Robust obstacle avoidance for aerial platforms using adaptive model predictive control

Garimella, Gowtham; Sheckells, Matthew; Kobilarov, Marin

doi:10.1109/icra.2017.7989692

Cited by 32 publications

(19 citation statements)

References 22 publications

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“…This method utilizes the LOS guidance to parameterize different paths using only one parameter in 2D and two parameters in 3D. This method has a limited set of possible control actions making it less complete than optimal control methods with full control over the drone's behaviour, such as in [3]. The major advantage of the method is that the run-time is linear with respect to the number of possible combinations of control-actions, and is easily parallelizable which makes it much faster than full optimal control solutions on multi-core processors.…”

Section: Overviewmentioning

confidence: 99%

“…This is done for the linear case with linear constraints in [1] and for a nonlinear case with a predefined set of manoeuvres in [2]. Another example of bounding is in [3] where the positional variance at each time-step is calculated and a constraint is introduced that requires that obstacles are k standard deviations away from the drone. Bounding the accepted uncertainty or accepted positional offsets makes sure that the probability of the drone colliding is smaller than the probability that the bounds were wrong.…”

Section: Introduction a Background And Motivationmentioning

confidence: 99%

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Risk-Based Obstacle Avoidance in Unknown Environments using Scenario-Based Predictive Control for an Inspection Drone Equipped with Range Finding Sensors

Rothmund

Johansen

2019

2019 International Conference on Unmanned Aircraft Systems (ICUAS)

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This paper develops an obstacle avoidance strategy for inspection drones equipped with simple range finding sensors, such as radar or sonar. The obstacle avoidance strategy uses scenario-based model predictive control where the predicted outcomes of a set of possible control actions are evaluated. The action with the best predicted outcome amongst the safe options is chosen. The resulting behaviour is deemed safe if the probability of collision at each time-step in the prediction is lower than a given maximal accepted probability. The probability of collision is calculated by combining a probability density function of the position of the drone with an obstacle probability map generated by the range finding sensors. This constraint is checked at each step over the prediction horizon thus ensuring that the control action will give rise to safe behaviour. The algorithm is implemented in a 2D case and tested with a simple model for the range finding sensors. Simulations show that the drone is able to avoid obstacles and that the drone will change speed or take detours to avoid flying in potentially dangerous areas to mitigate risk. The algorithm is designed for avoiding obstacles along a pre-planned path. The pre-planned path is assumed to be generally good, but might be unsafe or not take some unknown obstacles into account. If the pre-planned path goes through a larger convex area or does not take a large obstacle into account, then this algorithm might not find a way around the obstacle and the drone will stop at a safe distance. The path of the drone must then be re-planned taking these obstacles into account. The resulting obstacle avoidance strategy guarantees safe behaviour which enables higher level controllers or human planners to plan a path based on lacking obstacle information without taking the safety of the drone into consideration.

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Section: Overviewmentioning

confidence: 99%

Section: Introduction a Background And Motivationmentioning

confidence: 99%

Risk-Based Obstacle Avoidance in Unknown Environments using Scenario-Based Predictive Control for an Inspection Drone Equipped with Range Finding Sensors

Rothmund

Johansen

2019

2019 International Conference on Unmanned Aircraft Systems (ICUAS)

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“…Currently, this framework is well-established in the field and has demonstrated success in diverse range of problems including manipulation [8], visual servoing [8], and motion planning. In robotic motion planning, MPC is widely in use for motion planning of mobile robots, manipulators, humanoids and aerial robots such as quadrotors [9]. Despite its merits, it can be computationally very expensive, especially in context of robot planning and control, since (a) unlike in process industries, typical robotic systems demand re-planning online at high frequency, (b) most systems have a non-linear dynamical model and (c) constraints apply both on state and controls.…”

Section: Related Workmentioning

confidence: 99%

T-PFC: A Trajectory-Optimized Perturbation Feedback Control Approach

Parunandi

Chakravorty

2019

IEEE Robot. Autom. Lett.

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Traditional stochastic optimal control methods that attempt to obtain an optimal feedback policy for nonlinear systems are computationally intractable. In this paper, we derive a decoupling principle between the open loop plan, and the closed loop feedback gains, that leads to a deterministic perturbation feedback control based solution (T-PFC) to fully observable stochastic optimal control problems, that is nearoptimal to the third order. Extensive numerical simulations validate the theory, revealing a wide range of applicability, coping with medium levels of noise. The performance is compared with Nonlinear Model Predictive Control in several difficult robotic planning and control examples that show near identical performance to NMPC while requiring much lesser computational effort.

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“…Boundary obstacles, such as walls or the floor, are also considered analogously as soft planar constraints based on its position r o i and normal vector n i as shown in (11) and (12).…”

Section: A Trajectory Trackingmentioning

confidence: 99%

“…Although great effort has been done in this area, dealing with multiple three-dimensional obstacles remains difficult. Recent work using NMPC often simplifies this problem by considering only the closest obstacle [11] or reducing the obstacles as bi-dimensional static [12], [13] or dynamic [14] constraints.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control for Aerial Collision Avoidance in Dynamic Environments

Castillo-Lopez

Sajadi-Alamdari

Sánchez-López

et al. 2018

2018 26th Mediterranean Conference on Control and Automation (MED)

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Autonomous navigation in unknown environments populated by humans and other robots is one of the main challenges when working with mobile robots. In this paper, we present a new approach to dynamic collision avoidance for multi-rotor unmanned aerial vehicles (UAVs). A new nonlinear model predictive control (NMPC) approach is proposed to safely navigate in a workspace populated by static and/or moving obstacles. The uniqueness of our approach lies in its ability to anticipate the dynamics of multiple obstacles, avoiding them in real-time. Exploiting active set algorithms, only the obstacles that affect to the UAV during the prediction horizon are considered at each sample time. We also improve the fluency of avoidance maneuvers by reformulating the obstacles as orientable ellipsoids, being less prone to local minima and allowing the definition of a preferred avoidance direction. Finally, we present two real-time implementations based on simulation. The former demonstrates that our approach outperforms its analog static formulation in terms of safety and efficiency. The latter shows its capability to avoid multiple dynamic obstacles.

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Robust obstacle avoidance for aerial platforms using adaptive model predictive control

Cited by 32 publications

References 22 publications

Risk-Based Obstacle Avoidance in Unknown Environments using Scenario-Based Predictive Control for an Inspection Drone Equipped with Range Finding Sensors

Risk-Based Obstacle Avoidance in Unknown Environments using Scenario-Based Predictive Control for an Inspection Drone Equipped with Range Finding Sensors

T-PFC: A Trajectory-Optimized Perturbation Feedback Control Approach

Model Predictive Control for Aerial Collision Avoidance in Dynamic Environments

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