A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

In this paper, a review on the three most important communication techniques (ground, aerial, and underwater vehicles) has been presented that throws light on trajectory planning, its optimization, and various issues in a summarized way. This kind of extensive research is not often seen in the literature, so an effort has been made for readers interested in path planning to fill the gap. Moreover, optimization techniques suitable for implementing ground, aerial, and underwater vehicles are also a part of this review. This paper covers the numerical, bio-inspired techniques and their hybridization with each other for each of the dimensions mentioned. The paper provides a consolidated platform, where plenty of available research on-ground autonomous vehicle and their trajectory optimization with the extension for aerial and underwater vehicles are documented

Path tracking control of differential drive mobile robot based on chaotic-billiards optimization algorithm

Mobile robots are typically depending only on robot kinematics control. However, when high-speed motions and highly loaded transfer are considered, it is necessary to analyze dynamics of the robot to limit tracking error. The goal of this paper is to present a new algorithm, chaotic-billiards optimizer (C-BO) to optimize internal controller parameters of a differential-drive mobile robot (DDMR)-based dynamic model. The C-BO algorithm is notable for its ease of implementation, minimal number of design parameters, high convergence speed, and low computing burden. In addition, a comparison between the performance of C-BO and ant colony optimization (ACO) to determine the optimum controller coefficient that provides superior performance and convergence of the path tracking. The ISE criterion is selected as a fitness function in a simulation-based optimization strategy. For the point of accuracy, the velocity-based dynamic compensation controller was successfully integrated with the motion controller proposed in this study for the robot's kinematics. Control structure of the model was tested using MATLAB/Simulink. The results demonstrate that the suggested C-BO, with steady state error performance of 0.6 percent compared to ACO's 0.8 percent, is the optimum alternative for parameter optimizing the controller for precise path tracking. Also, it offers advantages of quick response, high tracking precision, and outstanding anti-interference capability

Motion Planning for Mobile Robots

This chapter introduces two kinds of motion path planning algorithms for mobile robots or unmanned ground vehicles (UGV). First, we present an approach of trajectory planning for UGV or mobile robot under the existence of moving obstacles by using improved artificial potential field method. Then, we propose an I-RRT* algorithm for motion planning, which combines the environment with obstacle constraints, vehicle constraints, and kinematic constraints. All the simulation results and the experiments show that two kinds of algorithm are effective for practical use

Sistema robótico basado en el comportamiento de las hormigas para la optimización en la búsqueda de rutas más cortas

In recent decades, bio-inspired systems or biological systems have become essential elements for the understanding and explanation of the logic and complexity of life.[1] Given a problem or task, it is always possible to find a way to find a solution, but when the tasks to be performed become complex, so do the solutions. Given a problem or task, it is always possible to find a way to find a solution, but when the tasks to be performed become complex, so do the solutions become more complex, so it is more practical to provide solutions from bio-inspired systems that have already provided solutions to many existing problems in our reality through evolution, which leads to the implementation of a bio-inspired algorithm, which takes as a basis the behavior of what nature already provides and uses it in its favor to solve real-life problems more efficiently [2][3].En las últimas décadas, los sistemas bio-inspirados o sistemas biológicos se han convertido en elementos imprescindibles para la comprensión y la explicación de la lógica y la complejidad de la vida.[1]Dado un problema o tarea siempre se puede buscar la manera de dar con una solución, pero cuando las tareas a realizar se vuelven complejas, así mismo se hacen más complejas las soluciones, por lo cual es más práctico dar solución desde los sistemas bio-inspirados que ya han dado solución a muchos problemas existentes en nuestra realidad por medio de la evolución, lo cual lleva a la implementación de un algoritmo bio-inspirado, que toma como base el comportamiento de lo que ya otorga la naturaleza y lo usa en su favor para así resolver de manera más eficiente problemas de la vida real [2][3]

Motion Planning for Autonomous Ground Vehicles Using Artificial Potential Fields: A Review

Autonomous ground vehicle systems have found extensive potential and practical applications in the modern world. The development of an autonomous ground vehicle poses a significant challenge, particularly in identifying the best path plan, based on defined performance metrics such as safety margin, shortest time, and energy consumption. Various techniques for motion planning have been proposed by researchers, one of which is the use of artificial potential fields. Several authors in the past two decades have proposed various modified versions of the artificial potential field algorithms. The variations of the traditional APF approach have given an answer to prior shortcomings. This gives potential rise to a strategic survey on the improved versions of this algorithm. This study presents a review of motion planning for autonomous ground vehicles using artificial potential fields. Each article is evaluated based on criteria that involve the environment type, which may be either static or dynamic, the evaluation scenario, which may be real-time or simulated, and the method used for improving the search performance of the algorithm. All the customized designs of planning models are analyzed and evaluated. At the end, the results of the review are discussed, and future works are proposed

A Survey and Analysis of Cooperative Multi-Agent Robot Systems: Challenges and Directions

Research in the area of cooperative multi-agent robot systems has received wide attention among researchers in recent years. The main concern is to find the effective coordination among autonomous agents to perform the task in order to achieve a high quality of overall performance. Therefore, this paper reviewed various selected literatures primarily from recent conference proceedings and journals related to cooperation and coordination of multi-agent robot systems (MARS). The problems, issues, and directions of MARS research have been investigated in the literature reviews. Three main elements of MARS which are the type of agents, control architectures, and communications were discussed thoroughly in the beginning of this paper. A series of problems together with the issues were analyzed and reviewed, which included centralized and decentralized control, consensus, containment, formation, task allocation, intelligences, optimization and communications of multi-agent robots. Since the research in the field of multi-agent robot research is expanding, some issues and future challenges in MARS are recalled, discussed and clarified with future directions. Finally, the paper is concluded with some recommendations with respect to multi-agent systems

A Systematic Literature Review of Path-Planning Strategies for Robot Navigation in Unknown Environment

The Many industries, including ports, space, surveillance, military, medicine and agriculture have benefited greatly from mobile robot technology.  An autonomous mobile robot navigates in situations that are both static and dynamic. As a result, robotics experts have proposed a range of strategies. Perception, localization, path planning, and motion control are all required for mobile robot navigation. However, Path planning is a critical component of a quick and secure navigation. Over the previous few decades, many path-planning algorithms have been developed. Despite the fact that the majority of mobile robot applications take place in static environments, there is a scarcity of algorithms capable of guiding robots in dynamic contexts. This review compares qualitatively mobile robot path-planning systems capable of navigating robots in static and dynamic situations. Artificial potential fields, fuzzy logic, genetic algorithms, neural networks, particle swarm optimization, artificial bee colonies, bacterial foraging optimization, and ant-colony are all discussed in the paper. Each method's application domain, navigation technique and validation context are discussed and commonly utilized cutting-edge methods are analyzed. This research will help researchers choose appropriate path-planning approaches for various applications including robotic cranes at the sea ports as well as discover gaps for optimization

Drone Base Station Trajectory Management for Optimal Scheduling in LTE-Based Sparse Delay-Sensitive M2M Networks

Providing connectivity in areas out of reach of the cellular infrastructure is a very active area of research. This connectivity is particularly needed in case of the deployment of machine type communication devices (MTCDs) for critical purposes such as homeland security. In such applications, MTCDs are deployed in areas that are hard to reach using regular communications infrastructure while the collected data is timely critical. Drone-supported communications constitute a new trend in complementing the reach of the terrestrial communication infrastructure. In this study, drones are used as base stations to provide real-time communication services to gather critical data out of a group of MTCDs that are sparsely deployed in a marine environment. Studying different communication technologies as LTE, WiFi, LPWAN and Free-Space Optical communication (FSOC) incorporated with the drone communications was important in the first phase of this research to identify the best candidate for addressing this need. We have determined the cellular technology, and particularly LTE, to be the most suitable candidate to support such applications. In this case, an LTE base station would be mounted on the drone which will help communicate with the different MTCDs to transmit their data to the network backhaul. We then formulate the problem model mathematically and devise the trajectory planning and scheduling algorithm that decides the drone path and the resulting scheduling. Based on this formulation, we decided to compare between an Ant Colony Optimization (ACO) based technique that optimizes the drone movement among the sparsely-deployed MTCDs and a Genetic Algorithm (GA) based solution that achieves the same purpose. This optimization is based on minimizing the energy cost of the drone movement while ensuring the data transmission deadline missing is minimized. We present the results of several simulation experiments that validate the different performance aspects of the technique