-optimal strategy obtained by P. Souères and J.-P. Laumond in the 1990s (see [45]), is very rich.Finally, we propose control strategies to provide supervision of a moving target, that are based upon the previous ones.

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