DEVELOPMENT OF AN ARDUINO-BASED OBSTACLE AVOIDANCE ROBOTIC SYSTEM FOR AN UNMANNED VEHICLE

The use of autonomous systems in the world to perform relevant and delicate task is fast growing. However, its application in various fields cannot be over emphasized. This paper presents an obstacle detection and avoidance system for an unmanned Lawnmower. The system consists of two (Infrared and Ultrasonic) sensors, an Arduino microcontroller and a gear DC motor. The ultrasonic and infrared sensors are implemented to detect obstacles on the robot’s path by sending signals to an interfaced microcontroller. The micro-controller redirects the robot to move in an alternate direction by actuating the motorsin order to avoid the detected obstacle. The performance evaluation of the system indicates an accuracy of 85% and 0.15 probability of failure respectively. In conclusion, an obstacle detection circuit was successfully implemented using infrared and ultrasonic sensors modules which were placed at the front of the robot to throw both light and sound waves at any obstacle and when a reflection is received, a low output is sent to the Arduino microcontroller which interprets the output and makes the robot to stop

Development of a bio-inspired vision system for mobile micro-robots

In this paper, we present a new bio-inspired vision system for mobile micro-robots. The processing method takes inspiration from vision of locusts in detecting the fast approaching objects. Research suggested that locusts use wide field visual neuron called the lobula giant movement detector to respond to imminent collisions. We employed the locusts' vision mechanism to motion control of a mobile robot. The selected image processing method is implemented on a developed extension module using a low-cost and fast ARM processor. The vision module is placed on top of a micro-robot to control its trajectory and to avoid obstacles. The observed results from several performed experiments demonstrated that the developed extension module and the inspired vision system are feasible to employ as a vision module for obstacle avoidance and motion control

A phased array antenna system of a millimeter-wave FMCW radar for blind spot detection of mobile robots

Mobile robots have been extensively used in manufacturing plants for inter-logistic transportation in recent years. This paper covers a phased array antenna design for a millimeter wave radar system to improve lidar-based navigation systems' safety and environmental consciousness. The K-band phased array antenna, when integrated with 24 GHz Frequency-Modulated-Continuous-Wave (FMCW) radar, not only enhances the accuracy of the 2-D Area Scanning lidar system but also helps with the safe operation of the vehicle. The safety improvement is made by covering blind spots to mitigate collision risks during the rotations. The paper first reviews the system-level details of the 2D lidar sensor and shows the blind spots when integrated into a Mobile Robot prototype. Then continues with the inclusion of an FMCW Low-Speed Ramp radar system and discusses the design details of the proposed K-band antenna array, which will be integrated with a radar sensor

SLAM research for port AGV based on 2D LIDAR

With the increase in international trade, the transshipment of goods at international container ports is very busy. The AGV (Automated Guided Vehicle) has been used as a new generation of automated container horizontal transport equipment. The AGV is an automated unmanned vehicle that can work 24 hours a day, increasing productivity and reducing labor costs compared to using container trucks. The ability to obtain information about the surrounding environment is a prerequisite for the AGV to automatically complete tasks in the port area. At present, the method of AGV based on RFID tag positioning and navigation has a problem of excessive cost. This dissertation has carried out a research on applying light detection and ranging (LIDAR) simultaneous localization and mapping (SLAM) technology to port AGV. In this master's thesis, a mobile test platform based on a laser range finder is developed to scan 360-degree environmental information (distance and angle) centered on the LIDAR and upload the information to a real-time database to generate surrounding environmental maps, and the obstacle avoidance strategy was developed based on the acquired information. The effectiveness of the platform was verified by the experiments from multiple scenarios. Then based on the first platform, another experimental platform with encoder and IMU sensor was developed. In this platform, the functionality of SLAM is enabled by the GMapping algorithm and the installation of the encoder and IMU sensor. Based on the established environment SLAM map, the path planning and obstacle avoidance functions of the platform were realized.Com o aumento do comércio internacional, o transbordo de mercadorias em portos internacionais de contentores é muito movimentado. O AGV (“Automated Guided Vehicle”) foi usado como uma nova geração de equipamentos para transporte horizontal de contentores de forma automatizada. O AGV é um veículo não tripulado automatizado que pode funcionar 24 horas por dia, aumentando a produtividade e reduzindo os custos de mão-de-obra em comparação com o uso de camiões porta-contentores. A capacidade de obter informações sobre o ambiente circundante é um pré-requisito para o AGV concluir automaticamente tarefas na área portuária. Atualmente, o método de AGV baseado no posicionamento e navegação de etiquetas RFID apresenta um problema de custo excessivo. Nesta dissertação foi realizada uma pesquisa sobre a aplicação da tecnologia LIDAR de localização e mapeamento simultâneo (SLAM) num AGV. Uma plataforma de teste móvel baseada num telémetro a laser é desenvolvida para examinar o ambiente em redor em 360 graus (distância e ângulo), centrado no LIDAR, e fazer upload da informação para uma base de dados em tempo real para gerar um mapa do ambiente em redor. Uma estratégia de prevenção de obstáculos foi também desenvolvida com base nas informações adquiridas. A eficácia da plataforma foi verificada através da realização de testes com vários cenários e obstáculos. Por fim, com base na primeira plataforma, uma outra plataforma experimental com codificador e sensor IMU foi também desenvolvida. Nesta plataforma, a funcionalidade do SLAM é ativada pelo algoritmo GMapping e pela instalação do codificador e do sensor IMU. Com base no estabelecimento do ambiente circundante SLAM, foram realizadas as funções de planeamento de trajetória e prevenção de obstáculos pela plataforma

School Logo Cleveland State University Logo Title Evolutionary Optimization for Safe Navigation of an Autonomous Robot in Cluttered Dynamic Unknown Environments

We present a path planning approach based on probabilistic methods for a robot to navigate in a cluttered, dynamic, unknown environment. There are dynamic obstacles moving around and static obstacles located in the map. The robot does not have any prior information about them but should be able to navigate through the map beginning from a known starting point and safely ending at a known target point. The only information the robot has is the location of the starting point and the target point and it uses sensory information to collect information about its surroundings. Our method is compared to the D* Lite algorithm and results are presented. In the last section, the parameters of the robot are optimized using biogeography-based optimization (BBO). This is an efficient multivariable optimizer and it is shown that the results of optimization achieve significant improvement in robot navigation performance. In this thesis, we show that using evolutionary optimization methods like BBO can reduce the risk of collision and the navigation time by about 25% each. The resulting risk of collision indicates safe navigation by the robot which leads to the conclusion that this is a feasible method for real-world robots

School Logo Cleveland State University Logo Title Evolutionary Optimization for Safe Navigation of an Autonomous Robot in Cluttered Dynamic Unknown Environments

We present a path planning approach based on probabilistic methods for a robot to navigate in a cluttered, dynamic, unknown environment. There are dynamic obstacles moving around and static obstacles located in the map. The robot does not have any prior information about them but should be able to navigate through the map beginning from a known starting point and safely ending at a known target point. The only information the robot has is the location of the starting point and the target point and it uses sensory information to collect information about its surroundings. Our method is compared to the D* Lite algorithm and results are presented. In the last section, the parameters of the robot are optimized using biogeography-based optimization (BBO). This is an efficient multivariable optimizer and it is shown that the results of optimization achieve significant improvement in robot navigation performance. In this thesis, we show that using evolutionary optimization methods like BBO can reduce the risk of collision and the navigation time by about 25% each. The resulting risk of collision indicates safe navigation by the robot which leads to the conclusion that this is a feasible method for real-world robots

Evolutionary Optimization for Safe Navigation of an Autonomous Robot in Cluttered Dynamic Unknown Environments

We present a path planning approach based on probabilistic methods for a robot to navigate in a cluttered, dynamic, unknown environment. There are dynamic obstacles moving around and static obstacles located in the map. The robot does not have any prior information about them but should be able to navigate through the map beginning from a known starting point and safely ending at a known target point. The only information the robot has is the location of the starting point and the target point and it uses sensory information to collect information about its surroundings. Our method is compared to the D* Lite algorithm and results are presented. In the last section, the parameters of the robot are optimized using biogeography-based optimization (BBO). This is an efficient multivariable optimizer and it is shown that the results of optimization achieve significant improvement in robot navigation performance. In this thesis, we show that using evolutionary optimization methods like BBO can reduce the risk of collision and the navigation time by about 25% each. The resulting risk of collision indicates safe navigation by the robot which leads to the conclusion that this is a feasible method for real-world robots