Search for: All records

Abstract Simultaneous Localization and Mapping (SLAM) is an autonomous localization technique used for mobile robots without GPS. Since autonomous localization relies on pre-existing maps, to use SLAM with the Robotic Operating System (ROS), a map of the surroundings must first be created, and a controller can then use the initial map. The first mapping procedure is mostly carried out manually, with human intervention. When operating manually, the person operating the robot is responsible for avoiding obstacles and moving the robot to different sections of the space to create a full map of the entire environment. The mapping process, if done manually, is time demanding, and often not feasible. To solve this constraint, which is to construct a map of the environment autonomously without human involvement while avoiding obstacles, the Vector Field Histogram (VFH) technique is implemented in this study by integrating it with SLAM. VFH is a real-time motion planning approach in robotics that uses a statistical representation of the robot’s surroundings known as the histogram grid, to place a strong emphasis on handling modeling errors and sensor uncertainty. Furthermore, using range sensor values, the VFH algorithm determines a robot’s obstacle-free driving directions. Aside from its real-time obstacle avoidance function, the VFH method is enhanced in this study to collaborate with SLAM to create maps and reduce localization complexity. While generating maps, the VFH approach uses a two-step data-reduction procedure to calculate the appropriate vehicle control directives. The robot’s temporary location is used to generate a one-dimensional polar histogram, which is the first stage of the histogram grid reduction process. The polar obstacle density in a given direction is represented by a value in each sector of the polar histogram. In the second stage, the robot’s steering is oriented in the direction of the most appropriate sector, which the algorithm determines from all the polar histogram sectors with a low polar obstacle density. Following that, further algorithms, such as Rapidly Exploring Random Tree (RRT) and A*, can be used to plan autonomous pathways using the map provided by VFH. In order to put the concept into practice, MATLAB and ROS are used together in collaboration to autonomously and simultaneously map the environment and localize the robot. The combination of MATLAB and ROS provides many advantages because of their extensive feature set and ability to integrate with each other. Finally, a simulation and a real-time robot are utilized to analyze and validate the study’s findings. 

Abstract In contemporary manufacturing processes, reliable but efficient pick-and-place robots are frequently used. The automation and optimization of the pick and place procedures utilizing various path-planning approaches thereby support the expansion of application areas. Yet, the design of a controller faces significant difficulties due to the nonlinearities inherent in robotic manipulators and the unpredictable nature of the ambient factors. In place of the classic model predictive control (MPC), this paper presents the application of the Nonlinear Model Predictive Controller (NLMPC) as an acceptable control mechanism for real-time optimization and robust stability of the KINOVA Gen3 robotic arm. The developed NLMPC-based method ensures that the robotic arm does not run into obstacles in the workplace or with itself while reaching, gripping, selecting, and placing the necessary items. To acquire the control input trajectory, the optimization in NLMPC is solved repeatedly. When input constraints are available, the modeled system tracks reference trajectories to achieve the aim of recognizing and organizing distinct objects. After the NLMPC is successfully developed, a simulation environment is built and finally brought to life by combining all the processes into one using a MATLAB Stateflow chart.