Roni Permana Saputra

Midriem Mirdanies, Roni Permana Saputra, Edwar Yazid et al.

This paper describes the implementation of a learning-based lane detection algorithm on an Autonomous Mobile Robot. It aims to implement the Ultra Fast Lane Detection algorithm for real-time application on the SEATER P2MC-BRIN prototype using a camera and optimize its performance on the Jetson Nano platform. Preliminary experiments were conducted to evaluate the algorithm's performance in terms of data processing speed and accuracy using two types of datasets: outdoor using a public dataset and indoor using an internal dataset from the indoor area of the BRIN Workshop Building in Bandung. The experiments revealed that the algorithm runs more optimally on the Jetson Nano platform after conversion to TensorRT compared to the ONNX model, achieving processing speeds of approximately 101 ms using CULane and 105 ms using TuSimple, which is about 22 times faster than the previous model. While the algorithm demonstrates good accuracy on the outdoor public dataset, its performance falls short on the indoor dataset. Future work should focus on transfer learning and fine-tuning to enhance indoor lane detection accuracy.

Roni Permana Saputra, Estiko Rijanto

Automatic Guided Vehicle (AGV) has been widely applied in automatic logistics system because it provides flexibility and efficiency. This paper addresses review and design of multi AGVs system (MAGS) and its coordination control to be implemented at containers terminal logistics systems. It focuses on design of electric AGV and supervisory control to coordinate multi AGVs. Firstly, related previous works by other researchers are reviewed including containers terminal system, application of AGV in logistics systems and multi mobile robots control systems. Secondly, application of AGV system in containers terminal is proposed including AGV design specification, AGV control method, multi AGV system supervisory control method, and containers terminal port model. Conclusions have been obtained as follows: an electric AGV may be developed using electric motors with total power between 700-900KW and speed of 228 rpm; a MAGVS is proposed based on free ranging navigation system using GPS, digital compass and rotary encoder sensors; path planning, coordination and traffic management control is conducted by the supervisory controller based on priority based coordinative (PBC) control algorithm; a model of MAGVS for containers terminal logistics system can be developed using the navigation system and PBC control algorithm.

Roni Permana Saputra, Nemanja Rakicevic, Petar Kormushev

This paper addresses the problem of human body detection---particularly a human body lying on the ground (a.k.a. casualty)---using point cloud data. This ability to detect a casualty is one of the most important features of mobile rescue robots, in order for them to be able to operate autonomously. We propose a deep-learning-based casualty detection method using a deep convolutional neural network (CNN). This network is trained to be able to detect a casualty using a point-cloud data input. In the method we propose, the point cloud input is pre-processed to generate a depth image-like ground-projected heightmap. This heightmap is generated based on the projected distance of each point onto the detected ground plane within the point cloud data. The generated heightmap -- in image form -- is then used as an input for the CNN to detect a human body lying on the ground. To train the neural network, we propose a novel sim-to-real approach, in which the network model is trained using synthetic data obtained in simulation and then tested on real sensor data. To make the model transferable to real data implementations, during the training we adopt specific data augmentation strategies with the synthetic training data. The experimental results show that data augmentation introduced during the training process is essential for improving the performance of the trained model on real data. More specifically, the results demonstrate that the data augmentations on raw point-cloud data have contributed to a considerable improvement of the trained model performance.

Roni Permana Saputra

The main goal of this project is that the basic EKF-based SLAM operation can be implemented sufficiently for estimating the state of the UGV that is operated in this real environment involving dynamic objects. Several problems in practical implementation of SLAM operation such as processing measurement data, removing bias measurement, extracting landmarks from the measurement data, pre-filtering extracted landmarks and data association in the observed landmarks are observed during the operation of EKF-based SLAM system . In addition, the comparison of EKF-based SLAM operation with dead reckoning operation and Global Positioning System (GPS) are also performed to determine the effectiveness and performance of EKF-based SLAM operation in the real environment.

Roni Permana Saputra, Petar Kormushev

One of the most important features of mobile rescue robots is the ability to autonomously detect casualties, i.e. human bodies, which are usually lying on the ground. This paper proposes a novel method for autonomously detecting casualties lying on the ground using obtained 3D point-cloud data from an on-board sensor, such as an RGB-D camera or a 3D LIDAR, on a mobile rescue robot. In this method, the obtained 3D point-cloud data is projected onto the detected ground plane, i.e. floor, within the point cloud. Then, this projected point cloud is converted into a grid-map that is used afterwards as an input for the algorithm to detect human body shapes. The proposed method is evaluated by performing detection of a human dummy, placed in different random positions and orientations, using an on-board RGB-D camera on a mobile rescue robot called ResQbot. To evaluate the robustness of the casualty detection method to different camera angles, the orientation of the camera is set to different angles. The experimental results show that using the point-cloud data from the on-board RGB-D camera, the proposed method successfully detects the casualty in all tested body positions and orientations relative to the on-board camera, as well as in all tested camera angles.