Path Navigation For Robot Using Matlab

Path navigation using fuzzy logic controller and trajectory prediction table is to drive a robot in the dynamic environment to a target position,without collision. This path navigation method consists of static navigation method and dynamic path planning. The static navigation used to avoid the static obstacles by using fuzzy logic controller, which contains four sensor input and two output variables. If the robot detects moving obstacles, the robot can recognize the velocity and moving direction of each obstacle and generate the Trajectory Prediction Table to predict the obstacles’ future trajectory. If the trajectory prediction table which reveals that the robot will collide with an obstacle, the dynamic path planning will find a new collision free path to avoid the obstacle by waiting strategy or detouring strategy. . A lot of research work has been carried out in order to solve this problem. In order to navigate successfully in an unknown or partially known environment, the mobile robots should be able to extract the necessary surrounding information from the environment using sensor input, use their built-in knowledge for perception and to take the action required to plan a feasible path for collision free motion and to reach the goal

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

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