Path Planning of Anti ship Missile based on Voronoi Diagram and Binary Tree Algorithm

The path planning of anti-ship missile should be considered both cruising in safety and striking in quick, which is an intractable problem. In particular, it is difficult to consider the safety of each missile path in the path planning of multiple missiles. To solve this problem, the “AREA Algorithm” is presented to divide the relative relations of areas：relative security area of the threat areas and fast-attack area of target approaching. Specifically，it is a way to achieve area division through the relationship between the target and the center of the operational area. The Voronoi diagram topology network, Dijkstra algorithm and binary tree algorithm have been used in the above process as well. Finally, Simulations have verified the feasibility and obvious advantages of “AREA Algorithm” compared with the single algorithm, and the tactical meaning in path planning of multiple missiles

Gravitational Search and Harmony Search Algorithms for Solving the Chemical Kinetics Optimization Problems

The article is dedicated to the analysis of the global optimization algorithms application to the solution of inverse problems of chemical kinetics. Two heuristic algorithms are considered - the gravitational search algorithm and the harmony algorithm. The article describes the algorithms, as well as the application of these algorithms to the optimization of test functions. After that, these algorithms are used to search for the kinetic parameters of two chemical processes – propane pre-reforming on Ni-catalyst and catalytic isomerization of pentane-hexane fraction. For the first process both algorithms showed approximately the same solution, while for the second problem the gravitational search algorithm showed a smaller value of the minimizing function. Wherefore, it is concluded that on large-scale problems it is better to use the gravitational search algorithm rather than the harmony algorithm, while obtaining a smaller value of the minimizing function in a minimum time. On low-scale problems both algorithms showed approximately the same result, while demonstrating the coincidence of the calculated data with the experimental ones

A generalized laser simulator algorithm for optimal path planning in constraints environment

Path planning plays a vital role in autonomous mobile robot navigation, and it has thus become one of the most studied areas in robotics. Path planning refers to a robot's search for a collision-free and optimal path from a start point to a predefined goal position in a given environment. This research focuses on developing a novel path planning algorithm, called Generalized Laser Simulator (GLS), to solve the path planning problem of mobile robots in a constrained environment. This approach allows finding the path for a mobile robot while avoiding obstacles, searching for a goal, considering some constraints and finding an optimal path during the robot movement in both known and unknown environments. The feasible path is determined between the start and goal positions by generating a wave of points in all directions towards the goal point with adhering to constraints. A simulation study employing the proposed approach is applied to the grid map settings to determine a collision-free path from the start to goal positions. First, the grid mapping of the robot's workspace environment is constructed, and then the borders of the workspace environment are detected based on the new proposed function. This function guides the robot to move toward the desired goal. Two concepts have been implemented to find the best candidate point to move next: minimum distance to goal and maximum index distance to the boundary, integrated by negative probability to sort out the most preferred point for the robot trajectory determination. In order to construct an optimal collision-free path, an optimization step was included to find out the minimum distance within the candidate points that have been determined by GLS while adhering to particular constraint's rules and avoiding obstacles. The proposed algorithm will switch its working pattern based on the goal minimum and boundary maximum index distances. For static obstacle avoidance, the boundaries of the obstacle(s) are considered borders of the environment. However, the algorithm detects obstacles as a new border in dynamic obstacles once it occurs in front of the GLS waves. The proposed method has been tested in several test environments with different degrees of complexity. Twenty different arbitrary environments are categorized into four: Simple, complex, narrow, and maze, with five test environments in each. The results demonstrated that the proposed method could generate an optimal collision-free path. Moreover, the proposed algorithm result are compared to some common algorithms such as the A* algorithm, Probabilistic Road Map, RRT, Bi-directional RRT, and Laser Simulator algorithm to demonstrate its effectiveness. The suggested algorithm outperforms the competition in terms of improving path cost, smoothness, and search time. A statistical test was used to demonstrate the efficiency of the proposed algorithm over the compared methods. The GLS is 7.8 and 5.5 times faster than A* and LS, respectively, generating a path 1.2 and 1.5 times shorter than A* and LS. The mean value of the path cost achieved by the proposed approach is 4% and 15% lower than PRM and RRT, respectively. The mean path cost generated by the LS algorithm, on the other hand, is 14% higher than that generated by the PRM. Finally, to verify the performance of the developed method for generating a collision-free path, experimental studies were carried out using an existing WMR platform in labs and roads. The experimental work investigates complete autonomous WMR path planning in the lab and road environments using live video streaming. The local maps were built using data from live video streaming s by real-time image processing to detect the segments of the lab and road environments. The image processing includes several operations to apply GLS on the prepared local map. The proposed algorithm generates the path within the prepared local map to find the path between start-to-goal positions to avoid obstacles and adhere to constraints. The experimental test shows that the proposed method can generate the shortest path and best smooth trajectory from start to goal points in comparison with the laser simulator

Learning Team-Based Navigation: A Review of Deep Reinforcement Learning Techniques for Multi-Agent Pathfinding

Multi-agent pathfinding (MAPF) is a critical field in many large-scale robotic applications, often being the fundamental step in multi-agent systems. The increasing complexity of MAPF in complex and crowded environments, however, critically diminishes the effectiveness of existing solutions. In contrast to other studies that have either presented a general overview of the recent advancements in MAPF or extensively reviewed Deep Reinforcement Learning (DRL) within multi-agent system settings independently, our work presented in this review paper focuses on highlighting the integration of DRL-based approaches in MAPF. Moreover, we aim to bridge the current gap in evaluating MAPF solutions by addressing the lack of unified evaluation metrics and providing comprehensive clarification on these metrics. Finally, our paper discusses the potential of model-based DRL as a promising future direction and provides its required foundational understanding to address current challenges in MAPF. Our objective is to assist readers in gaining insight into the current research direction, providing unified metrics for comparing different MAPF algorithms and expanding their knowledge of model-based DRL to address the existing challenges in MAPF.Comment: 36 pages, 10 figures, published in Artif Intell Rev 57, 41 (2024

Da racionalidade axiomática à racionalidade ecológica: elementos para a construção de uma agenda de pesquisa em simulação computacional econômica

This paper discusses the development of a research agenda in economic computer simulation for the notion of ecological rationality. It starts with the criticism of axiomatic rationality in economics, discussing its ineffectiveness for the treatment of real decision processes. It is shown that the criticism of axiomatic rationality made by Herbert Simon and his notion of Procedural Rationality allowed the development of a research program that systematically incorporates the treatment of heuristics and can consistently integrate the observation of the real behavior of agents. This research program was developed by Gerd Gigerenzer, along with the notion of Ecological Rationality. Therefore, this work proposes the development of a research agenda in computational economic simulation to obtain a set of empirical results that can support the advancement of the theory.Este artigo discute o desenvolvimento de uma agenda de pesquisa em simulação computacional econômica para a noção de racionalidade ecológica. Parte-se das críticas à racionalidade axiomática em economia, discutindo-se a sua ineficácia para o tratamento de processos de decisão real. Mostra-se que a crítica à racionalidade axiomática feita por Herbert Simon e à sua noção de Racionalidade Processual permitiu o desenvolvimento de um programa de pesquisa que incorpora sistematicamente o tratamento de heurísticas e pode integrar de maneira consistente a observação do comportamento real dos agentes. Esse programa de pesquisa foi desenvolvido por Gerd Gigerenzer, juntamente com a noção de Racionalidade Ecológica. Propõe-se então, neste artigo, o desenvolvimento de uma agenda de pesquisa em simulação econômica computacional para a obtenção de um conjunto de resultados empíricos que possa apoiar o avanço da teoria

Development of Fully Autonomous and Cooperative Robotic System for Interplanetary Explorations

The next frontier of interplanetary exploration missions would encounter countless unpredictable geographical challenges including uninhabitable caves, icy craters of the Moon and Mars, unsustainable mountain cliffs, high radiation areas, and extreme temperature environments. This research will design a fully autonomous and a cooperative robotic team composed of unmanned ground vehicles (UGVs) with hybrid operational modes to tackle the multiple traveling salesman problem (mTSP) and to overcome environmental obstacles, to accomplish the challenging interplanetary exploration missions. The hybrid operational modes allow every UGV in the team to not only travel on a ground surface but also jump over obstacles, and these UGVs were named jumping rovers. The jumping capability provides a flexible form of locomotion by leaping and landing on top of obstacles instead of navigating around obstacles. Through the cooperation of heterogeneous robots, the goal is to explore unknown areas subject to extreme environmental conditions. To solve the mTSP, an optimal path between any two objective points in an mTSP is determined by the optimized rapidly-exploring random tree method, named RRT*, and is further improved through a refined RRT* algorithm to find a smoother path between targets. Then, the mTSP is formulated as a mixed-integer linear programming (MILP) problem to search for the most cost-effective combination of paths for multiple UGVs that can allocate tasks like visiting target points. The effectiveness of the hybrid operational modes and optimized motion with assigned tasks is verified in an indoor, physical experimental environment using customized jumping rovers.A one-year embargo was granted for this item.Academic Major: Mechanical Engineerin

Analytical Models and Control Design Approaches for a 6 DOF Motion Test Apparatus

Wind tunnels play an indispensable role in the process of aircraft design, providing a test bed to produce valuable, accurate data that can be extrapolated to actual flight conditions. Historically, time-averaged data has made up the bulk of wind tunnel research, but modern flight design necessitates the use of dynamic wind tunnel testing to provide time-accurate data for high frequency motion. This research explores the use of a 6 degree of freedom (DOF) motion test apparatus (MTA) in the form of a robotic arm to allow models inside a subsonic wind tunnel to track prescribed trajectories to obtain time-accurate force and moment coefficients. Specifically, different control laws were designed, simulated, and integrated into a 2 DOF model representative of the elbow pitch and wrist pitch joints of the MTA system to decrease positional tracking error for a desired end-effector trajectory. Stability of the closed-loop systems was proven via Lyapunov analysis for all of the control laws, and the control laws proved to decrease tracking error during the trajectory case studies. An adaptive sliding mode control scheme was chosen as most suitable to simulate on the 6 DOF model due to the small tracking error as compared to the other control schemes and the availability of parameters of the actual MTA system when subject to the time-varying aerodynamics of the wind tunnel

Научная инициатива иностранных студентов и аспирантов. Т. 1

Сборник представляет интерес для специалистов и исследователей в области математики, механики, электротехники, информатики и вычислительных систем, физики, химии, геологии, гуманитарных наук и экономики

Научная инициатива иностранных студентов и аспирантов. Т. 1

Сборник представляет интерес для специалистов и исследователей в области математики, механики, электротехники, информатики и вычислительных систем, физики, химии, геологии, гуманитарных наук и экономики