Dynamic Obstacle Avoidance of Multi-Rotor UAV using Chance Constrained MPC

Wakabayashi, Takumi; Nunoya, Yuma; Suzuki, Satoshi

doi:10.23919/iccas52745.2021.9649942

Cited by 3 publications

(4 citation statements)

References 11 publications

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“…The approach to the Kalman filter is inspired by solving the Riccati equation for calculating optimal Kalman gain K, for each discrete timestep. The following equation (10) represents the mathematical architecture of Kalman filtering for State Space models assuming x ̂k ∈ ℝ n vector & yk ∈ ℝ m vector and correspondingly covariance matrix for process noise and measurement noise respectively as…”

Section: Kalman State Estimationmentioning

confidence: 99%

“…To calculate the optimal Kalman gain for each timestep, the Kalman filter model inputs the previous timestep's estimated state vector (𝑥 ̂𝑘−1 ) and error covariance peak (𝑃 𝑘−1 ). The prediction step in (10) calculates the prior estimates 𝑥 ̂𝑘 − & 𝑃 𝑘 − , which calculates the Kalman gain for that timestep followed by a posteriori estimate of the state vector for the current timestep. The filter also prepares the posteriori P matrix for the current timestep used as an input to the next time step.…”

Section: Prediction Stepmentioning

confidence: 99%

“…In replicating real-life scenarios, constraints are considered to be hard constraints, level of confidence (α) has been diligently taken to be 99.9%, where the standard format for a chance constraint problem is stated as ℙ(G∆u ⃗⃗⃗⃗ G ≤ h(ℰ)) ≥ α which duly solves as G∆u ⃗⃗⃗⃗ G ≤ μh + Kα*σh, where μh & σh are the mean and standard deviation for the h(ℰ) matrix. Kα is the parameter to be calculated for each row of the random variable in the h(ℰ) matrix [10]. Since all the motors are assumed to be identical and the performance of each is assumed as time-invariant, all the random variables along the elements of 𝑦 ̅ G min 𝐶 & 𝑦 ̅ G max 𝐶 have similar Kα values.…”

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confidence: 99%

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Reference tracking of nonlinear airborne systems using stochastic MPC with disturbance observers and actuator chance constraint optimization

Aryan

2023

Preprint

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With ever-growing advancements in the field of aerospace technology, there are much wider opportunities and capabilities emerging as a constant demand in terms of speed and accuracy. One of the major segments for any airborne mission is its Guidance-Navigation-Control technique which becomes very crucial for the aspects of speed and accuracy, especially for defense or highly complex missions like missile launches, space missions, airstrikes, and many more. A controller technique that is much more accurate than its traditional counterparts like Proportional-Integral-Derivative (PID) Control and Linear Quadratic Regulator (LQR), as well as constantly considering any instantaneous state measurement noises into account, is an irresistible demand and advantageous over other control techniques in use. A Model Predictive Controller (MPC) can smoothly handle actuator constraints, angle-of-attack constraints, and pitch-angle constraints which makes it more versatile and near perfect for practical nonlinear systems. A real-world nonlinear airborne system is mathematically modeled and represented in its nonlinear state space representation. To accurately measure the instantaneous states, the Kalman filter with distinct covariances for both state and control measurements is taken considering noises to be normally distributed. This is followed by the nonlinear model predictive controller which acts based on the system’s guidance requirements on the perturbated states at each time instance for optimal reference path tracking as well as dealing with stochastic actuator values optimized by chance-constraint programming. The performance of the proposed reference tracking technique is implemented in MATLAB/Simulink tool with numerical as well as graphical analysis results.

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Section: Kalman State Estimationmentioning

confidence: 99%

Section: Prediction Stepmentioning

confidence: 99%

mentioning

confidence: 99%

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Reference tracking of nonlinear airborne systems using stochastic MPC with disturbance observers and actuator chance constraint optimization

Aryan

2023

Preprint

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“…The model only predicted the next position of the obstacle trajectory by using the historical and current positions. The authors of References 30,31 studied probabilistic collision avoidance, which used the constant velocity hypothesis to predict the obstacle trajectories and controls errors by adjusting the number of prediction segments. The proposed trajectory prediction method can only predict a small segment of an obstacle's trajectory.…”

Section: Introductionmentioning

confidence: 99%

Dynamic smooth and stable obstacle avoidance for unmanned aerial vehicle based on collision prediction

Ma,

Cai,

et al. 2023

Optim Control Appl Methods

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Autonomous dynamic obstacle avoidance of unmanned aerial vehicles (UAVs) based on collision prediction is critical for stable flight missions. UAVs must deal with high‐speed and nonlinearly moving obstacles that obey nonlinear dynamics, which are one of the numerous objects affecting stable flight. To satisfy the requirements of smooth flight and attitude stability, a novel dynamic smooth obstacle avoidance (QVA) method based on obstacle trajectory prediction is proposed. To predict the dynamic obstacle trajectories, a trajectory prediction (QLTP) method using a quasi‐linear parameter varying representations is proposed. The proposed QVA integrates the QLTP approach, velocity obstacle (VO) approach, and artificial potential field (APF) methods. The QVA detects an imminent collision based on the QLTP method, and then replans the UAV path based on the VO method at the time of the predicted collision. The UAV tracks planned waypoints for collision avoidance. To ensure the flight safety of the UAV, a virtual APF is constructed with waypoints as local targets and obstacles. The simulation results show that the proposed method performs better than the improved APF and VO methods in terms of the smoothness of the obstacle avoidance path and the stability of the UAV attitude.

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Dynamic obstacle avoidance for Multi-rotor UAV using chance-constraints based on obstacle velocity

Wakabayashi

Suzuki

2023

Robotics and Autonomous Systems

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Dynamic Obstacle Avoidance of Multi-Rotor UAV using Chance Constrained MPC

Cited by 3 publications

References 11 publications

Reference tracking of nonlinear airborne systems using stochastic MPC with disturbance observers and actuator chance constraint optimization

Reference tracking of nonlinear airborne systems using stochastic MPC with disturbance observers and actuator chance constraint optimization

Dynamic smooth and stable obstacle avoidance for unmanned aerial vehicle based on collision prediction

Dynamic obstacle avoidance for Multi-rotor UAV using chance-constraints based on obstacle velocity

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