Infinity-Norm-Based Worst-Case Collision Avoidance Control for Quadrotors

Rocket flight tests invariably serve a purpose, one of which involves area monitoring or aerial photography. Consequently, the rocket necessitates the installation of a camera that remains consistently oriented toward the Earth's surface throughout its trajectory. Thus, ensuring the rocket's stability and preventing any rotation becomes imperative. To achieve this, the Onboard Attitude Determination Control System (OADCS) was researched and developed, fully controlled by NI myRIO with Labview as the programming language, ensures the rocket's attitude control and maintains a rolling angle of 0 degrees during flight. The MyRIO oversees the retrieval of attitude and position data from the X-Plane flight simulator, offering feedback through actuator control. The development of the OADCS proceeded incrementally through stages utilizing the Software in the Loop Simulation (SILS) and Hardware in the Loop Simulation (HILS) techniques, to ensure the verification of the system's functionality before its application to the rocket for real flight testing. In the OADCS control scheme, Model Predictive Control (MPC) is chosen, and it is compared with a PID controller to serve as a benchmark for processing speed. Because the rocket's flight time is short and its speeds of up to Mach 4. The simulation results indicate that MPC can halt the rocket's rotation 12 times more rapidly than PID control. Additionally, the MPC's ability to maintain a zero-degree rotation can persist throughout the rocket's flight time. Employing SILS and HILS enhances the OADCS rocket development process by incorporating MPC, which holds promise for application in real rockets.

This paper presents a viable approach for incorporating collision avoidance strategies into existing guidance and control systems on marine vessels. We propose a method that facilitates the use of simulation-based Model Predictive Control (MPC) for collision avoidance (COLAV) on marine vessels. Any COLAV strategy to be applied in real traffic must adhere to the international regulations for preventing collisions at sea (COLREGS). The proposed MPC COLAV method does not rely on an accurate model of the guidance system to achieve vessel behaviors that are compliant with the COLREGS. Rather, it depends on transitional costs in the MPC objective for collision avoidance maneuvers that are being executed by the marine vessel. Hence, it is straightforward to implement the MPC COLAV on different vessels without specific knowledge of the vessel's guidance strategy. Moreover, it offers the possibility to switch between different (possibly application specific) guidance strategies on the same vessel while running the same MPC COLAV algorithm. We present results from full scale experiments that show the viability of our method in different collision avoidance scenarios.

This paper presents a distributed solution for the problem of collaborative collision avoidance for autonomous inland waterway ships. A two-layer collision avoidance framework that considers inland waterway traffic regulations is proposed to increase navigational safety for autonomous ships. Our approach allows for modifying traffic rules without changing the collision avoidance algorithm, and is based on a novel formulation of model predictive control (MPC) for collision avoidance of ships. This MPC formulation is designed for inland waterway traffic and can handle complex scenarios. The alternating direction method of multipliers is used as a scheme for exchanging and negotiating intentions among ships. Simulation results show that the proposed algorithm can comply with traffic rules. Furthermore, the proposed algorithm can safely deviate from traffic rules when necessary to increase efficiency in complex scenarios.

This paper presents a new method based on the application of the Hough Transform for solving the robot motion problem and their intermediate steps: geometric modelling and collision detection. In terms of modelling, given a set of points, which is the representation of a given object, the maximum inner and the minimum outer spherically-extended polytopes (s-tope) are automatically generated. Collision detection is based on the computation of the translational penetration distance. Such a distance also allows to compute an intermediate point when collision avoidance is required. The set of intermediate points describes a solution to the path-planning problem. This new approach introduces an important decrease in the computational time required for robot motion planning.

Model predictive control (MPC) is a multi-objective control technique that can handle system constraints. However, the performance of an MPC controller highly relies on a proper prioritization weight for each objective, which highlights the need for a precise weight tuning technique. In this paper, we propose an analytical tuning technique by matching the MPC controller performance with the performance of a linear quadratic regulator (LQR) controller. The proposed methodology derives the transformation of a LQR weighting matrix with a fixed weighting factor using a discrete algebraic Riccati equation (DARE) and designs an MPC controller using the idea of a discrete time linear quadratic tracking problem (LQT) in the presence of constraints. The proposed methodology ensures optimal performance between unconstrained MPC and LQR controllers and provides a sub-optimal solution while the constraints are active during transient operations. The resulting MPC behaves as the discrete time LQR by selecting an appropriate weighting matrix in the MPC control problem and ensures the asymptotic stability of the system. In this paper, the effectiveness of the proposed technique is investigated in the application of a novel vehicle collision avoidance system that is designed in the form of linear inequality constraints within MPC. The simulation results confirm the potency of the proposed MPC control technique in performing a safe, feasible and collision-free path while respecting the inputs, states and collision avoidance constraints.

Uninhabited Aerial Vehicles will require autonomous collisio n detection and avoidance capabilities in order to gain wide acceptance and usage in the National Airspace System. The collision detection and avoidance problem is primarily a motion estimation problem, relying on the accuracy of the sensing, processing, actuation and control algorithms onboard the vehicle. Computer vision is a growing field in robotics and autonomous vehicles, and has made vision a feasible technology for object detection in different platforms. In this work, we investigate the feasibil ity of using optic flow techniques in order to design an altitude hold controller for a fixed wing aircraft flying a nap -of -the -earth trajectory in the presence of variable terrain. Simulated results yield an error of approximately 1.5% in altitude variat ion for steady level flight (approximately 15 cm) for terrain disturbances of maximum amplitude 4 m. Experimental results obtained from flight data using the UIUC UAV platform are used to validate the controller, and sources of error along with potential improvements in equipment and procedures are cited. I. Introduction N essential challenge inherent to the integration of uninhabited aerial vehicles (UAVs) into the National Airspace System (NAS) is the inability to provide adequate safety assurances for th ese vehicles in a verifiable manner. The FAA requires that UAVs be capable of see -and -avoid functionality, unfortunately most solutions to the problem of autonomous surveillance, guidance and navigation are insufficiently scalable, reliable, verifiable or cost -effective. Implementation of safe and reliable object avoidance is crucial for the survival of autonomous vehicles intended to navigate in densely populated environments such as the NAS. In order to meet these stringent requirements, UAVs must be eq uipped with reactive sensing technologies that enable real -time conflict detection and object avoidance. Numerous techniques have been developed in the past few years to optimize the decision making process and solve the path planning problem in real -time , while accounting for aircraft dynamics, obstacle avoidance constraints and safe flight considerations. Regrettably, many of these techniques underestimate the difficulty of accurately sensing a dynamic and imperfect environment in an accurate and practic al manner. Making decisions based on distorted estimations of the surrounding environs is a pitfall that can destabilize even the most robust of path planning algorithms. Presently, state -of -the -art conflict detection and resolution systems employ a combi nation of radar, sonar and laser systems: while reliable, these systems are often prohibitive due to weight, size and equipment cost for certain UAV applications. Video recording devices are another alternative for environmental sensing, and have become standard equipment on many UAVs due to their exploratory nature. Although video images from this source are often intended for secondary mission purposes, such as target tracking and surveillance, they have been shown capable of providing valuable informati on for more important tasks, like collision avoidance. However, the nature of a fixed wing UAV platform brings rigorous safety requirements and constraints to the field of autonomous conflict detection and resolution: limited minimum flying speeds and res tricted maneuverability, thereby making the need for real -time sensing and decision -making paramount for safety purposes. In this paper we investigate the applicability of c omputer vision techniques that have been developed in order to estimate motion from two dimensional images. In the next section we provide a brief discussion of optical flow theory, and section 3 provides a direct implementation of the optical flow theory to an aircraft altitude controlle r. Section 4 provides the setup and simulation of several scenarios used to evaluate the performance of the controller designed in the previous section. Experimental video data taken from actual flight tests is used in order to asses the 1

This paper presents a “low-cost, small, lightweight Guidance, Navigation and Control (GNC) Micro-System for Aircraft Collision Avoidance: Vision Based Miniaturized Aircraft Integrated Collision Avoidance System” which is referred to as “The Integrated System” in this paper. The Integrated System takes advantage of the American GNC Corporation’s (AGNC) coremicro AHRS/INS/GPS Integration Unit and object detection algorithms to achieve a vision based integrated GNC micro-system for aircraft and UAVs, which is capable of assisting aircraft pilots to avoid approach and collision with ground/water and other near objects in flight, thus, improving safety, reliability and cost effectiveness. The various data from the coremicro AHRS/INS/GPS Integration Unit, terrain data base, and object detection sensors are processed to produce collision warning audio/visual messages and collision detection and avoidance of terrain and obstacles through generation of guidance commands in a closed-loop system.

This article presents an optimal trajectory-planning method for robot manipulators with collision detection and avoidance. The obstacles and robot segments are represented by a set of convex polyhedra. The collision detection is performed at each discretized robot configuration by an efficient procedure devel oped with the computational geometry method, which computes a distance function of the robot segments and the obstacles. By introducing this function for specifying the collision-free con straint, the path-planning problem is formulated as an optimal control problem using the augmented Lagrangian, which may be considered as a combination of the duality, penalty and con straint relaxation methods. The problem is solved by a robust UZAWA-like algorithm, where a subgradient method is applied for the primal optimization, as the distance function is not ev erywhere differentiable. An example is given for the trajectory planning of a robot arm with three revolute joints.

This paper presents a dynamic collision avoidance controller of a connected vehicle group using Model Predictive Control (MPC). The vehicles should follow a predefined reference trajectory while simultaneously avoiding collisions on this trajectory. MPC pursues the tracking of the reference trajectory in the objective function of the optimization problem. In order to avoid collisions, MPC defines avoidance constraints between each pair of vehicles. Due to the collision avoidance constraints, the feasible set of the optimization problem is non-convex. Thus, the resulting optimization problem is non-convex. This paper presents an efficient method to solve this non-convex optimization problem based on sequential convex programming.

In active collision avoidance, the trajectory tracking controller determines the deviation from the reference path and the vehicle stability. The main objective of this study was to reduce the tracking error and improve the tracking performance in collision avoidance. Unlike the previously proposed model predictive control (MPC) strategies with constant sampling time, an improved MPC controller with varying sampling time based on the hierarchical control framework was proposed in this paper. Compared with the original MPC tracking controller, the improved MPC controller demonstrated better adaptive capability for the varying road adhesion coefficients and vehicle speed on a curved road. The simulation results revealed that the hierarchical control framework generated an optimal trajectory for collision avoidance in real-time by minimizing the potential field energy.

Tracking control is one of the important working conditions of unmanned driving and can help vehicles keep distances and run in an orderly manner to improve traffic utilization, ensure clear roads and avoid rear-end collisions. This paper constructs the motion trend of obstacles within the predictive step length of model predictive control (MPC), designs the danger level indicator between vehicles and obstacles, and formulates dynamic planning for the original reference path based on MPC. This paper proposes an integrated collision avoidance control strategy based on dynamic nonlinear MPC (DNMPC), and predicts the locations of moving obstacles within the predictive step length of DNMPC. The proposed method can perform initial planning for the collision-avoidance path according to the road environment information and based on the activation function. It builds the function for the tendency of obstacles within the predictive step length of MPC, introduces it into the objective function for optimization, designs dynamic path planning control based on the theories of MPC, and performs local optimization to the initial reference path under the obstacles in motion with a mass model. In addition, it defines varying discrete step lengths within the predictive step length and achieves the long-distance prediction and high-precision control of collision avoidance controllers. The experimental results show that the dynamic, nonlinear, and integrated collision avoidance control proposed in this paper can ensure excellent collision avoidance and steady vehicle driving and has very good practical value.

The paper presents proof of concept design and validation methodology of the flight recovery control system. The flight recovery control system algorithm is based on the model predictive control. Adaptive control is considered for situations where the airplane changes its parameters during the flight. The acceptability of maneuvers automatically conducted by flight recovery control system by pilots can be validated in the hardware in the loop simulation on the aircraft stand with the projection of the pilot view from the cabin. Matlab Simulink is used for models and the control law design. A special block set has been designed for interface Simulink models to the hardware in the loop simulator.

Ship predictive collision avoidance method based on an improved beetle antennae search algorithm

A real-time multi-ship collision avoidance decision-making system for autonomous ships considering ship motion uncertainty

In this study, a collision avoidance method with model predictive control is proposed for a nonlinear model of a four-wheeled vehicle. The C/GMRES algorithm is used for solving the nonlinear model predictive control (NMPC) problem within a short sampling period. A nonlinear tire model is employed to represent the realistic behavior of a vehicle. Here, to consider whether it is possible to avoid an obstacle physically, an unavoidable region (UR) is constructed as the region in which the vehicle cannot avoid the obstacle owing to physical limitations. Even if the distance between the vehicle and the obstacle is short, the vehicle is controlled by NMPC not only to avoid the UR but also to stay on the road. A performance index for satisfying the above constraints is proposed, and control responses are investigated through numerical simulations.