Optimization Design of Ship Collision Avoidance Strategy Based on Genetic Algorithm

COLREGs-abiding hybrid collision avoidance algorithm based on deep reinforcement learning for USVs

Improved random selection strategy, self-adaptive crossover and mutation operators, population co-evolution algorithm are adopted in this paper to set up a new genetic algorithm(GA), guaranteeing the universality of population, the thoroughness of solution and the execution efficiency of algorithm in solving multi-process, process crossover, work-piece flow and other condition based scheduling problem of flexible manufacturing system(FMS). The problem of GA in solving FMS scheduling is summarized in this paper, reasons and countermeasures for GA's pre-maturing are studied, longitudinal and transverse comprehensive state-based correlative self-adaptive crossover and mutation operators are raised according to the analysis on GA to avoid local optimization and pre-maturity, the co-evolutionary GA is designed to solve the scheduling model for FMS, and the algorithm is validated for the scheduling of box-type parts for precise machine tools.

There have been proposed various approaches for solving the collision avoidance problem of a robot manipulator. However, unfortunately, almost all of the research in this area has so far only dealt with the collision avoidance problem in which moving obstacles are not included in the workspace of a robot manipulator. In this paper, it is shown that path planning and the collision avoidance planning problem of a robot manipulator whose workspace includes moving obstacles can be solved successfully by using a genetic algorithm. Following a brief explanation of Simple GA, the new concept of pseudo-potential is introduced in order to detect the possibility of collision with moving obstacles in the future steps. This concept is utilized in order to evaluate a fitness function of a chromosome which represents a path connecting the starting point with the goal in the workspace of a robot manipulator and construct an appropriate collision avoidance planning. A computer simulation dealing with the collision avoidance problem of two robot manipulators confirms the effectiveness of our approach. >

This study aims to solve the problem of intelligent collision avoidance of unmanned ships at sea, and it proposes a novel driving strategy generating method of collision avoidance based on an extensive-form game mode with fuzzy credibility numbers. The innovation of this study is to propose an extensive-form game model of unmanned ships under the situation of two-sides clamping and verify the validity by fuzzy credibility. Firstly, this study divides the head-on situation of the ship at sea quantitatively to help the unmanned ship take targeted measures when making collision avoidance decisions. Secondly, this study adopts an extensive-form game model to model the problem of collision avoidance of an unmanned ship in the case of clamping on two sides. Thirdly, the extensive-form game model is organically combined with the fuzzy credibility degree to judge whether the collision avoidance game of unmanned ship achieves the optimal collision avoidance result. The effectiveness of the introduced game model is verified by case analysis and simulation. Finally, an illustrative example shows that the proposed mathematical model can better help unmanned ships make real-time game decisions at sea in the scenario of two-sides clamping effectively.

In the last twenty years genetic algorithms (GAs) were applied in a plethora of fields such as: control, system identification, robotics, planning and scheduling, image processing, and pattern and speech recognition (Back et al., 1997). In robotics the problems of trajectory planning, collision avoidance and manipulator structure design considering a single criteria has been solved using several techniques (Alander, 2003). Most engineering applications require the optimization of several criteria simultaneously. Often the problems are complex, include discrete and continuous variables and there is no prior knowledge about the search space. These kind of problems are very more complex, since they consider multiple design criteria simultaneously within the optimization procedure. This is known as a multi-criteria (or multiobjective) optimization, that has been addressed successfully through GAs (Deb, 2001). The overall aim of multi-criteria evolutionary algorithms is to achieve a set of non-dominated optimal solutions known as Pareto front. At the end of the optimization procedure, instead of a single optimal (or near optimal) solution, the decision maker can select a solution from the Pareto front. Some of the key issues in multi-criteria GAs are: i) the number of objectives, ii) to obtain a Pareto front as wide as possible and iii) to achieve a Pareto front uniformly spread. Indeed, multi-objective techniques using GAs have been increasing in relevance as a research area. In 1989, Goldberg suggested the use of a GA to solve multi-objective problems and since then other researchers have been developing new methods, such as the multi-objective genetic algorithm (MOGA) (Fonseca & Fleming, 1995), the non-dominated sorted genetic algorithm (NSGA) (Deb, 2001), and the niched Pareto genetic algorithm (NPGA) (Horn et al., 1994), among several other variants (Coello, 1998). In this work the trajectory planning problem considers: i) robots with 2 and 3 degrees of freedom (dof ), ii) the inclusion of obstacles in the workspace and iii) up to five criteria that are used to qualify the evolving trajectory, namely the: joint traveling distance, joint velocity, end effector / Cartesian distance, end effector / Cartesian velocity and energy involved. These criteria are used to minimize the joint 27

When sailing on cluttered water, effective collision avoidance is an essential safety requirement for unmanned surface vehicles (USVs). So that it can avoid multiple random ships effectively. This paper proposes a collision avoidance strategy for USVs based on the probabilistic game theory framework. First, a probability model is established combined with the USV’s kinematics for the collision avoidance problem. Then a greedy algorithm is designed under the framework of probability. It uses a cost function that searches possible movement paths to determine avoidance strategies for the USV. Compared with other collision avoidance methods, the method in this paper seeks to minimize the collision probability instead of guaranteeing no collision, because it is very difficult or impossible to completely avoid the collision in practice. Based on this, our method copes with kinematic constraints of the USV. The effectiveness of the algorithm is verified by simulation, and the simulation results show that the collision avoidance strategy proposed in this paper achieves a very good collision avoidance performance.

Unmanned Aerial Vehicle (UAV) has received wide interest in recent years because of their promising potentials for several applications that include border surveillance, intrusion, detection, emergent item delivery, wildlife monitoring, rescue operations etc. The growing number of entrances of drones into airspace has emerged a significant interest in the collision and obstacle avoidance in autonomous operation. One of major challenges for safer deployment of UAVs is collision avoidance in order to make UAVs autonomous and self-sustainable. In today’s technological era, it is possible for UAVs to avoid obstacles and prevent collisions while navigation by implementing cutting edge computer vision algorithms and visual tracking systems. But it is challenging due to the limited payload in UAV that only permits vision based sensors such as camera, inertial measurement unit such for control and obstacle detection. This paper aims to summarize existing solutions of collision avoidance strategies used in the navigation of UAV. Collision avoidance strategies based on geometry, optimization, sense and avoid, vision-based and force field based methods are investigated in this study.

Aims: To enhance the safety of intelligent vehicle drivers and help them achieve active collision avoidance. Background: Existing research on intelligent vehicles’ active collision avoidance focuses on reference path optimization and stability tracking control. However, there is relatively little research on collision avoidance in emergency accident scenarios. Longitudinal emergency braking collision avoidance technology has been relatively mature, while steering collision avoidance still needs a long time to develop. The research on joint steering and braking collision avoidance in emergency situations is still limited. Objective: Modeling the longitudinal and lateral safety distances based on the safety conditions of adjacent lanes, two active collision avoidance strategies are designed, namely the steering strategy and combined lateral and longitudinal collision avoidance strategy, with the aim of reducing the risk of collision. Methods: In the steering strategy, five control points are first determined sequentially according to steering collision constraints. Then, a collision avoidance path is established based on the quartic Bezier curve. To enhance the tracking of this path, an LQR path-tracking controller is employed to reduce errors via feed-forward and feedback control. In the case of a combined lateral and longitudinal collision avoidance strategy, the LQR and PID controllers are first adjusted and optimized, and then the collision avoidance path is replanned. Besides, the safety risks associated with braking and steering are taken into consideration, and constraints are set on the braking deceleration accordingly. Results: The effectiveness of the designed steering strategy and combined lateral and longitudinal collision avoidance strategy is verified through simulation results obtained by using CarSIM and Matlab/Simulink. They can effectively avoid collisions in emergency situations. Conclusion: The proposed collision avoidance algorithm can avoid collision in emergency scenarios and enhance the safety of autonomous driving.

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

BackgroundMining epistatic loci which affects specific phenotypic traits is an important research issue in the field of biology. Bayesian network (BN) is a graphical model which can express the relationship between genetic loci and phenotype. Until now, it has been widely used into epistasis mining in many research work. However, this method has two disadvantages: low learning efficiency and easy to fall into local optimum. Genetic algorithm has the excellence of rapid global search and avoiding falling into local optimum. It is scalable and easy to integrate with other algorithms. This work proposes an epistasis mining approach based on genetic tabu algorithm and Bayesian network (Epi-GTBN). It uses genetic algorithm into the heuristic search strategy of Bayesian network. The individual structure can be evolved through the genetic operations of selection, crossover and mutation. It can help to find the optimal network structure, and then further to mine the epistasis loci effectively. In order to enhance the diversity of the population and obtain a more effective global optimal solution, we use the tabu search strategy into the operations of crossover and mutation in genetic algorithm. It can help to accelerate the convergence of the algorithm.ResultsWe compared Epi-GTBN with other recent algorithms using both simulated and real datasets. The experimental results demonstrate that our method has much better epistasis detection accuracy in the case of not affecting the efficiency for different datasets.ConclusionsThe presented methodology (Epi-GTBN) is an effective method for epistasis detection, and it can be seen as an interesting addition to the arsenal used in complex traits analyses.

BackgroundCollision avoidance between two walkers involves a mutual adaptation to speed and orientation in order to successfully avoid a collision. Minimum Predicted Distance (MPD) is the distance at which two walkers would collide if their speed and path trajectory were maintained at first sight of one another. MPD has been used to describe the risk of collision and its evolution over time between two adult walkers when on a collision course. Middle-aged children have been shown to have poor perception-action coupling during static and dynamic collision avoidance tasks. Research has yet to examine whether perception-action coupling deficits persist in a dynamic collision avoidance task involving a child and another walker. Research questionCan the metric MPD(t) be used to examine collision avoidance strategies between children and adults? MethodsEighteen children (age: 10 ± 1.5 years) and eighteen adults (34 ± 9.6 years) walked along a 12.6 m pathway while avoiding another participant (child or adult). Groups of three children and three adults were recruited per session. Trials were randomized equally such that each adult interacted with another adult 20 times, each child interacted with another child 20 times, and each adult interacted with a child 21 times, for a total of 141 trials. 3D kinematic data of each participant’s head was recorded using the Vicon system. ResultsThe results demonstrated: (1) MPD(t) can be used to predict future collisions in children, (2) MPD(t) is an absolute measure that is consistently lower when a child is involved compared to two adult walkers, (3) the individual passing second, even when it is a child, contributes more to MPD(t) than the walker passing first. SignificanceIt appears children have developed adult-like strategies during a collision avoidance task involving two walkers. Body anthropometrics should be considered when determining collision avoidance strategies between children and adults.

A PD nonlinear dynamic inverse guidance law finite time convergence is proposed for the problem of UAV's collision avoidance in this paper. First, a proportional-derivative nonlinear dynamic inverse guidance law, which can be applied to multiple initial angles and speeds, is designed to ensure the security of UAV's collision avoidance. In addition, the stability of guidance law is proved by the circle criterion, and stability conditions are used to design coefficients of guidance law. In order to analysis the performance of this guidance law, a simulation system based on 6-DOF UAV is established. The results conclusively demonstrate that the maneuvering range of collision avoidance is smaller than the collision avoidance method based on traditional nonlinear dynamic inverse guidance.

Formation coordination can improve the overall performance of unmanned aerial vehicle (UAV) systems, so it is important to realize coordinated formation control. To solve the problems of ineffective internal collision avoidance and communication distance maintenance in existing formation control methods, a UAV formation control algorithm based on an improved artificial potential field and consensus is proposed. First, an improved artificial potential field obstacle avoidance method is used to solve the problems of collision avoidance and communication distance maintenance within the formation by setting a communication risk zone and a collision risk zone and by defining the dynamic adjustment parameters of the inter‐UAV virtual force to improve the traditional artificial potential field method. Second, a time integrating factor is introduced, and an improved consensus‐based formation control method is employed to overcome the problem of the artificial potential field method falling into the local optimum easily. Simulation experiments were designed to analyse the trends of the UAV formation motion trajectory, inter‐UAV relative distance, attitude, and formation time. The analysis results show that the method can make the UAV formation consistent with the flight trajectory of the intended formation, solving the problems of collision avoidance and communication distance maintenance within the UAV formation.

A formulation that makes possible the integration of collision prediction and avoidance stages for mobile robots moving in general terrains containing moving obstacles is presented. A dynamic model of the mobile robot and the dynamic constraints are derived. Collision avoidance is guaranteed if the distance between the robot and a moving obstacle is nonzero. A nominal trajectory is assumed to be known from off-line planning. The main idea is to change the velocity along the nominal trajectory so that collisions are avoided. A feedback control is developed and local asymptotic stability is proved if the velocity of the moving obstacle is bounded. Simulation results verify the value of the proposed strategy. >

An integrated collision prediction and avoidance scheme for mobile robots in non-stationary environments