Underwater collision avoidance using undulating elongated fin propulsion

Study on thrust increase characteristics of separated undulating fins

Considering the advantages of biomimetic undulating fin propulsion, a modular underwater robot was developed, and the linear underwater propulsion performance of its individual module was studied. The CFD method was used to analyze the hydrodynamic characteristics of the robot's underwater self-propulsion, verifying the feasibility of the modular robot's self-propulsion and the overall design's rationality. The variation in speed and thrust of the robot over time during its underwater self-propulsion process was clarified. The transient evolution of the undulating fin, robot and surrounding flow field during the steady-state propulsion cycle was analyzed. A parametric study was conducted to examine the effect of different undulating parameters of the fin on the robot's underwater propulsion performance. The effect of frequency on propulsion performance under different amplitude conditions showed a certain similarity. The effect of amplitude on propulsion performance under different frequency conditions followed the same trend, and when the frequency exceeded 2 Hz, the curve of propulsion performance with respect to amplitude became more similar. The research results provide a theoretical fluid dynamics foundation for undulating fin robot underwater propulsion and also serve as the basis for future research on underwater propulsion in modular reconfigured forms.

An autonomous underwater vehicle with a biomechanical propulsion system is a possible answer to the demand for small, silent sensor platforms in many fields. The design of Galatea, a bio-mimetic AUV, involves four aspects: hydrodynamic shape, the propulsion, the motion control systems and payload. The shape of the hull is based on a modified Wortmann FX 71-L-150/20 airfoil. Wind tunnel tests have been conducted to determine the hydrodynamic force coefficients. The propulsion system is based on bio-mimetic undulating fin propulsion. A test set-up is build to get more insight in the fundamentals of this mechanism. The swimming behaviour is currently manually controlled and will be developed into an fully autonomous system. In the future, more research on the undulating fin propulsion system will be carried out and a second, modular prototype robot will be developed.

In an autonomous multi-mobile robot environment, path planning and collision avoidance are important functions used to perform a given task collaboratively and cooperatively. This study considers these important and challenging problems. The proposed approach is based on a potential field method and fuzzy logic system. First, a global path planner selects the paths of the robots that minimize the potential value from each robot to its own target using a potential field. Then, a local path planner modifies the path and orientation from the global planner to avoid collisions with static and dynamic obstacles using a fuzzy logic system. In this paper, each robot independently selects its destination and considers other robots as dynamic obstacles, and there is no need to predict the motion of obstacles. This process continues until the corresponding target of each robot is found. To test this method, an autonomous multi-mobile robot simulator (AMMRS) is developed, and both simulation-based and experimental results are given. The results show that the path planning and collision avoidance strategies are effective and useful for multi-mobile robot systems.

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

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.

Driven by the commercialization of intelligent ships, the increasingly complex mixed maritime traffic environment presents significant challenges for collision avoidance between multiple ships due to cognitive and behavioral differences between intelligent and traditional ships. Therefore, it is essential to develop a human-like collision avoidance strategy that incorporates traditional navigational experience and handling practices, enhancing explainability and autonomy. By addressing the actual decision-making needs for predicting other ships’ intentions and considering potential risk impacts, a hierarchical strategy is designed that first seeks course direction adjustment and then determines the magnitude of adjustment. A direction adjustment intention estimation model is proposed, accounting for risk membership and COLREGS, to predict other ships’ collision avoidance intentions. Additionally, an intention influence model and a state influence model are introduced to design decision-making objectives, forming an optimization function based on angle range and maneuvering time constraints to determine the appropriate adjustment magnitude. The results demonstrate the strategy’s effectiveness across various scenarios. Specifically, the distance between ships increased by nearly 25% during the process, significantly enhancing safety. It is worth mentioning that the model has the potential to enhance intelligent ships’ capabilities in complex situational handling and intention understanding.

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.

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.

Influence of hydrofoil motion patterns on the hydrodynamic performance of undulating fin for biomimetic underwater robots

This study proposes a novel model predictive control (MPC) based on receding horizon particle swarm optimisation (RHPSO) for formation control of non-holonomic mobile robots by incorporating collision avoidance and control input minimisation and guaranteeing asymptotic stability. In most conventional MPC approaches, the collision avoidance constraint is imposed by the 2-norm of a relative position vector at each discrete time step. Thus, multi-robot formation control problem can be formulated as a constrained non-linear optimisation problem. In general, traditional optimisation techniques suitable for addressing constrained non-linear optimisation problems take a longer computation time with an increase in the number of constraints. The traditional approaches therefore suffer from the computational complexity problem corresponding to an increase in the prediction horizon. To address this problem without a significant increase in computational complexity, a novel strategy for collision avoidance is proposed to incorporating a particle swarm optimisation. In addition, the stability conditions are derived in simplified forms that can be satisfied by selecting appropriate constant values for control gains and weight parameters. Numerical simulations verify the effectiveness of the proposed RHPSO-based formation control.

Aiming at the problem of ship collision avoidance, an assistant decision-making method based on genetic algorithm to give a better collision avoidance strategy is proposed. The algorithm adopts steering angle, steering time, navigation restoration angle, navigation restoration time as genetic factors. The collision avoidance scheme given by this gene coding method gives specific operation and is more consistent with the practice of navigation. At the same time, the global optimization ability of genetic algorithm is improved by multi group co evolution, adaptive selection operator and Metropolis criteria screening new individuals. According to the requirements in the rules, the initial population is generated randomly in the feasible region of the variable. At the same time, the new individuals generated by the crossover operator and the mutation operator pass the test of the rule, and the new individuals that do not conform to the rule are rejected. The simulation results show that the optimal individual obtained by the improved GA not only satisfies the security requirements, but also has low collision avoidance cost, which is in line with the economic requirements.

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.

Computer-controlled, human-like virtual agents (VAs), are often embedded into immersive virtual environments (IVEs) in order to enliven a scene or to assist users. Certain constraints need to be fulfilled, e.g., a collision avoidance strategy allowing users to maintain their personal space. Violating this flexible protective zone causes discomfort in real-world situations and in IVEs. However, no studies on collision avoidance for small-scale IVEs have been conducted yet. Our goal is to close this gap by presenting the results of a controlled user study in a CAVE. 27 participants were immersed in a small-scale office with the task of reaching the office door. Their way was blocked either by a male or female VA, representing their co-worker. The VA showed different behavioral patterns regarding gaze and locomotion. Our results indicate that participants preferred collaborative collision avoidance: they expect the VA to step aside in order to get more space to pass while being willing to adapt their own walking paths.