Faculty of Computing

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Subject: search.filters.subject.Robot Operating System

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Now showing 1 - 4 of 4
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    Deep Q-Network-Based Path Planning in a Simulated Warehouse Environment with SLAM Map Integration and Dynamic Obstacles
    (Department of Agribusiness, Universitas Muhammadiyah Yogyakarta, 2025-09-19) Medagangoda, H; Jayawickrama, N; de Silva, R; Samantha K.R.U.U; Abeygunawardhana, P.K.W
    With the rise of e-Commerce and the evolution of robotic technologies, the focus on autonomous navigation within warehouse environments has increased. This study presents a simulation-based framework for path planning using Deep Q-Networks (DQN) in a warehouse environment modeled with moving obstacles. The proposed solution integrates a prebuilt map of the environment generated using Simultaneous Localization and Mapping (SLAM), which provides prior spatial knowledge of static obstacles. The reinforcement learning model is formulated with a state space derived from grayscale images that combine the static map generated by SLAM and dynamic obstacles in real time. The action space consists of four discrete movements for the agent. A reward shaping strategy includes a distance-based reward and penalty for collisions to encourage goal-reaching and discourage collisions. An epsilon-greedy policy with exponential decay is used to balance exploration and exploitation. This system was implemented in the Robot Operating System (ROS) and Gazebo simulation environment. The agent was trained over 1000 episodes and metrics such as the number of actions executed to reach the goal and the cumulative reward per episode were analyzed to evaluate the convergence of the proposed solution. The results across two goal locations show that incorporating the SLAM map enhances learning stability, with the agent reaching a goal approximately 150 times, nearly double the success rate compared to the baseline without map information, which achieved only 80 successful episodes over the same number of episodes. This indicates faster convergence and reduced exploration overhead due to improved spatial awareness.
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    Moving a Robot In Unknown Areas Without Collision Using Robot Operating System
    (IEEE, 2022-02-23) Gayashani, K. K. P; Rajapaksha, S; Jayawardena, C
    Nowadays, robots have become a most crucial role. With technology development, we can do so many things using robotic technology. There are lots of projects in which robots move in a known area. This study proposes a mechanism to move a robot in an unknown area. We can use this kind of robot in hazardous environments, and we can use this robot in several ways. The proposed system is based on the Robotic Operation System (ROS) and the simulator Gazebo. The obstacle avoidance part is done using a laser sensor. After that, there should be a direction-changing mechanism in the developing algorithm. That implemented using loops. Because after the robot changes direction, it again needs to check whether another object is there in the navigated location. The proposed algorithm was developed with the autonomous navigation mechanism. Map generation is another functionality of this project. It is done using Simultaneous Localization And Mapping (SLAM). Map visualization was done using the Rviz application. With the robot’s movement, the robot’s current position is calculated using x, y, and z coordinates. Also, this project has included reverse navigation functionality. Reverse navigation is a novel section in this research work. The objective of this study and the outcome is to move the robot without having any crashes. Also, we can use this to evaluate dangerous areas. Experimental results of the direction and velocity changes have been mentioned in the results and discussion section.
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    Ontology based Optimized Algorithms to Communicate with a Service Robot using a User Command with Unknown Terms
    (2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Jayawardena, C.; Rajapaksha, U. U.S.
    In real world applications, seamless integration of heterogeneous robots is very important to complete a task given by high level user instruction with unknown terms to all robotic devices simultaneously. In this research, we have used the technologies in Semantic Web mainly with the use of the ontology to represent the meaning of the unknown terms in the given high level instruction. If a user has given an instruction in domestic environment as “clean My Room 01 while finding my key for the car” to clean different locations with different capabilities and there can be robot who does not the meaning of the “key”. The robot can get the meaning of the unknown term by communicating with the semantic analyzer which is working with the ontology. According to our analysis we have proved that the object represented by the unknown term can be detected more accurately with compared to existing object detection algorithms since our ontology can represents more concepts related to the given object. The results indicate that if number of unknown terms in the command are increased then the time taken to process the command also be increased.
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    PublicationEmbargo
    Ontology based Optimized Algorithms to Communicate with a Service Robot using a User Command with Unknown Terms
    (IEEE, 2020-12-10) Rajapaksha, U. U. S; Jayawardena, C
    In real world applications, seamless integration of heterogeneous robots is very important to complete a task given by high level user instruction with unknown terms to all robotic devices simultaneously. In this research, we have used the technologies in Semantic Web mainly with the use of the ontology to represent the meaning of the unknown terms in the given high level instruction. If a user has given an instruction in domestic environment as “clean My Room 01 while finding my key for the car” to clean different locations with different capabilities and there can be robot who does not the meaning of the “key”. The robot can get the meaning of the unknown term by communicating with the semantic analyzer which is working with the ontology. According to our analysis we have proved that the object represented by the unknown term can be detected more accurately with compared to existing object detection algorithms since our ontology can represents more concepts related to the given object. The results indicate that if number of unknown terms in the command are increased then the time taken to process the command also be increased.