Multi-modal mobile sensor data fusion for autonomous robot mapping problem دمج بيانات المستشعر المتنقل متعدد الوسائط لمشكلة رسم خرائط الروبوت المستقل Multi-modal mobile sensor data fusion for autonomous robot mapping problem Multi-modal mobile sensor data fusion for autonomous robot mapping problem Multimodal móvil sensor data fusion for autonomous robot mapping problem

Perception is the first step for a mobile robot to perform any task and for it to gain perception mobile robots use sensors to measure the states which represent the surrounding environment. Sensors measurements are always combined with some sort of uncertainty and noise. Which can make the system very unstable and unreliable. In order to get better readings we can always use better types of sensors where we come to a trade off between price and quality. And that's why our proposed approach to solve this problem was to use data fusion techniques to eliminate the noise and reduce the uncertainty in the readings. The topic of data fusion has been under extensive research in the past decade many approaches had been suggested and yet the research on data fusion is increasing and this because of its importance and applications. This study discuss the use of probabilistic data fusion techniques to reduce the uncertainty and eliminate the noise of the measurements from range finder active sensors to improve the task of mapping for mobile robots. The data fusion methods used were Kalman filter and Bayes filter. الإدراك هو الخطوة الأولى للروبوت المتنقل لأداء أي مهمة ولكي يكتسب الإدراك، تستخدم الروبوتات المتنقلة أجهزة الاستشعار لقياس الحالات التي تمثل البيئة المحيطة. يتم دائمًا دمج قياسات المستشعرات مع نوع من عدم اليقين والضوضاء. مما قد يجعل النظام غير مستقر وغير موثوق به. من أجل الحصول على قراءات أفضل، يمكننا دائمًا استخدام أنواع أفضل من أجهزة الاستشعار حيث نصل إلى المفاضلة بين السعر والجودة. ولهذاالسبب كان نهجنا المقترح لحل هذه المشكلة هو استخدام تقنيات دمج البيانات للقضاء على الضوضاء وتقليل عدم اليقين في القراءات. كان موضوع دمج البيانات قيد البحث المكثف في العقد الماضي. تم اقتراح العديد من الأساليب ومع ذلك فإن البحث في دمج البيانات آخذ في الازدياد وهذا بسبب أهميته وتطبيقاته. تناقش هذه الدراسة استخدام تقنيات دمج البيانات الاحتمالية للحد من عدم اليقين والقضاء على ضوضاء القياسات من أجهزة الاستشعار النشطة للكشف عن المدى لتحسين مهمة رسم الخرائط للروبوتات المتنقلة. كانت طرق دمج البيانات المستخدمة هي مرشح كالمان ومرشح بايز. Perception is the first step for a mobile robot to perform any task and for it to gain perception mobile robots use sensors to measure the states which represent the surrounding environment. Sensors measurements are always combined with some sort of uncertainty and noise. Which can make the system very unstable and unreliable. In order to get better readings we can always use better types of sensors where we come to trade off between price and quality. And that's why our proposed approach to solving this problem was to use data fusion techniques to eliminate noise and reduce uncertainty in the readings. The topic of data fusion has been under extensive research in the past decade. Many approaches have been suggested and yet the research on data fusion is increasing and this because of its importance and applications. This study discusses the use of probabilistic data fusion techniques to reduce uncertainty and eliminate the noise of measurements from range finder active sensors to improve the task of mapping for mobile robots. The data fusion methods used were Kalman filter and Bayes filter. Perception is the first step for a mobile robot to perform any task and for it to gain perception mobile robots use sensors to measure the states which represent the surrounding environment. Sensors measurements are always combined with some sort of uncertainty and noise. Which can make the system very unstable and unreliable. In order to get better readings we can always use better types of sensors where we come to a trade off between price and quality. And that's why our proposed approach to solve this problem was to use data fusion techniques to eliminate the noise and reduce the uncertainty in the readings. The topic of data fusion has been under extensive research in the past decade many approaches had been suggested and yet the research on data fusion is increasing and this because of its importance and applications. This study discuss the use of probabilistic data fusion techniques to reduce the uncertainty and eliminate the noise of the measurements from range finder active sensors to improve the task of mapping for mobile robots. The data fusion methods used were Kalman filter and Bayes filter. Perception is the first step for a mobile robot to perform any task and for it to gain perception mobile robots use sensors to measure the states which represent the surrounding environment. Sensors measurements are always combined with some sort of uncertainty and noise. Which can make the system very unstable and unreliable. In order to get better readings we can always use better types of sensors where we come to a trade off between price and quality. And that's why our proposed approach to solve this problem was to use data fusion techniques to eliminate the noise and reduce the uncertainty in the readings. The topic of data fusion has been under extensive research in the past decade many approaches had been suggested and yet the research on data fusion is increasing and this because of its importance and applications. This study discuss the use of probabilistic data fusion techniques to reduce the uncertainty and eliminate the noise of the measurements from range finder active sensors to improve the task of mapping for mobile robots. The data fusion methods used were Kalman filter and Bayes filter.