TRAVERSE-Voice Commanded Self Navigating Indoor Robot

This paper presents a complete Simultaneous Localization and Mapping (SLAM) solution for indoor mobile robots, addressing feature extraction, autonomous exploration and navigation using the continuously updating map. The platform used is Pioneer PeopleBot equipped with SICK Laser Measurment System (LMS) and odometery. Our algorithm uses Hough Transform to extract the major representative features of indoor environment such as lines and edges. Localization is accomplished using Relative Filter which depends directly on the perception model for the correction of error in the robot state. Our map for localization is in the form of a landmark network whereas for navigation we are using occupancy grid. The resulting algorithm makes the approach computationally lightweight and easy to implement. Finally, we present the results of testing the algorithm in Player/Stage as well as on PeopleBot in our Robotics and Control Lab.

International Journal of Computer Applications, 2014

This paper solves the problems of Simultaneous localization and mapping (SLAM) that deals with local path planning of an autonomous mobile robot in indoor environment, by using sonar sensors for object detection and range information, and also uses wheel encoders for tracking robot position and orientation based on dead-reckoning process.

Engineering Technology And Applied Sciences Research, 2022

Mapping is an essential and basic requirement for a mobile robot in order to be able to navigate autonomously. This paper proposes a solution for autonomous navigation and realtime mapping using the virtual humanoid robot called NAO. The robot navigates through its environment using ultrasonic sensors only and develops a 2-D map of the environment. For implementation and testing, the Webots simulator was used. It provides real-time values, modification and designing of the 3-D world arena, and plugins for other parameters control. We test autonomous navigation in differently shaped environments. The proposed mathematical algorithm allows the autonomous navigation of the robot and calculates the position of the robot and the obstacles (if any). The results indicate that the algorithm can localize the robot within the environment whereas the accuracy in localization can be increased by adding a control constant to the orientation of the robot. The results demonstrate that the algorithm is more effective in the rectangular arena than in the triangular and pentagon arenas.

International Journal of Engineering and Technology, 2017

Autonomous robots are intelligent machines capable of performing tasks by themselves without human control. In this work, an autonomous robot that can navigate an indoor restaurant setting and assume the role of an automated customer servicing system is proposed. The robot has to be aware of its own position in the workspace and compute the shortest path to the customer when introduced in a known environment. The RRT path planning algorithm is optimized using goal biasing and vector field RRT is built upon it to reduce the total number of computations required, eventually reducing the latency. We closely model a real time restaurant environment where multiple customers request for service simultaneously or at random instants of time. The servicing robot has to prioritize requests based on different parameters such as distance-to-goal, number of obstacles in the path, frequency of calls made by the customer and other special concession for customers based on their age etc. and appropriately service the table with highest priority. The performance analysis of the proposed algorithm is compared with RRT based routing and it is observed that there is significant reduction in servicing time when the proposed model is implemented.

Proccedings of the ICAIIT2016, 2016

The publication presents a three wheeled robot that has been designed to map rooms, halls and other indoor areas. The device uses an ultrasonic sensor for measuring distance, which is later used for both navigation and obstacle detection. Data were used later to compose a matrixthe schematic map of the room. This map could be uploaded to the cloud for later use by other 3rd party devices so they do not have to redo the mapping process again.

— This Paper proposed a new navigation method for indoor mobile robots. The robot system is composed of a Radio Frequency Identification (RFID) tag sensor and Ultrasonic sensors. The RFID tags are used as landmarks for global path planning and the topological relation map which shows the connection of scattered tags through the environment is used as course instructions to a goal. The robot automatically moves along hallways using the scanned range data until a tag is found and then refers to the topological map for the next movement. Our proposed technique would be useful for real-world robotic applications such as intelligent navigation for motorized wheelchairs, surveillance and security purposes and in Nuclear power plants where humans are prone to harmful radiations.

IGI Global eBooks, 2020

Identifying the current location of a robot is a prerequisite for robot navigation. To localize a robot, one popular way is to use particle filters that estimate the posterior probabilistic density of a robot's state space. But this Bayesian recursion approach is computationally expensive. Most microcontrollers in a small mobile robot cannot afford it. The authors propose to use a smartphone as a robot's brain in which heavy-duty computations take place whereas an embedded microcontroller on the robot processes rudimentary sensors such as ultrasonic and touch sensors. In their design, a smartphone is wirelessly connected to a robot via Bluetooth by which distance measurements from the robot are sent to the smartphone. Then the smartphone takes responsible for computationally expensive operations like executing the particle filter algorithm. In this paper, the authors designed a mobile robot and its control architecture to demonstrate that the robot can navigate indoor environment while avoiding obstacles and localize its current position.

This paper analyzes the Monte Carlo localization (MCL) method, which estimates the pose of an indoor mobile robot. A mobile robot must know where it is to navigate in an indoor environment. The MCL technique is one of the most influential and popular techniques for estimation of robot position and orientation using a particle filter. For the analysis, we perform experiments in an indoor environment with a differential drive robot and ultrasonic range sensor system. The analysis uses MATLAB for implementation of the MCL and investigates the effects of the control parameters on the MCL performance. The control parameters are the uncertainty of the motion model of the mobile robot and the noise level of the measurement model of the range sensor.

IEEE International Conference on Electro/Information Technology, 2014

This paper addresses the challenge of mobile robot navigation in indoor environments. There is a critical need for cost-effective, reliable, and fairly accurate solutions to meet the demands of indoor robotic applications. Currently, researchers are exploring various approaches for this problem. The one we are presenting in this paper is based on QR (Quick Response) codes to provide location references for mobile robots. The mobile robot is equipped with a Smartphone that is programmed to detect and read information on QR codes that are strategically placed in the operating environment of the robot. The mobile robot can perform the autonomous run throughout the guide route by using real-time QR code recognition. The lab information on QR code is played to the visitors using Text-to-Speech provided through Android device. Ultrasonic range sensors which can detect objects and measure distances with high accuracy are used to implement the wallfollowing and obstacle-avoidance behaviors. The collected sonar range information by ultrasonic range sensors is processed by a microcontroller that autonomously controls a tour guide robot. An algorithm based on a proportional-integral-derivative (PID) control is applied to the tour guide robot to perform more accurate robot motion control. A Bluetooth technology is used to send stored information on QR codes from the Smartphone to the tour guide robot wirelessly. The experimental setup of the tour guide robot along with the successful implementation of the efficient method for a navigation technique is presented.

2016

This paper presents the use of probabilistic algo-rithms in a differential drive robot for totally autonomous nav-igation in indoor environment. The robot is capable of buildingmap of its environment and global localization in the map thusbuilt. The robot then performs path planning to compute anoptimal path towards the goal location. We make use of LiDARfor mapping and localization, and a Microsoft Kinect is used inaddition to detect obstacles not falling in the plane of LiDAR.