Implementation of Fast MPC with a Quadrotor for Obstacle Avoidance

Greatwood, Colin; Richards, Arthur

doi:10.2514/6.2013-4790

Cited by 7 publications

(3 citation statements)

References 21 publications

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“…The key features of quadrotor aircrafts lie in its flexibility and maneuverability so it comes out that we do not need to think much about the restrictions of the minimum turning radius and the overload problem while turning. As a result, a four-rotor aircraft can be regarded as a controllable particle, 19 without considering the rotation around center of mass. So, the kinematic model of UAV is defined by its position ( x uav , y uav ) , orientation θ and velocity vector V true→ uav (see Figure 2).…”

Section: Problem Statements and Uav Modelingmentioning

confidence: 99%

Robust path planning for avoiding obstacles using time-environment dynamic map

Zhao

2019

Measurement and Control

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Small unmanned aerial vehicles are widely used in urban space because of its flexibility and maneuverability. However, there are full of dynamic obstacles and immobile obstacles which will affect safe flying in urban space. In this paper, a novel integrated path planning approach for unmanned aerial vehicles is presented, which is consisted of three steps. First, a time-environment dynamic map is constructed to represent obstacles by introducing time axis. Second, unmanned aerial vehicles’ flyable paths are explored based on breadth-first algorithm. Third, a path planning method using A* algorithm and local trace-back model is designed in order to discover sub-optimal feasible path rapidly in unmanned aerial vehicles’ field of view. Finally, the simulation results have illustrated that the proposed method can ensure unmanned aerial vehicles’ autonomous path planning safely and effectively in urban space crowded with obstacles.

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Section: Problem Statements and Uav Modelingmentioning

confidence: 99%

Robust path planning for avoiding obstacles using time-environment dynamic map

Zhao

2019

Measurement and Control

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“…1) or some other catastrophic event, such as rollover in the case of a ground vehicle [3]. Despite this, some planners designed to avoid static obstacles for UAV applications [4] utilize a kinematic vehicle model (Case A, Fig. 1).…”

Section: Introductionmentioning

confidence: 99%

“…This work uses a nonlinear model predictive control (NMPC)-based trajectory planner; this approach is also used in [4], [5], [9], [10], [11], [17], [12], [13], [14], [15]. Unfortunately, it is very difficult to solve the proposed planning formulation in real-time with a short execution horizon.…”

Section: Introductionmentioning

confidence: 99%

Real-time trajectory planning for automated vehicle safety and performance in dynamic environments

Febbo¹,

Jayakumar²,

Stein³

et al. 2020

Preprint

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Safe trajectory planning for high-performance automated vehicles in an environment with both static and moving obstacles is a challenging problem. Part of the challenge is developing a formulation that can be solved in real-time while including the following set of specifications: minimum time-togoal, a dynamic vehicle model, minimum control effort, both static and moving obstacle avoidance, simultaneous optimization of speed and steering, and a short execution horizon. This paper presents a nonlinear model predictive control-based trajectory planning formulation, tailored for a large, high-speed unmanned ground vehicle, that includes the above set of specifications. This paper also evaluates NLOptControl's ability to solve this formulation in real-time in conjunction with the KNITRO nonlinear programming problem solver; NLOptControl is our open-source, direct-collocation based, optimal control problem solver. This formulation is tested with various sets of the specifications. In particular, a parametric study relating execution horizon and obstacle speed, indicates that the moving obstacle avoidance specification is not needed for safety when the planner has a small execution horizon (≤ 0.375 s) and the obstacles are moving slowly (≤ 2.11 m s ). However, a moving obstacle avoidance specification is needed when the obstacles are moving faster, and this specification improves the overall safety by a factor of 6.73 (p = 2.2 × 10 −16 ) without, in most cases, increasing the solve-times. Overall, the results indicate that (1) safe trajectory planners for high-performance automated vehicles should include the entire set of specifications mentioned above, unless a static or low-speed environment permits a less comprehensive planner; and (2) NLOptControl can solve the formulation in real-time.

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