Communication-based UAV Swarm Missions

Unmanned aerial vehicles have developed rapidly in recent years due to technological advances. UAV technology can be applied to a wide range of applications in surveillance, rescue, agriculture and transport. The problems that can exist in these areas can be mitigated by combining clusters of drones with several technologies. For example, when a swarm of drones is under attack, it may not be able to obtain the position feedback provided by the Global Positioning System (GPS). This poses a new challenge for the UAV swarm to fulfill a specific mission. This thesis intends to use as few sensors as possible on the UAVs and to design the smallest possible information transfer between the UAVs to maintain the shape of the UAV formation in flight and to follow a predetermined trajectory. This thesis presents Extended Kalman Filter methods to navigate autonomously in a GPS-denied environment. The UAV formation control and distributed communication methods are also discussed and given in detail

Indoor collaborative positioning based on a multi-sensor and multi-user system

With recent developments in the Global Satellite Navigation Systems (GNSS), the applications and services of positioning and navigation have developed rapidly worldwide. Location-based services (LBS) have become a big application which provide position related services to the mass market. As LBS applications become more popular, positioning services and capacity are demanded to cover all types of environment with improved accuracy and reliability. While GNSS can provide promising positioning and navigation solutions in open outdoor environments, it does not work well when inside buildings, in tunnels or under canopy. Positioning in such difficult environments have been known as the indoor positioning problem. Although the problem has been looked into for more than a decade, there currently no solution that can compare to the performance of GNSS in outdoor environments. This thesis introduces a collaborative indoor positioning solution based on particle filtering which integrates multiple sensors, e.g. inertial sensors, Wi-Fi signals, map information etc., and multiple local users which provide peer-to-peer (P2P) relative ranging measurements. This solution addresses three current problems of indoor positioning. First of all is the positioning accuracy, which is limited by the availability of sensors and the quality of their signals in the environment. The collaborative positioning solution integrates a number of sensors and users to provide better measurements and restrict measurement error from growing. Secondly, the reliability of the positioning solutions, which is also affected by the signal quality. The unpredictable behaviour of positioning signals and data could lead to many uncertainties in the final positioning result. A successful positioning system should be able to deal with changes in the signal and provide reliable positioning results using different data processing strategies. Thirdly, the continuity and robustness of positioning solutions. While the indoor environment can be very different from one another, hence applicable signals are also different, the positioning solution should take into account the uniqueness of different situations and provide continuous positioning result regardless of the changing datWith recent developments in the Global Satellite Navigation Systems (GNSS), the applications and services of positioning and navigation have developed rapidly worldwide. Location based services (LBS) have become a big application which provide position related services to the mass market. As LBS applications become more popular, positioning services and capacity are demanded to cover all types of environment with improved accuracy and reliability. While GNSS can provide promising positioning and navigation solutions in open outdoor environments, it does not work well when inside buildings, in tunnels or under canopy. Positioning in such difficult environments have been known as the indoor positioning problem. Although the problem has been looked into for more than a decade, there currently no solution that can compare to the performance of GNSS in outdoor environments. This thesis introduces a collaborative indoor positioning solution based on particle filtering which integrates multiple sensors, e.g. inertial sensors, Wi-Fi signals, map information etc., and multiple local users which provide peer-to-peer (P2P) relative ranging measurements. This solution addresses three current problems of indoor positioning. First of all is the positioning accuracy, which is limited by the availability of sensors and the quality of their signals in the environment. The collaborative positioning solution integrates a number of sensors and users to provide better measurements and restrict measurement error from growing. Secondly, the reliability of the positioning solutions, which is also affected by the signal quality. The unpredictable behaviour of positioning signals and data could lead to many uncertainties in the final positioning result. A successful positioning system should be able to deal with changes in the signal and provide reliable positioning results using different data processing strategies. Thirdly, the continuity and robustness of positioning solutions. While the indoor environment can be very different from one another, hence applicable signals are also different, the positioning solution should take into account the uniqueness of different situations and provide continuous positioning result regardless of the changing data. The collaborative positioning aspect is examined from three aspects, the network geometry, the network size and the P2P ranging measurement accuracy. Both theoretical and experimental results indicate that a collaborative network with a low dilution of precision (DOP) value could achieve better positioning accuracy. While increasing sensors and users will reduce DOP, it will also increase computation load which is already a disadvantage of particle filters. The most effective collaborative positioning network size is thus identified and applied. While the positioning system measurement error is constrained by the accuracy of the P2P ranging constraint, the work in this thesis shows that even low accuracy measurements can provide effective constraint as long as the system is able to identify the different qualities of the measurements. The proposed collaborative positioning algorithm constrains both inertial measurements and Wi-Fi fingerprinting to enhance the stability and accuracy of positioning result, achieving metre-level accuracy. The application of collaborative constraints also eliminate the requirement for indoor map matching which had been a very useful tool in particle filters for indoor positioning purposes. The wall constraint can be replaced flexibly and easily with relative constraint. Simulations and indoor trials are carried out to evaluate the algorithms. Results indicate that metre-level positioning accuracy could be achieved and collaborative positioning also gives the system more flexibility to adapt to different situations when Wi-Fi or collaborative ranging is unavailable. The collaborative positioning aspect is examined from three aspects, the network geometry, the network size and the P2P ranging measurement accuracy. Both theoretical and experimental results indicate that a collaborative network with a low dilution of precision (DOP) value could achieve better positioning accuracy. While increasing sensors and users will reduce DOP, it will also increase computation load which is already a disadvantage of particle filters. The most effective collaborative positioning network size is thus identified and applied. While the positioning system measurement error is constrained by the accuracy of the P2P ranging constraint, the work in this thesis shows that even low accuracy measurements can provide effective constraint as long as the system is able to identify the different qualities of the measurements. The proposed collaborative positioning algorithm constrains both inertial measurements and Wi-Fi fingerprinting to enhance the stability and accuracy of positioning result, achieving metre-level accuracy. The application of collaborative constraints also eliminate the requirement for indoor map matching which had been a very useful tool in particle filters for indoor positioning purposes. The wall constraint can be replaced flexibly and easily with relative constraint. Simulations and indoor trials are carried out to evaluate the algorithms. Results indicate that metre-level positioning accuracy could be achieved and collaborative positioning also gives the system more flexibility to adapt to different situations when Wi-Fi or collaborative ranging is unavailable

Indoor collaborative positioning based on a multi-sensor and multi-user system

With recent developments in the Global Satellite Navigation Systems (GNSS), the applications and services of positioning and navigation have developed rapidly worldwide. Location-based services (LBS) have become a big application which provide position related services to the mass market. As LBS applications become more popular, positioning services and capacity are demanded to cover all types of environment with improved accuracy and reliability. While GNSS can provide promising positioning and navigation solutions in open outdoor environments, it does not work well when inside buildings, in tunnels or under canopy. Positioning in such difficult environments have been known as the indoor positioning problem. Although the problem has been looked into for more than a decade, there currently no solution that can compare to the performance of GNSS in outdoor environments. This thesis introduces a collaborative indoor positioning solution based on particle filtering which integrates multiple sensors, e.g. inertial sensors, Wi-Fi signals, map information etc., and multiple local users which provide peer-to-peer (P2P) relative ranging measurements. This solution addresses three current problems of indoor positioning. First of all is the positioning accuracy, which is limited by the availability of sensors and the quality of their signals in the environment. The collaborative positioning solution integrates a number of sensors and users to provide better measurements and restrict measurement error from growing. Secondly, the reliability of the positioning solutions, which is also affected by the signal quality. The unpredictable behaviour of positioning signals and data could lead to many uncertainties in the final positioning result. A successful positioning system should be able to deal with changes in the signal and provide reliable positioning results using different data processing strategies. Thirdly, the continuity and robustness of positioning solutions. While the indoor environment can be very different from one another, hence applicable signals are also different, the positioning solution should take into account the uniqueness of different situations and provide continuous positioning result regardless of the changing datWith recent developments in the Global Satellite Navigation Systems (GNSS), the applications and services of positioning and navigation have developed rapidly worldwide. Location based services (LBS) have become a big application which provide position related services to the mass market. As LBS applications become more popular, positioning services and capacity are demanded to cover all types of environment with improved accuracy and reliability. While GNSS can provide promising positioning and navigation solutions in open outdoor environments, it does not work well when inside buildings, in tunnels or under canopy. Positioning in such difficult environments have been known as the indoor positioning problem. Although the problem has been looked into for more than a decade, there currently no solution that can compare to the performance of GNSS in outdoor environments. This thesis introduces a collaborative indoor positioning solution based on particle filtering which integrates multiple sensors, e.g. inertial sensors, Wi-Fi signals, map information etc., and multiple local users which provide peer-to-peer (P2P) relative ranging measurements. This solution addresses three current problems of indoor positioning. First of all is the positioning accuracy, which is limited by the availability of sensors and the quality of their signals in the environment. The collaborative positioning solution integrates a number of sensors and users to provide better measurements and restrict measurement error from growing. Secondly, the reliability of the positioning solutions, which is also affected by the signal quality. The unpredictable behaviour of positioning signals and data could lead to many uncertainties in the final positioning result. A successful positioning system should be able to deal with changes in the signal and provide reliable positioning results using different data processing strategies. Thirdly, the continuity and robustness of positioning solutions. While the indoor environment can be very different from one another, hence applicable signals are also different, the positioning solution should take into account the uniqueness of different situations and provide continuous positioning result regardless of the changing data. The collaborative positioning aspect is examined from three aspects, the network geometry, the network size and the P2P ranging measurement accuracy. Both theoretical and experimental results indicate that a collaborative network with a low dilution of precision (DOP) value could achieve better positioning accuracy. While increasing sensors and users will reduce DOP, it will also increase computation load which is already a disadvantage of particle filters. The most effective collaborative positioning network size is thus identified and applied. While the positioning system measurement error is constrained by the accuracy of the P2P ranging constraint, the work in this thesis shows that even low accuracy measurements can provide effective constraint as long as the system is able to identify the different qualities of the measurements. The proposed collaborative positioning algorithm constrains both inertial measurements and Wi-Fi fingerprinting to enhance the stability and accuracy of positioning result, achieving metre-level accuracy. The application of collaborative constraints also eliminate the requirement for indoor map matching which had been a very useful tool in particle filters for indoor positioning purposes. The wall constraint can be replaced flexibly and easily with relative constraint. Simulations and indoor trials are carried out to evaluate the algorithms. Results indicate that metre-level positioning accuracy could be achieved and collaborative positioning also gives the system more flexibility to adapt to different situations when Wi-Fi or collaborative ranging is unavailable. The collaborative positioning aspect is examined from three aspects, the network geometry, the network size and the P2P ranging measurement accuracy. Both theoretical and experimental results indicate that a collaborative network with a low dilution of precision (DOP) value could achieve better positioning accuracy. While increasing sensors and users will reduce DOP, it will also increase computation load which is already a disadvantage of particle filters. The most effective collaborative positioning network size is thus identified and applied. While the positioning system measurement error is constrained by the accuracy of the P2P ranging constraint, the work in this thesis shows that even low accuracy measurements can provide effective constraint as long as the system is able to identify the different qualities of the measurements. The proposed collaborative positioning algorithm constrains both inertial measurements and Wi-Fi fingerprinting to enhance the stability and accuracy of positioning result, achieving metre-level accuracy. The application of collaborative constraints also eliminate the requirement for indoor map matching which had been a very useful tool in particle filters for indoor positioning purposes. The wall constraint can be replaced flexibly and easily with relative constraint. Simulations and indoor trials are carried out to evaluate the algorithms. Results indicate that metre-level positioning accuracy could be achieved and collaborative positioning also gives the system more flexibility to adapt to different situations when Wi-Fi or collaborative ranging is unavailable

Visual-Inertial first responder localisation in large-scale indoor training environments.

Accurately and reliably determining the position and heading of first responders undertaking training exercises can provide valuable insights into their situational awareness and give a larger context to the decisions made. Measuring first responder movement, however, requires an accurate and portable localisation system. Training exercises of- ten take place in large-scale indoor environments with limited power infrastructure to support localisation. Indoor positioning technologies that use radio or sound waves for localisation require an extensive network of transmitters or receivers to be installed within the environment to ensure reliable coverage. These technologies also need power sources to operate, making their use impractical for this application. Inertial sensors are infrastructure independent, low cost, and low power positioning devices which are attached to the person or object being tracked, but their localisation accuracy deteriorates over long-term tracking due to intrinsic biases and sensor noise. This thesis investigates how inertial sensor tracking can be improved by providing correction from a visual sensor that uses passive infrastructure (fiducial markers) to calculate accurate position and heading values. Even though using a visual sensor increase the accuracy of the localisation system, combining them with inertial sensors is not trivial, especially when mounted on different parts of the human body and going through different motion dynamics. Additionally, visual sensors have higher energy consumption, requiring more batteries to be carried by the first responder. This thesis presents a novel sensor fusion approach by loosely coupling visual and inertial sensors to create a positioning system that accurately localises walking humans in largescale indoor environments. Experimental evaluation of the devised localisation system indicates sub-metre accuracy for a 250m long indoor trajectory. The thesis also proposes two methods to improve the energy efficiency of the localisation system. The first is a distance-based error correction approach which uses distance estimation from the foot-mounted inertial sensor to reduce the number of corrections required from the visual sensor. Results indicate a 70% decrease in energy consumption while maintaining submetre localisation accuracy. The second method is a motion type adaptive error correction approach, which uses the human walking motion type (forward, backward, or sideways) as an input to further optimise the energy efficiency of the localisation system by modulating the operation of the visual sensor. Results of this approach indicate a 25% reduction in the number of corrections required to keep submetre localisation accuracy. Overall, this thesis advances the state of the art by providing a sensor fusion solution for long-term submetre accurate localisation and methods to reduce the energy consumption, making it more practical for use in first responder training exercises

Cooperative Radio Communications for Green Smart Environments

The demand for mobile connectivity is continuously increasing, and by 2020 Mobile and Wireless Communications will serve not only very dense populations of mobile phones and nomadic computers, but also the expected multiplicity of devices and sensors located in machines, vehicles, health systems and city infrastructures. Future Mobile Networks are then faced with many new scenarios and use cases, which will load the networks with different data traffic patterns, in new or shared spectrum bands, creating new specific requirements. This book addresses both the techniques to model, analyse and optimise the radio links and transmission systems in such scenarios, together with the most advanced radio access, resource management and mobile networking technologies. This text summarises the work performed by more than 500 researchers from more than 120 institutions in Europe, America and Asia, from both academia and industries, within the framework of the COST IC1004 Action on "Cooperative Radio Communications for Green and Smart Environments". The book will have appeal to graduates and researchers in the Radio Communications area, and also to engineers working in the Wireless industry. Topics discussed in this book include: • Radio waves propagation phenomena in diverse urban, indoor, vehicular and body environments• Measurements, characterization, and modelling of radio channels beyond 4G networks• Key issues in Vehicle (V2X) communication• Wireless Body Area Networks, including specific Radio Channel Models for WBANs• Energy efficiency and resource management enhancements in Radio Access Networks• Definitions and models for the virtualised and cloud RAN architectures• Advances on feasible indoor localization and tracking techniques• Recent findings and innovations in antenna systems for communications• Physical Layer Network Coding for next generation wireless systems• Methods and techniques for MIMO Over the Air (OTA) testin

Cooperative Radio Communications for Green Smart Environments

The demand for mobile connectivity is continuously increasing, and by 2020 Mobile and Wireless Communications will serve not only very dense populations of mobile phones and nomadic computers, but also the expected multiplicity of devices and sensors located in machines, vehicles, health systems and city infrastructures. Future Mobile Networks are then faced with many new scenarios and use cases, which will load the networks with different data traffic patterns, in new or shared spectrum bands, creating new specific requirements. This book addresses both the techniques to model, analyse and optimise the radio links and transmission systems in such scenarios, together with the most advanced radio access, resource management and mobile networking technologies. This text summarises the work performed by more than 500 researchers from more than 120 institutions in Europe, America and Asia, from both academia and industries, within the framework of the COST IC1004 Action on "Cooperative Radio Communications for Green and Smart Environments". The book will have appeal to graduates and researchers in the Radio Communications area, and also to engineers working in the Wireless industry. Topics discussed in this book include: • Radio waves propagation phenomena in diverse urban, indoor, vehicular and body environments• Measurements, characterization, and modelling of radio channels beyond 4G networks• Key issues in Vehicle (V2X) communication• Wireless Body Area Networks, including specific Radio Channel Models for WBANs• Energy efficiency and resource management enhancements in Radio Access Networks• Definitions and models for the virtualised and cloud RAN architectures• Advances on feasible indoor localization and tracking techniques• Recent findings and innovations in antenna systems for communications• Physical Layer Network Coding for next generation wireless systems• Methods and techniques for MIMO Over the Air (OTA) testin

Off-line evaluation of indoor positioning systems in different scenarios: the experiences from IPIN 2020 competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.Track 3 organizers were supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie Grant 813278 (A-WEAR: A network for dynamic WEarable Applications with pRivacy constraints), MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE), INSIGNIA (MICINN ref. PTQ2018-009981), and REPNIN+ (MICINN, ref. TEC2017-90808-REDT). We would like to thanks the UJI’s Library managers and employees for their support while collecting the required datasets for Track 3. Track 5 organizers were supported by JST-OPERA Program, Japan, under Grant JPMJOP1612. Track 7 organizers were supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II. ” Team UMinho (Track 3) was supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope under Grant UIDB/00319/2020, and the Ph.D. Fellowship under Grant PD/BD/137401/2018. Team YAI (Track 3) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 109-2221-E-197-026. Team Indora (Track 3) was supported in part by the Slovak Grant Agency, Ministry of Education and Academy of Science, Slovakia, under Grant 1/0177/21, and in part by the Slovak Research and Development Agency under Contract APVV-15-0091. Team TJU (Track 3) was supported in part by the National Natural Science Foundation of China under Grant 61771338 and in part by the Tianjin Research Funding under Grant 18ZXRHSY00190. Team Next-Newbie Reckoners (Track 3) were supported by the Singapore Government through the Industry Alignment Fund—Industry Collaboration Projects Grant. This research was conducted at Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU). Team KawaguchiLab (Track 5) was supported by JSPS KAKENHI under Grant JP17H01762. Team WHU&AutoNavi (Track 6) was supported by the National Key Research and Development Program of China under Grant 2016YFB0502202. Team YAI (Tracks 6 and 7) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 110-2634-F-155-001

Off-Line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences From IPIN 2020 Competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements

Edge Artificial Intelligence for Real-Time Target Monitoring

The key enabling technology for the exponentially growing cellular communications sector is location-based services. The need for location-aware services has increased along with the number of wireless and mobile devices. Estimation problems, and particularly parameter estimation, have drawn a lot of interest because of its relevance and engineers' ongoing need for higher performance. As applications expanded, a lot of interest was generated in the accurate assessment of temporal and spatial properties. In the thesis, two different approaches to subject monitoring are thoroughly addressed. For military applications, medical tracking, industrial workers, and providing location-based services to the mobile user community, which is always growing, this kind of activity is crucial. In-depth consideration is given to the viability of applying the Angle of Arrival (AoA) and Receiver Signal Strength Indication (RSSI) localization algorithms in real-world situations. We presented two prospective systems, discussed them, and presented specific assessments and tests. These systems were put to the test in diverse contexts (e.g., indoor, outdoor, in water...). The findings showed the localization capability, but because of the low-cost antenna we employed, this method is only practical up to a distance of roughly 150 meters. Consequently, depending on the use-case, this method may or may not be advantageous. An estimation algorithm that enhances the performance of the AoA technique was implemented on an edge device. Another approach was also considered. Radar sensors have shown to be durable in inclement weather and bad lighting conditions. Frequency Modulated Continuous Wave (FMCW) radars are the most frequently employed among the several sorts of radar technologies for these kinds of applications. Actually, this is because they are low-cost and can simultaneously provide range and Doppler data. In comparison to pulse and Ultra Wide Band (UWB) radar sensors, they also need a lower sample rate and a lower peak to average ratio. The system employs a cutting-edge surveillance method based on widely available FMCW radar technology. The data processing approach is built on an ad hoc-chain of different blocks that transforms data, extract features, and make a classification decision before cancelling clutters and leakage using a frame subtraction technique, applying DL algorithms to Range-Doppler (RD) maps, and adding a peak to cluster assignment step before tracking targets. In conclusion, the FMCW radar and DL technique for the RD maps performed well together for indoor use-cases. The aforementioned tests used an edge device and Infineon Technologies' Position2Go FMCW radar tool-set

Using a mobile robot for hazardous substances detection in a factory environment

Dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáIndustries that work with toxic materials need extensive security protocols to avoid accidents. Instead of having fixed sensors, the concept of assembling the sensors on a mobile robot that performs the scanning through a defined path is cheaper, configurable and adaptable. This work describes a mobile robot, equipped with several gas sensors and LIDAR, that follows a trajectory based on waypoints, simulating a working Autonomous Guided Vehicle (AGV). At the same time, the robot keeps measuring for toxic gases. In other words, the robot follows the trajectory while the gas concentration is under a defined value. Otherwise, it starts the autonomous leakage search based on a search algorithm that allows to find the leakage position avoiding obstacles in real time. The proposed methodology is verified in simulation based on a model of the real robot. Therefore, three path plannings were developed and their performance compared. A Light Detection And Ranging (LIDAR) device was integrated with the path planning to propose an obstacle avoidance system with a dilation technique to enlarge the obstacles, thus, considering the robot’s dimensions. Moreover, if needed, the robot can be remotely operated with visual feedback. In addition, a controller was made for the robot. Gas sensors were embedded in the robot with Finite Impulse Response (FIR) filter to process the data. A low cost AGV was developed to compete in Festival Nacional de Robótica (Portuguese Robotics Open) 2019 - Gondomar, describing the robot’s control and software solution to the competition.As indústrias que trabalham com materiais tóxicos necessitam de extensos protocolos de segurança para evitar acidentes. Ao invés de ter sensores estáticos, o conceito de instalar sensores em um robô móvel que inspeciona através de um caminho definido é mais barato, configurável e adaptável. O presente trabalho descreve um robô móvel, equipado com vários sensores de gás e LIDAR, que percorre uma trajetória baseada em pontos de controle, simulando um AGV em trabalho. Em simultâneo são efetuadas medidas de gases tóxicos. Em outras palavras, o robô segue uma trajetória enquanto a concentração de gás está abaixo de um valor definido. Caso contrário, inicia uma busca autônoma de vazamento de gás com um algoritmo de busca que permite achar a posição do gás evitando os obstáculos em tempo real. A metodologia proposta é verificada em simulação. Três algoritmos de planejamento de caminho foram desenvolvidos e suas performances comparadas. Um LIDAR foi integrado com o planejamento de caminho para propôr um sistema de evitar obstáculos. Além disso, o robô pode ser operado remotamente com auxílio visual. Foi feito um controlador para o robô. Sensores de gás foram embarcados no robô com um filtro de resposta ao impulso finita para processar as informações. Um veículo guiado automático de baixo custo foi desenvolvido para competir no Festival Nacional de Robótica 2019 - Gondomar. O controle do veículo foi descrito com o programa de solução para a competição