This paper presents the application of artificial intelligence, gaming, and optimization algorithms to safe route planning and traffic control for various types of autonomous vehicles. The study covers autonomous mobile robots and vehicles based on land, autonomous surface and underwater vehicles based in water, marine autonomous surface ships, and autonomous air vehicles. Biomimetic autonomous vehicle solutions are presented for each of these three areas of operation. A comparative analysis of the methods enables the mapping of real-world characteristics of autonomous processes, such as their multi-objective and game-like nature. To address the challenges of ensuring the safe operation of autonomous systems, this work presents a comparative assessment of individual route-planning methods regarding collision risk and optimality. In particular, route-planning methods are developed for various possible kinematic, dynamic, and game trajectories, both cooperative and non-cooperative. An experiment comparing individual path-planning methods was conducted using examples of recorded real-world navigation situations. Following the analysis, special attention was paid to the types and methods of cybersecurity used for individual types of autonomous vehicles.

To address the challenges of ensuring the safe operation of autonomous vehicles, this paper presents a comparative evaluation of the methods developed by the author for determining safe and optimal routes in multi-vehicle cooperative and non-cooperative navigation situations with varying traffic density, taking into account collision risk assessment. For each of the three types of autonomous vehicles—land, water, and air—we propose a kinematic trajectory (KT) method, a dynamic trajectory (DT) method, and a game trajectory (GT) method. Linear and dynamic programming, artificial neural networks, and matrix and positional multiplayer games were used for this purpose. Simulation studies of these three methods for determining safe trajectories in test navigation situations of varying complexity allowed for a comparison of their effectiveness in various environmental states. Additionally, biomimetic solutions for autonomous vehicles were described for each of their three operational areas. This analysis also highlighted the types and methods of cybersecurity used for individual types of autonomous vehicles.

This paper presents a comprehensive review of recent works (2020–2026) on machine learning (ML) algorithms applied to autonomous platforms such as unmanned underwater vehicles (UUVs), unmanned surface vehicles (USVs), unmanned aerial vehicles (UAVs), and ground-based mobile robots. The review focuses on the following functional areas: environment perception, simultaneous localization and mapping (SLAM), collision avoidance and path planning, and motion control. Different ML methods are covered, including supervised, semi-supervised, and unsupervised learning, as well as reinforcement learning and deep reinforcement learning. The reviewed methods are analyzed with respect to their performance, robustness, and suitability for different operational environments, including underwater, surface, air, and land domains. Finally, the authors identify key challenges and outline promising future directions aimed at improving the safety, autonomy, and reliability of autonomous vehicles.

The aim of this paper is to synthesize a method for controlling an autonomous maritime object in order to prevent collision situations caused by incorrect assessment of the proximity of other objects, the subjectivity of the decision-making method and the influence of the traffic conditions. For this purpose, a model of game control of the safe movement of an autonomous object in situations where it passes a large number of other objects was formulated. The game control method was used to synthesize an algorithm for determining the object safe trajectory. The analysis of the problem is complemented by computer simulations of the game control algorithm in a multi-object situation under various traffic conditions.

Machine learning algorithms are powerful tools for solving many real-life problems and are applied in many branches of the industry. In finances they are used for analyzing market trends. In health care they are applied to help in diagnosing diseases at an early stage. In marketing they are applied to detect customers preference. In transportation, they are applied for calculation of routes, considering current traffic conditions. This paper presents the results of a review of recent machine learning approaches for environment perception, localization, collision avoidance, path planning and motion control of unmanned underwater vehicles (UUVs).

The aim of this work, which is an extension of previous research, is a comparative analysis of the results of the dynamic optimization of safe multi-object control, with different representations of the constraints of process state variables. These constraints are generated with an artificial neural network and take movable shapes in the form of a parabola, ellipse, hexagon, and circle. The developed algorithm allows one to determine a safe and optimal trajectory of an object when passing other multi-objects. The obtained results of the simulation tests of the algorithm allow for the selection of the best representation of the motion of passing objects in the form of neural constraints. Moreover, the obtained characteristics of the sensitivity of the object’s trajectory to the inaccuracy of the input data make it possible to select the best representation of the motion of other objects in the form of an excessive approximation area as neural constraints of the control process.

The following article presents the task of optimizing the control of an autonomous object within a group of other passing objects using Pontryagin’s bounded maximum principle. The basis of this principle is a multidimensional nonlinear model of the control process, with state constraints reflecting the motion of passing objects. The analytical synthesis of optimal multi-object control became the basis for the algorithm for determining the optimal and safe object trajectory. Simulation tests of the algorithm on the example of real navigation situations with various numbers of objects illustrate their safe trajectories in changing environmental conditions. The optimal object trajectory obtained using Pontryagin’s maximum principle was compared with the trajectory calculated using the Bellman dynamic programming method. The analysis of the research allowed for the formulation of valuable conclusions and a plan for further research in the field of autonomous vehicle control optimization. The maximum principle algorithm allows one to take into account a larger number of objects whose data are derived from ARPA anti-collision radar systems.

This work analyzes the sensitivity functions and optimum control of a transport and logistics process model. It explains the fundamental model of controlling safe ship movement as a differential game, and optimizing control algorithms through multi-matrix game and multi-stage positioning game. The sensitivity features for controlling safe ship in actual collision scenario are described in relation to inaccurate information of process position and variations in its varables, based on the determination of computer simulation algorithms in Matlab/Simulink software.

The essence of this work, which is an extension of the author’s previous research, is an analysis of computational intelligence algorithms that the support safe control of an autonomous object moving in a large group of other autonomous objects. Linear and dynamic programming methods with neural constraints on the process state, as well as positional and matrix game methods, were used to synthesize computational algorithms for the safe trajectory of one’s own object. The aim of the comparative analysis of intelligent computational methods for the safe trajectory of an object was to show, through their use, the possibility of taking into account the risk of collision resulting from both the degree of cooperation of objects while observing traffic laws and the impact of the environment in the form of visibility and the complexity of the situation. Simulation tests of the algorithms were carried out on the example of a real navigation situation of several dozen objects passing each other at sea.

The analysis of the state of the literature in the field of methods of perception and control of the movement of autonomous vehicles shows the possibilities of improving them by using an artificial neural network to generate domains of prohibited maneuvers of passing objects, contributing to increasing the safety of autonomous driving in various real conditions of the surrounding environment. This article concerns radar perception, which involves receiving information about the movement of many autonomous objects, then identifying and assigning them a collision risk and preparing a maneuvering response. In the identification process, each object is assigned a domain generated by a previously trained neural network. The size of the domain is proportional to the risk of collisions and distance changes during autonomous driving. Then, an optimal trajectory is determined from among the possible safe paths, ensuring control in a minimum of time. The presented solution to the radar perception task was illustrated with a computer simulation of autonomous driving in a situation of passing many objects. The main achievements presented in this article are the synthesis of a radar perception algorithm mapping the neural domains of autonomous objects characterizing their collision risk and the assessment of the degree of radar perception on the example of multi-object autonomous driving simulation.

This paper presents a solution to the problem of providing an autonomous vehicle with a safe control task when moving around many other autonomous vehicles. This is achieved by developing an appropriate computer control algorithm that takes into account the possible risk of a collision resulting from both the impact of environmental disturbances and the imperfection of the rules of maneuvering in situations where many vehicles pass each other, giving the control process a decisive character. For this purpose, three types of algorithms were synthesized: kinematic and dynamic optimization with neural domains, as well as sequential game control of an autonomous vehicle. The control algorithms determine a safe trajectory, which is implemented by the actuators of the autonomous vehicle. Computer simulations of the control algorithms in the Matlab/Simulink software allow for their comparative analysis in terms of meeting the criteria for the optimality and safety of an autonomous vehicle when passing a larger number of other autonomous vehicles. For this purpose, scenarios of multidirectional and one-way traffic of autonomous vehicles were used.

This article presents a combination of remote sensing, an artificial neural network, and game theory to synthesize a system for safe ship traffic management at sea. Serial data transmission from the ARPA anti-collision radar system are used to enable computer support of the navigator’s maneuvering decisions in situations where a large number of ships must be passed. The following methods were used to determine the safe and optimal trajectory of one’s own ship: static optimization, dynamic programming with neural constraints on the state of the control process in the form of domains of encountered ships generated by a three-layer artificial neural network, and positional and matrix games. Then, computer calculations for the safe trajectory of one’s own ship were carried out using the presented algorithms. The calculations were carried out for an actual navigational situation recorded on a r/v HORYZONT II research/training vessel radar screen under a real navigational situation in the Skagerrak–Kattegat Straits.

This article presents the synthesis of a nonlinear multi-object differential game model in relation to the process of safe ship control in collision situations at sea. Nonlinear dynamic equations of a target ship and linear kinematic equations of passing ships were used to formulate the game state equations. The model of such a differential game was developed using LabVIEW 2022 version software. This was then subjected to simulation tests using the example of a navigational situation in which the target ship passed three encountered ships at a safe distance under the conditions of non-cooperation of ships, their cooperation, and optimal non-game control. The results of the computer simulation are presented in the form of ship trajectories and time courses of individual game control variables. The distinguishing feature of the model built in LabVIEW software is the ability to conduct research in online mode, where the user has the opportunity to track the impact of changes in the model parameters on the course of the differential game simulation on an ongoing basis. Further refinements of the simulation model should concern the larger number of ships and test the sensitivity of the game control quality to inaccuracies in the measured state variables and to changes in the parameters of the ship's dynamics.

The aim of this study was to make a novel symmetry analysis in relation to the importance of optimizing the ship’s trajectory and safety in situations at sea where there is a risk of collision with other ships. To achieve this, the state constraints in the optimization were formulated as ship domains generated by the neural network. In addition, the use of the Bellman dynamic programming method enabled the effective optimization of the ship’s safe control. The above assumptions were confirmed by the calculations of the optimal and safe ship traffic paths for the two valid agree with COLREGs states of visibility at sea and for different densities of the dynamic programming grid. Practical conclusions from the research were formulated, and a plan for further research on methods of ensuring safety in navigation was outlined.

The article presents a model of the process of safe and optimal control of an autonomous surface object in a group of encountered objects. An algorithm for determining the optimal and safe trajectory based on a multi-object game model was proposed, and an algorithm for determining the optimal trajectory was proposed for comparative analysis, not taking into account the maneuverability of other objects. Simulation studies of the algorithms made it possible to assess the optimality of the trajectories for various acceptable object strategies. An analysis of the characteristics of the sensitivity of the safe control-assessed with the risk of collision, both on the inaccuracy of navigation data and on the number of possible strategies of objects, was carried out.

The article presents the synthesis of a multi-layer group control system for a marine autonomous surface vessel with the use of modern control theory methods. First, an evolutionary programming algorithm for determining the optimal route path was presented. Then the algorithms-dynamic programming with neural state constraints, ant colony, and neuro-phase safe control algorithms-were presented. LMI and predictive line-of-sight methods were used for optimal control. The direct control layer is implemented in multi-operations on the principle of switching. The results of the computer simulation of the algorithms were used to assess the quality control.

This article presents the task of safely guiding a ship, taking into account the movement of many other marine units. An optimally neural modified algorithm for determining a safe trajectory is presented. The possible shapes of the domains assigned to other ships as traffic restrictions for the particular ship were subjected to a detailed analysis. The codes for the computer program Neuro-Constraints for generating these domains are presented. The results of the simulation tests of the algorithm for a navigational situation are presented. The safe trajectories of the ship were compared at different distances, changing the sailing conditions and ship sizes.

This article describes heuristic approaches to static optimization methods, taking into account the nature of the motion of a swarm of particles representing, for example, birds, ants, bees, fireflies, bats, krill, cuckoos, cuttlefish, cockroaches, or the pollination process of flowers. In the given descriptions of the methods, the features of swarm intelligence are detailed, the optimization quality indicators are formulated, and flow charts of the computational algorithms are provided.

A model of the process of a ship's safe control, moving in the vicinity of many other ships, was formulated, which enables the synthesis of algorithms for safe path planning that is appropriate according to the state of the environment. This state can be mapped using three possible algorithms - the game non-cooperative path, game cooperative path and optimal path. Computer simulation of the algorithms on the example of a real navigation situation recorded in the Baltic Sea of the ship's own movement in the vicinity of another nine moving ships made it possible to assess their effectiveness. When comparing the algorithms, the degree of cooperation of the vessels, the state of visibility at sea and the dynamics of the ship itself determined by the manoeuvring advance time were taken into account. The simulation results of the presented algorithms confirm the effectiveness and good representation of the real state of the traffic environment of many ships. The conclusions from the conducted research can be used to optimize other processes of controlling mobile objects.

The aim of this work is to obtain multi-objective linear programming algorithms that can be used to solve the global problem of multi-object safety control processes in order to minimize the risk of collisions. In multi-objective linear optimization models, satisfactory trade-off assesses and resolves the conflict between different control objectives. A comparison of single-, bi-, and tri-objective linear programming algorithms allows us to adapt the appropriate optimization method to the conditions of the control process. An important outcome of the present research is the demonstration of the greater effectiveness of bi- and tri-objective optimization compared to single-objective optimization, reflecting the compromises taken into account when choosing between objects and achieving a minimum risk of collision when passing them.