Adaptive and intelligent navigation of autonomous planetary rovers - A survey

The application of robotics and autonomous systems in space has increased dramatically. The ongoing Mars rover mission involving the Curiosity rover, along with the success of its predecessors, is a key milestone that showcases the existing capabilities of robotic technology. Nevertheless, there has still been a heavy reliance on human tele-operators to drive these systems. Reducing the reliance on human experts for navigational tasks on Mars remains a major challenge due to the harsh and complex nature of the Martian terrains. The development of a truly autonomous rover system with the capability to be effectively navigated in such environments requires intelligent and adaptive methods fitting for a system with limited resources. This paper surveys a representative selection of work applicable to autonomous planetary rover navigation, discussing some ongoing challenges and promising future research directions from the perspectives of the authors

REAL-TIME PLATOONING OF MOBILE ROBOTS: STUDY OF TRAJECTORY WITH OBSTACLE AVOIDANCE

Robot platooning is a robot positioning method, whereby a few robots will be moving in formation. Platooning has been a very commonly discussed technique especially for its applications on vehicles. However, up until now, it still has many issues that are preventing it from being implemented in real life and one of them is its irregular trajectory in the maintaining phase. In this project, study will be made on the real-time platooning trajectory in its maintaining phase under the most two common factors in real life, which are changes in velocity and presence of obstacle. By studying its performance under the mentioned factors, this project aims to contribute by having algorithm that can improve the platooning trajectory during its maintaining phase.In order to do so, two robotic cars are used as models to simulate real vehicles

Intelligent Robotics Navigation System: Problems, Methods, and Algorithm

This paper set out to supplement new studies with a brief and comprehensible review of the advanced development in the area of the navigation system, starting from a single robot, multi-robot, and swarm robots from a particular perspective by taking insights from these biological systems. The inspiration is taken from nature by observing the human and the social animal that is believed to be very beneficial for this purpose. The intelligent navigation system is developed based on an individual characteristic or a social animal biological structure. The discussion of this paper will focus on how simple agent’s structure utilizes flexible and potential outcomes in order to navigate in a productive and unorganized surrounding. The combination of the navigation system and biologically inspired approach has attracted considerable attention, which makes it an important research area in the intelligent robotic system. Overall, this paper explores the implementation, which is resulted from the simulation performed by the embodiment of robots operating in real environments

Contribuições à locomoção de robôs móveis não-holonômicos usando controle fuzzy baseado em modelo

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Automação e Sistemas, Florianópolis, 2015.A locomoção de robôs móveis apresenta desafios no controle da execução de trajetórias, principalmente quando o tipo de robô exibe não-holonomia, pois as restrições de movimento e atuação imprimem, entre outras, redução no conjunto factível de trajetórias. As principais soluções da literatura, aplicáveis e de desempenho comprovado, não apresentam uma forma automática para cálculo de parâmetros de controle que garantam requisitos de desempenho. Este trabalho desenvolve estruturas de locomoção compostas por controladores fuzzy baseados em modelos Takagi-Sugeno (TS-Fuzzy) que são capazes de representar o problema de rastreamento de trajetórias e solucioná-lo com qualidade equivalente às principais técnicas existentes e fornecem, ainda, capacidade de cálculo automático de ganhos que garantem requisitos de desempenho do sistema de controle, ou em caso de não haver solução, acusar inexistência de tais ganhos. Descreve-se uma solução completa de locomoção, composta pelos compensadores propostos e uma técnica de planejamento de locomoção capaz de gerar referências factíveis às limitações de Robôs Móveis Diferenciais (RMDs). Com esta solução foi possível a aplicação prática e a análise de desempenho das estruturas de controle descritas. Os desenvolvimentos teóricos são ilustrados através de aplicações experimentais e simuladas, baseadas na plataforma robótica Powerbot que representa um RMD de médio porte.Abstract : The challenges in locomotion control of non-holonomic mobile robots come from constraints related to sub actuation and trajectory feasibility. The main solutions found in the literature, with proved performance and applicability does not show an automatic method to compute control gains that guarantee global performance requirements. This work develops locomotion structures composed by Takagy-Sugeno fuzzy model based controllers. These structures are capable to represent and solve the trajectory-tracking problem with quality equivalent to the main existent techniques with the capability to compute the controller gains automatically, ensuring performance requirements to the compensator or even evince their absence in case of no solution. The document describes a full locomotion solution, composed by the developed controllers and a methodology of locomotion planning. The planning method is capable of generating feasible references over the locomotion and actuation constraints of the differential mobile robots (DMRs). This solution provides the practical application and performance analysis of the proposed control architectures. The theoretical achievements of this work are illustrated by experimental and simulated scenarios based on the Powerbot robotic platform, witch one represents a DMR of medium size

Mobile Robot Navigation in Static and Dynamic Environments using Various Soft Computing Techniques

The applications of the autonomous mobile robot in many fields such as industry, space, defence and transportation, and other social sectors are growing day by day. The mobile robot performs many tasks such as rescue operation, patrolling, disaster relief, planetary exploration, and material handling, etc. Therefore, an intelligent mobile robot is required that could travel autonomously in various static and dynamic environments. The present research focuses on the design and implementation of the intelligent navigation algorithms, which is capable of navigating a mobile robot autonomously in static as well as dynamic environments. Navigation and obstacle avoidance are one of the most important tasks for any mobile robots. The primary objective of this research work is to improve the navigation accuracy and efficiency of the mobile robot using various soft computing techniques. In this research work, Hybrid Fuzzy (H-Fuzzy) architecture, Cascade Neuro-Fuzzy (CN-Fuzzy) architecture, Fuzzy-Simulated Annealing (Fuzzy-SA) algorithm, Wind Driven Optimization (WDO) algorithm, and Fuzzy-Wind Driven Optimization (Fuzzy-WDO) algorithm have been designed and implemented to solve the navigation problems of a mobile robot in different static and dynamic environments. The performances of these proposed techniques are demonstrated through computer simulations using MATLAB software and implemented in real time by using experimental mobile robots. Furthermore, the performances of Wind Driven Optimization algorithm and Fuzzy-Wind Driven Optimization algorithm are found to be most efficient (in terms of path length and navigation time) as compared to rest of the techniques, which verifies the effectiveness and efficiency of these newly built techniques for mobile robot navigation. The results obtained from the proposed techniques are compared with other developed techniques such as Fuzzy Logics, Genetic algorithm (GA), Neural Network, and Particle Swarm Optimization (PSO) algorithm, etc. to prove the authenticity of the proposed developed techniques