Research On Robot Path Planning In Indoor Environment

Autonomous robotics is one of the key subjects of this generation of research and has grown to be more and more popular in recent years uses especially in such industries,like hospitals,hotels,and plenty more.To that,path planning,mapping,and localization is a critical issue in the applications of these autonomous mobile robots.Autonomously making a map,localizing within it,and planning with it are fundamental problems in mobile robotics.Every mobile robotic system must include a solution to all problems.Path planning and the interdependence of mapping and localization is very well known as the Simultaneous Localization and Mapping(SLAM)issue,there is indeed a growing realization in the research field.Path planning that how a robot goes about mapping and finding an environment(and then operating in the environment)can avoid obstacles degenerate conditions and it is needed to greatly reduce path planning and SLAM complexity.Its objectives are mapping,localizing in an unknown environment,and finding the most suitable short path planning for the robot from a beginning place to a destination place in surroundings with complex barriers(obstacles).In view of the shortcomings of the preceding by traditional A* algorithm in simplifying the problem of finding a path for an autonomous mobile robot.This thesis has two main contributions.The first main contribution is to improve the path planning of autonomous mobile robot in the indoor environment on the basis of the A star algorithm and offer a unique capability field approach for time efficiency,moving the robots in the complex obstacle to getting the goal,and obtain better path for robots as well as for solving the problems that the traditional A* algorithm method often converges of much time,longdistance,and some oscillatory movement of the robot.By using the camera of the mobile phone or a webcam to capture the environment or by design/sketching the layout of indoor environment images according to the demand needed gathering the environmental area and then processing the image for the system is an important aspect of the system for working map.This working map is used for path planning utilization.The second main contribution of this thesis is building a map,localizing within it,and planning with it,so that the system has three components: mapping,localization,and planning.The mapping module uses the Cartography and G-mapping ROS package and algorithm to create a global map for an unknown environment.The localization module estimates the mobile robot's current position and orientation within the built map using the ACML ROS package and the particle filter-based AMCL approach.Using the controller and move base ROS package,the planning module creates a local cost map for collision avoidance,generates collision-free trajectories to reach a goal pose,and generates control commands to follow the trajectories.In generally the simulation results of path planning show that the method proposed is optimal path,time-efficient,and an effective algorithm is greatly improved,and as well as the method proposed for mapping and localization is also show that the system provides dependable performance for autonomous indoor mobile robot mapping,localization,and planning including collision avoidance with both static and dynamic objects.