Obstacle detection using ultrasonic sensor for a mobile robot

There have been a number of successful attempts in designing obstacle avoiding robots. These works differ by selection of sensors, path mapping process and the algorithms applied to set the operational parameters. In this paper we present a los cost ultrasonic distance sensor for obstacle avoidance for mobile robot navigation. The system is implemented using microcontroller Arduino Uno, a Wi-Fi module, an Arduino motor shield driver which controls the robot through the geared dc motors. The system showed good performance under various lighting conditions. Experimental results with varied positions of obstacle show the flexibility of the robot to avoid it and have shown a decent performance in our laboratory. The robot is additionally ready to acknowledge victims before it, the sensing element system is extremely low-cost as a result of it solely uses one distance sensing element

2019 8th International Conference on Mechatronics and Control Engineering

There have been a number of successful attempts in designing obstacle avoiding robots. These works differ by selection of sensors, path mapping process and the algorithms applied to set the operational parameters. In this paper we present a los cost ultrasonic distance sensor for obstacle avoidance for mobile robot navigation. The system is implemented using microcontroller Arduino Uno, a Wi-Fi module, an Arduino motor shield driver which controls the robot through the geared dc motors. The system showed good performance under various lighting conditions. Experimental results with varied positions of obstacle show the flexibility of the robot to avoid it and have shown a decent performance in our laboratory. The robot is additionally ready to acknowledge victims before it, the sensing element system is extremely low-cost as a result of it solely uses one distance sensing element

Design and implementation of advanced sensor systems for smart robotic wheelchairs: A review

Smart robotic wheelchairs have emerged as promising assistive devices to enhance mobility and independence for individuals with mobility impairments. The successful integration of advanced sensor systems plays a critical role in improving the functionality and safety of these wheelchairs. This paper presents a comprehensive review of the design and implementation of advanced sensor systems for smart robotic wheelchairs. Through an extensive literature review, the limitations of existing sensor technologies are identified, and the potential of advanced sensors is explored. Vision-based sensors, range and proximity sensors, force and pressure sensors, inertial sensors, and environmental sensors are discussed in detail. Furthermore, this review highlights the design considerations, hardware components, software development, and calibration procedures involved in implementing advanced sensor systems. Evaluation and performance analysis metrics are discussed to assess the effectiveness of the sensor systems. The research findings indicate that advanced sensor systems have the potential to significantly enhance the functionality and safety of smart robotic wheelchairs. However, challenges such as sensor integration, data fusion, and user feedback must be addressed. This review paper concludes by discussing the implications of advanced sensor systems in improving wheelchair functionality and user experience, and proposes future directions for research in this domain

Analisis Keunikan Fitur Cwt Sinyal Eeg Untuk Pembuatan Lima Indikator Pengendalian Kursi Roda BCI

Penelitian ini dilakukan dengan tujuan untuk membuat lima indikator pengendalian kursi roda BCI berdasarkan fitur yang diekstraksi dari sinyal elektroensefalogram (EEG). Sinyal EEG didekomposisi menggunakan metode continuous wavelet transform (CWT). Nilai rata-rata absolut dan standar deviasi dari sinyal yang telah didekomposisi tersebut digunakan sebagai fitur. Fitur hasil ekstraksi kemudian dianalisis keunikannya menggunakan metode Friedman. Untuk mendekati sifat alami fitur sinyal EEG yang nonlinier, metode support vector machine (SVM) dengan kernel radial basis function (RBF) digunakan untuk membuat indikator pengendalian kursi roda BCI berdasarkan fitur sinyal EEG yang paling unik. Hasil penelitian ini menunjukkan bahwa metode yang diusulkan dapat mengukur tingkat keunikan fitur CWT sinyal EEG. Dari penelitian penentuan keunikan fitur CWT dapat diperoleh lima indikator pengendalian untuk kursi roda BCI yang didasarkan pada sinyal EEG dari Neurosky MW001. Akan tetapi, akurasi kelima indikator tersebut belum dapat digunakan sebagai indikator kontrol untuk aktuator kursi roda BCI. Hal ini disebabkan oleh tingkat kepercayaan rata-rata indikator tersebut masih di bawah 60%, sedangkan untuk indikator yang berpasangan masih di bawah 70%