Convergence Speed Of Algorithm Research Articles

Separation and Dynamic Binding Mechanism of 3D Model Animation by Integrating Improved Genetic Algorithm and WebAR

With the development of the economy, the demand for 3D model animation in fields such as games, film and television, and education is constantly increasing. At present, many 3D model animations are built based on genetic algorithm. However, due to the slow convergence speed and poor local search ability of genetic algorithm, this model still has many shortcomings. Therefore, a cellular automaton is used to optimize the genetic algorithm, and a cellular genetic algorithm is proposed. It is integrated with WebAR technology, which has the characteristics of lightweight and low cost, aiming to improve the separation and dynamic binding mechanism of 3D model animation. Experimental analysis showed that when the number of chromosome genes was 90, the vertex gene value of the research algorithm was the highest, ranging from 281 to 501, and the convergence was the highest. The file size of the model dataset based on the research method was reduced by 50% compared with before. The FPDT based on the animation separation and dynamic binding mechanism was decreased by 15.2 s on average. To sum up, it can be seen that the designed 3D model animation is more detailed and the animation dynamic is also the best. This research method can significantly improve the separation and dynamic binding mechanism of 3D model animation. This research will improve the integration of genetic algorithm and WebAR technology, which can not only effectively deal with complex 3D models and animations, but also has broad application prospects.

Tactical intent-driven autonomous air combat behavior generation method

With the rapid development and deep application of artificial intelligence, modern air combat is incrementally evolving towards intelligent combat. Although deep reinforcement learning algorithms have contributed to dramatic advances in in air combat, they still face challenges such as poor interpretability and weak transferability of adversarial strategies. In this regard, this paper proposes a tactical intent-driven method for autonomous air combat behaviour generation. Firstly, this paper explores the mapping relationship between optimal strategies and rewards, demonstrating the detrimental effects of the combination of sparse rewards and dense rewards on policy. Built around this, the decision-making process of pilot behavior is analyzed, and a reward mapping model from intent to behavior is established. Finally, to address the problems of poor stability and slow convergence speed of deep reinforcement learning algorithms in large-scale state-action spaces, the dueling-noisy-multi-step DQN algorithm is devised, which not only improves the accuracy of value function approximation but also enhances the efficiency of space exploration and network generalization. Through experiments, the conflicts between sparse rewards and dense rewards are demonstrated. The superior performance and stability of the proposed algorithm compared to other algorithms are captured by our empirical results. More intuitively, the strategies under different intents exhibit strong interpretability and flexibility, which can provide tactical support for intelligent decision-making in air combat.

Quantitative analysis of performance-oriented design efficiency in early divergent parametric design of office building façade

Optimizing early-stage building design significantly impacts energy efficiency, carbon reduction, and indoor air quality. While parametric design coupled with Multi-Objective Optimization is a prevalent approach in energy-focused preliminary design, it's important to note that the emphasis has been on parameter adjustments rather than algorithm modifications. However, altering the design algorithm can have a more fundamental influence on early-stage design, yet this area remains relatively under-researched. To aid designers in producing better performance-oriented designs at an early stage, this study proposes a set of quantitative index and methods to swiftly evaluate and compare the performance potential of design algorithms. Office passive design case studies are utilized to assess the performance of two common divergent design algorithms. Through solution space sampling, we analyze their respective performance boundaries and distributions by conducting sampling analysis and performance optimization for each algorithm. Notably, energy consumption differences can reach up to 5.07 kWh/ (m2·a), while sUDI variations may amount to 25.2%, and these variances are contingent upon climate zones. Furthermore, the efficiency and convergence speed of these design algorithms during optimization processes were quantitatively compared. Results indicate that optimal energy consumption can vary by up to 2.13 kWh/ (m2·a), with a time cost ranging from three to four times for approximating an optimal solution. The key contribution lies in providing designers with a rapid evaluation methods for performance-oriented comparison for different early design algorithms.

Fault diagnosis in hydropower units based on chaotic Kepler optimization algorithm-enhanced BiLSTM model

Safe and stable operation of hydropower units is the cornerstone of the whole hydroelectric power generation system. This paper proposes a deep learning model based on the chaotic Kepler optimization algorithm (CKOA) for fault diagnosis of hydropower units. The Tent chaotic function is introduced to initialize the initial population of KOA, which accelerates the convergence speed of the KOA algorithm and improves the global search capability by improving the uniformity and uncertainty of the population. CKOA is used to optimize the hyperparameters of Bidirectional Long Short-term Memory (BiLSTM) to improve the robustness and generalization ability of the model, making it suitable for dealing with complex nonlinear fault signals of hydropower units. The experimental results show that the training accuracy and testing accuracy of the CKOA-BiLSTM model are 97.6% and 98.3%, respectively, which are better than those of the LSTM, BiLSTM, and KOA-BiLSTM models. Meanwhile, diagnosing faults from the acoustic point of view, the accuracy of impact faults in hydropower units is much higher than that of wear and tear faults. This study can serve as a valuable supplement to the existing hydropower units condition monitoring and fault diagnosis system.

Rapid digital background calibration of bit weights in pipelined SAR ADC based on multi-PN injection

This brief presents a digital background calibration technique based on a dual-comparator architecture, aimed at addressing capacitor mismatch, residual gain error, and residue amplifier (RA) nonlinearity in pipelined successive-approximation-register (pipelined SAR) analog-to-digital converters (ADCs). Although numerous reports have documented bit weight calibration in pipelined SAR ADCs, none have been comprehensive, with op-amp nonlinearity never previously addressed. For the first time, we also consider and resolve the randomness issue of PN sequences following window sampling. Behavioral simulation results demonstrate the effectiveness of this technique, with the SNDR and SFDR performance of a 12-bit two-stage pipelined SAR ADC improving from 52.2 dB and 65.5 dB to an average of 68.8 dB and 91.0 dB, respectively. The convergence speed of this calibration algorithm is 0.6M samples, outpacing all previously published bit weight calibration works.

Application of Hybrid Algorithm Based on Ant Colony Optimization and Sparrow Search in UAV Path Planning

The Traveling Salesman Problem (TSP) is a classic problem in combinatorial optimization, aiming to find the shortest path that traverses all cities and eventually returns to the starting point. The ant colony optimization algorithm has achieved significant results, but when the number of cities increases, the ant colony algorithm is prone to fall into local optimal solutions, making it difficult to obtain the global optimal path. To overcome this limitation, this paper proposes an innovative hybrid ant colony algorithm. Our main motivation is to introduce other optimization strategies to improve the global search ability and convergence speed of the ant colony algorithm in solving TSP problems. We first incorporate the iterative solution of the sparrow search algorithm (SSA) into the ant colony algorithm to provide a better initial pheromone distribution. Second, we improve the pheromone update method to enhance the algorithm’s diversity during the search process and reduce the risk of falling into local optima. Finally, we define a dynamic pheromone evaporation factor to adjust the pheromone evaporation rate according to real-time changes in the search process. Through simulation tests on large-scale TSP problems and practical applications, we find that the hybrid ant colony algorithm outperforms the ant colony algorithm in both accuracy and running time. In Eg.2, the average accuracy of ISSA-ACO is improved by 12%, and the average running time is reduced by 45.6%. This study not only provides a new and effective method for solving large-scale TSP problems but also provides valuable references and insights for the application of ant colony algorithms in solving other complex optimization problems. At the same time, our research further verifies the effectiveness of improving heuristic algorithms by fusing different optimization strategies, providing new ideas and directions for future algorithm design and optimization.

Research on Complete Coverage Path Planning of Agricultural Robots Based on Markov Chain Improved Genetic Algorithm

Due to the limitations of low coverage, high repetition rate, and slow convergence speed of the basic genetic algorithm (GA) in robot complete coverage path planning, the state transition matrix of the Markov chain is introduced to guide individual mutation based on the genetic mutation path planning algorithm, which can improve the quality of population individuals, enhancing the search ability and convergence speed of the genetic algorithm. The proposed improved genetic algorithm is used for complete coverage path planning simulation analysis in different work areas. The analysis results show that compared to traditional genetic algorithms, the improved genetic algorithm proposed in this paper reduces the average path length by 21.8%, the average number of turns by 6 times, the repetition rate by 83.8%, and the coverage rate by 7.76% in 6 different work areas. The results prove that the proposed improved genetic algorithm is applicable in complete coverage path planning. To verify whether the Markov chain genetic algorithm (MCGA) proposed is suitable for agricultural robot path tracking and operation, it was used to plan the path of an actual land parcel. An automatic navigation robot can track the planned path, which can verify the feasibility of the MCGA proposed.

Research on Obstacle-Avoidance Trajectory Planning for Drill and Anchor Materials Handling by a Mechanical Arm on a Coal Mine Drilling and Anchoring Robot.

At present, China's coal mine permanent tunneling support commonly uses mechanized drilling and anchoring equipment; there are low support efficiency, labor intensity, and other issues. In order to further improve the support efficiency and liberate productivity, this paper further researches the trajectory planning of the drilling and anchoring materials of the robotic arm for the drilling machine "grasping-carrying-loading-unloading" on the basis of the drilling and anchoring robotic system designed by the team in the previous stage. Firstly, the kinematic model of the robotic arm with material was established by improving the D-H parameter method. Then, the working space of the robotic arm with the material was analyzed using the Monte Carlo method. The singular bit-shaped region of the robotic arm was restricted, and obstacles were removed from the working space. The inverse kinematics was utilized to solve the feasible domain of the robotic arm with material. Secondly, in order to avoid blind searching, the guidance of the Bi-RRT algorithm was improved by adding the target guidance factor, and the two-way tree connection strategy for determining the feasible domain was combined with the Bi-RRT algorithm's feasible domain judgment bi-directional tree connection strategy to improve the convergence speed of the Bi-RRT algorithm. Then, in order to adapt to the dynamic environment and avoid the global planning algorithm from falling into the local minima, on the basis of the above planning methods, an improved Bi-RRT trajectory planning algorithm incorporating the artificial potential field was proposed, which takes the planned paths as the guiding potential field of the artificial potential field to make full use of the global information and avoid falling into the local minimization. Finally, a simulation environment was built in a ROS environment to compare and analyze the planning effect of different algorithms. The simulation results showed that the improved Bi-RRT trajectory planning algorithm incorporating the artificial potential field improved the optimization speed by 69.8% and shortened the trajectory length by 46.6% compared with the traditional RRT algorithm.

Multi-Objective Optimization Strategy for Commercial Vehicle Permanent Magnet Water Pump Motor Based on Improved Sparrow Algorithm

In order to achieve multi-objective optimization for a permanent magnet water pump motor in heavy commercial vehicles, we propose a strategy based on response-surface methodology and the improved sparrow algorithm (CGE-SSA). Firstly, the output capacity of the pump during actual operation was tested with an experimental bench to determine the design parameters of the motor, and then its modeling was completed using Ansys Maxwell 2022r2 software. Secondly, the response-surface model was established by taking the parameters of permanent magnet width, rib width, and slot width as optimization parameters and the output torque (Ta), torque ripple (Tr), and back electromotive force (EMF) amplitude as optimization objectives. Meanwhile, three methods—namely, circular sinusoidal chaotic mapping, improved golden sinusoidal strategy, and adaptive weight coefficients—were used to improve the convergence speed and accuracy of the sparrow search algorithm (SSA). Finally, the multi-objective optimization of the permanent magnet synchronous motor was completed using the improved sparrow algorithm. A comparative analysis of the motor’s output before and after optimization showed that the torque pulsation and reverse electromotive force of the motor were significantly improved after optimization.

Path Planning of an Unmanned Aerial Vehicle Based on a Multi-Strategy Improved Pelican Optimization Algorithm.

The UAV path planning algorithm has many applications in urban environments, where an effective algorithm can enhance the efficiency of UAV tasks. The main concept of UAV path planning is to find the optimal flight path while avoiding collisions. This paper transforms the path planning problem into a multi-constraint optimization problem by considering three costs: path length, turning angle, and collision avoidance. A multi-strategy improved POA algorithm (IPOA) is proposed to address this. Specifically, by incorporating the iterative chaotic mapping method with refracted reverse learning strategy, nonlinear inertia weight factors, the Levy flight mechanism, and adaptive t-distribution variation, the convergence accuracy and speed of the POA algorithm are enhanced. In the CEC2022 test functions, IPOA outperformed other algorithms in 69.4% of cases. In the real map simulation experiment, compared to POA, the path length, turning angle, distance to obstacles, and flight time improved by 8.44%, 5.82%, 4.07%, and 9.36%, respectively. Similarly, compared to MPOA, the improvements were 4.09%, 0.76%, 1.85%, and 4.21%, respectively.

Traffic signal phase control at urban isolated intersections: an adaptive strategy utilizing the improved D3QN algorithm

Abstract The intelligent control of traffic signals at urban single intersections has emerged as an effective approach to mitigating urban traffic congestion. However, the existing fixed phase control strategy of traffic signal lights lacks capability to dynamically adjust signal phase switching based on real-time traffic conditions leading to traffic congestion. In this paper, an adaptive real-time control method employed by the traffic signal phase at a single intersection is considered based on the improved Double Dueling Deep Q Network (D3QN) algorithm. Firstly, the traffic signal phase control problem is modeled as a Markov decision process, with its state, action, and reward defined. Subsequently, to enhance the convergence speed and learning performance of the D3QN algorithm, attenuation action selection strategy and priority experience playback technology based on tree summation structure are introduced. Then, traffic flow data from various traffic scenarios are utilized to train the traffic signal control model based on the improved D3QN to obtain the optimal signal phase switch strategy. Finally, the effectiveness and optimal performance of the improved D3QN-based traffic signal control strategy are validated across diverse traffic scenarios. The simulation results show that, compared with the control strategy based on AC, DQN, DDQN, D3QN, and C-D3QN algorithms, the cumulative reward of the proposed I-D3QN strategy is increased by at least 6.57%, and the average queue length and average waiting time are reduced by at least 9.64% and 7.61%, which can effectively reduce the congestion at isolated intersections and significantly improve traffic efficiency.

Design of Intelligent Firefighting and Smart Escape Route Planning System Based on Improved Ant Colony Algorithm.

Due to the lack of real-time planning for fire escape routes in large buildings, the current route planning methods fail to adequately consider factors related to the fire situation. This study introduces a real-time fire monitoring and dynamic path planning system based on an improved ant colony algorithm, comprising a hierarchical arrangement of upper and lower computing units. The lower unit employs an array of sensors to collect environmental data in real time, which is subsequently transmitted to an upper-level computer equipped with LabVIEW. Following a comprehensive data analysis, pertinent visualizations are presented. Capitalizing on the acquired fire situational awareness, a propagation model for fire spreading is developed. An enhanced ant colony algorithm is then deployed to calculate and plan escape routes by introducing a fire spread model to enhance the accuracy of escape route planning and incorporating the A* algorithm to improve the convergence speed of the ant colony algorithm. In response to potential anomalies in sensor data under elevated temperature conditions, a correction model for data integrity is proposed. The real-time depiction of escape routes is facilitated through the integration of LabVIEW2018 and MATLAB2023a, ensuring the dependability and safety of the path planning process. Empirical results demonstrate the system's capability to perform real-time fire surveillance coupled with efficient escape route planning. When benchmarked against the traditional ant colony algorithm, the refined version exhibits expedited convergence, augmented real-time performance, and effectuates an average reduction of 17.1% in the length of the escape trajectory. Such advancements contribute significantly to enhancing evacuation efficiency and minimizing potential casualties.

Novel Multi-Classification Dynamic Detection Model for Android Malware Based on Improved Zebra Optimization Algorithm and LightGBM.

With the increasing popularity of Android smartphones, malware targeting the Android platform is showing explosive growth. Currently, mainstream detection methods use static analysis methods to extract features of the software and apply machine learning algorithms for detection. However, static analysis methods can be less effective when faced with Android malware that employs sophisticated obfuscation techniques such as altering code structure. In order to effectively detect Android malware and improve the detection accuracy, this paper proposes a dynamic detection model for Android malware based on the combination of an Improved Zebra Optimization Algorithm (IZOA) and Light Gradient Boosting Machine (LightGBM) model, called IZOA-LightGBM. By introducing elite opposition-based learning and firefly perturbation strategies, IZOA enhances the convergence speed and search capability of the traditional zebra optimization algorithm. Then, the IZOA is employed to optimize the LightGBM model hyperparameters for the dynamic detection of Android malware multi-classification. The results from experiments indicate that the overall accuracy of the proposed IZOA-LightGBM model on the CICMalDroid-2020, CCCS-CIC-AndMal-2020, and CIC-AAGM-2017 datasets is 99.75%, 98.86%, and 97.95%, respectively, which are higher than the other comparative models.

Intelligent decision-making algorithm for airborne phased array radar search tasks based on a hierarchical strategy framework

To address the guided search task of airborne phased array radar in the scenarios of large airspace with widespread distribution of cluster targets in Beyond Visual Range (BVR) air combat, a hierarchical strategy framework based on deep reinforcement learning is proposed to guide different stages of search tasks. Firstly, an airspace set-covering model and a radar parameter optimization model for the guided search task of cluster targets are established. Secondly, the hierarchical strategy framework including upper-level and lower-level strategies is constructed based on the above models. Finally, the happo-rgs algorithm is proposed for feature extraction from Markov continuous observation sequences, to enhance the training effectiveness and improve the algorithm convergence speed. Simulation results show that the trained agent can make precise autonomous decisions rapidly based on airspace-target covering situation and target guidance information which significantly improves the radar search performance in the forementioned scenarios compared to traditional algorithms.

An improved Coati Optimization Algorithm with multiple strategies for engineering design optimization problems

Aiming at the problems of insufficient ability of artificial COA in the late optimization search period, loss of population diversity, easy to fall into local extreme value, resulting in slow convergence and lack of exploration ability; In this paper, an improved COA algorithm based on chaotic sequence, nonlinear inertia weight, adaptive T-distribution variation strategy and alert updating strategy is proposed to enhance the performance of COA (shorted as TNTWCOA). The algorithm introduces chaotic sequence mechanism to initialize the position. The position distribution of the initial solution is more uniform, the high quality initial solution is generated, the population richness is increased, and the problem of poor quality and uneven initial solution of the Coati Optimization Algorithm is solved. In exploration phase, the nonlinear inertial weight factor is introduced to coordinate the local optimization ability and global search ability of the algorithm. In the exploitation phase, adaptive T-distribution variation is introduced to increase the diversity of individual population under low fitness value and improve the ability of the algorithm to jump out of the local optimal value. At the same time, the alert update mechanism is proposed to improve the alert ability of COA algorithm, so that it can search within the optional range. When Coati is aware of the danger, Coati on the edge of the population will quickly move to the safe area to obtain a better position, while Coati in the middle of the population will randomly move to get closer to other Coatis. IEEE CEC2017 with 29 classic test functions were used to evaluate the convergence speed, convergence accuracy and other indicators of TNTWCOA algorithm. Meanwhile, TNTWCOA was used to verify 4 engineering design optimization problems, such as pressure vessel optimization design and welding beam design. The results of IEEE CEC2017 and engineering design Optimization problems are compared with Improved Coati Optimization Algorithm (ICOA), Coati Optimization Algorithm (COA), Golden Jackal Optimization Algorithm (GJO), Osprey Optimization Algorithm (OOA), Sand Cat Swarm Optimization Algorithm (SCSO), Subtraction-Average-Based Optimizer (SABO). The experimental results show that the improved TNTWCOA algorithm significantly improves the convergence speed and optimization accuracy, and has good robustness. Three‑bar truss design problem, The Gear Train Design Problem, Speed reducer design problem shows a strong solution advantage. The superior optimization ability and engineering practicability of TNTWCOA algorithm are verified.

Reduction Accelerated Adaptive Step‐Size FISTA Based Smooth‐Lasso Regularization for Fluorescence Molecular Tomography Reconstruction

ABSTRACTIn this paper, a reduced accelerated adaptive fast iterative shrinkage threshold algorithm based on Smooth‐Lasso regularization (SL‐RAFISTA‐BB) is proposed for fluorescence molecular tomography (FMT) 3D reconstruction. This method uses the Smooth‐Lasso regularization to fuse the group sparse prior information which can balance the relationship between the sparsity and smoothness of the solution, simplifying the process of calculation. In particular, the convergence speed of the FISTA is improved by introducing a reduction strategy and Barzilai‐Borwein variable step size factor, and constructing a continuation strategy to reduce computing costs and the number of iterations. The experimental results show that the proposed algorithm not only accelerates the convergence speed of the iterative algorithm, but also improves the positioning accuracy of the tumor target, alleviates the over‐sparse or over‐smooth phenomenon of the reconstructed target, and clearly outlines the boundary information of the tumor target. We hope that this method can promote the development of optical molecular tomography.

An improved whale optimization algorithm for multi-robot path planning

Multi-robot path planning is challenging and has increasingly attracted attention with its widespread applications. This article proposes an improved Whale Optimization Algorithm (WOA) with Refracted opposition-based learning and Quantum behaviour (RQWOA). The algorithm is able to plan smooth and collisionless paths for robots combining cubic spline interpolation and multi-robot coordination. A quantum behavioural mechanism is used to coordinate the evolution of the whale population during the variable phase to increase the population quality and balance the exploration and exploitation capabilities of the WOA. Simultaneously, refracted opposition-based learning is introduced to improve the algorithm's optimization accuracy and convergence speed. The RQWOA was compared with seven efficient algorithms in experiments on classical test functions and multi-robot path planning cases. The results of these methods were tested statistically. The experimental results indicate that the RQWOA has superior solution accuracy. The RQWOA is highly competitive in terms of pathlength and stability in solving multi-robot path planning problems.

FATA: An efficient optimization method based on geophysics

An efficient swarm intelligence algorithm is proposed to solve continuous multi-type optimization problems, named the fata morgana algorithm (FATA). By mimicking the process of mirage formation, FATA designs the mirage light filtering principle (MLF) and the light propagation strategy (LPS), respectively. The MLF strategy, combined with the definite integration principle, drives the algorithmic population to enhance FATA’s exploration capability. The LPS strategy, combined with the trigonometric principle, drives the algorithmic individual to improve the algorithm's convergence speed and exploitation capability. These two search strategies can better use FATA’s population and individual search capabilities. The FATA is compared with a broad array of competitive optimizers on 23 benchmark functions and IEEE CEC 2014 to verify the optimization capability. This work is designed separately for qualitative analysis, exploration and exploitation competence analysis, the analysis of avoiding locally optimal solutions, and comprehensive comparison experiments. The experimental results demonstrate the comprehensiveness and competitiveness of FATA for solving multi-type functions. Meanwhile, FATA is applied to three practical engineering optimization problems to evaluate its performance. Then, the algorithm obtains better results than its counterparts in engineering problems. According to the results, FATA has excellent potential to be used as an efficient computer-aided tool for dealing with practical optimization tasks. Source codes and related files are available at https://aliasgharheidari.com/FATA.html and other websites.

An online decoupling-whitening frequency domain filtered-error least mean square algorithm for active road noise control.

Active road noise control (ARNC) systems have been widely investigated for low-frequency road noise attenuation in vehicle cabins. Multiple reference and error sensors are usually required to ensure noticeable noise reduction. However, this tends to slow down the convergence speed of adaptive algorithms due to the coupling of secondary paths and the cross correlation of reference signals. Furthermore, the high computational burden of normally utilized multichannel control algorithms exacerbates the difficulty of practical implementations. In this paper, an online decoupling-whitening frequency domain filtered-error least mean square (ODW-FDFeLMS) algorithm is proposed to address the aforementioned problems. Secondary path decoupling through inner-outer product decomposition and online reference whitening through spectral factorization effectively accelerate the convergence rate. Additionally, the utilization of the filtered-error algorithm based on frequency domain processing mitigates the computational complexity. Simulations with measured road noise data confirm the superiority of the ODW-FDFeLMS algorithm over existing algorithms in terms of convergence speed and computational complexity. Real-time experiments in a vehicle cabin further confirm the effectiveness of the proposed algorithm.

Hybrid Path Planning Strategy Based on Improved Particle Swarm Optimisation Algorithm Combined with DWA for Unmanned Surface Vehicles

Path planning is one of the core issues in the autonomous navigation of an Unmanned Surface Vehicle (USV), as the accuracy of the results directly affects the safety of the USV. Hence, this paper proposes a USV path planning algorithm that integrates an improved Particle Swarm Optimisation (PSO) algorithm with a Dynamic Window Approach (DWA). Firstly, in order to advance the solution accuracy and convergence speed of the PSO algorithm, a nonlinear decreasing inertia weight and adaptive learning factors are introduced. Secondly, in order to solve the problem of long path and path non-smoothness, the fitness function of PSO is modified to consider both path length and path smoothness. Finally, the International Regulations for Preventing Collisions at Sea (COLREGS) are utilised to achieve dynamic obstacle avoidance while complying with maritime practices. Numerical cases verify that the path planned via the proposed algorithm is shorter and smoother, guaranteeing the safety of USV navigation while complying with the COLREGS.