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Studying and analysis of path following algorithms for robots and its correction for obstacle avoidance

دراسة خوارزميات تتبع مسار عربة و تحليله و تصحيحه لتجنب العوائق

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 Publication date 2014
and research's language is العربية
 Created by Shamra Editor




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In this research a review of methods needed to make a vehicle follow a predefined path has been done where the vehicle can follow the path and return to it if any deviation happened. The following Algorithms have been applied to follow the path: 1- Follow The Carrot [1] Algorithm 2- Pure Pursuit [2] Algorithm 3- Follow The Past [3] Algorithm The implementation of these algorithms has been done by using statistical analysis software (MATLAB) to make a robotic vehicle movement simulator that uses algorithms to follow a predefined path (recorded path). We have found as a result that the (Follow the Carrot) algorithm is simple for understanding and applying, on the other hand it causes larger errors in position and larger deviation from the path. Also in the (Follow the Carrot) algorithm, the vehicle tends to take short cuts and moves directly towards the goal point instead of moving on path curves. Pure Pursuit algorithm also suffers from the same problems, but not in the same critical way, where we can get better proportion results .Whereas the (Follow the Past) algorithm achieves a perfect path tracking for applying certain conditions and study parameters.


Artificial intelligence review:
Research summary
تتناول هذه الدراسة تحليل خوارزميات تتبع مسار عربة روبوتية وتصحيح مسارها لتجنب العوائق. تم استخدام ثلاث خوارزميات رئيسية: خوارزمية ملاحقة الجزرة (Follow The Carrot)، خوارزمية الملاحقة الصافية (Pure Pursuit)، وخوارزمية ملاحقة الماضي (Follow The Past). تم استخدام برنامج MATLAB لمحاكاة حركة العربة الروبوتية وتقييم أداء هذه الخوارزميات. أظهرت النتائج أن خوارزمية ملاحقة الجزرة تتميز بالبساطة ولكنها تعاني من أخطاء كبيرة في الموقع والانحراف عن المسار، بينما كانت خوارزمية الملاحقة الصافية أقل حدة في هذه المشاكل. أما خوارزمية ملاحقة الماضي فقد أظهرت أداءً مثالياً في تتبع المسار. تم دمج خوارزميات تجنب العوائق مع خوارزميات تتبع المسار لتحسين الأداء في بيئات معقدة. أظهرت الدراسة أن خوارزمية ملاحقة الماضي هي الأكثر فعالية في تتبع المسار بدقة عالية، بينما تعاني الخوارزميات الأخرى من مشاكل في الانحراف والاهتزاز حول المسار. تم تقديم توصيات لتحسين المحاكاة في الدراسات المستقبلية، مثل دراسة تأثير الضجيج على تحديد موقع العربة واستخدام لغات برمجة أخرى لتحسين الأداء.
Critical review
دراسة نقدية: على الرغم من أن البحث قدم تحليلاً شاملاً لخوارزميات تتبع المسار وتصحيحها، إلا أنه كان يمكن تحسينه من خلال تضمين تجارب ميدانية حقيقية بدلاً من الاعتماد فقط على المحاكاة باستخدام MATLAB. كما أن الدراسة لم تأخذ في الاعتبار تأثير الضجيج البيئي على أداء الخوارزميات، وهو عامل مهم يجب دراسته في المستقبل. بالإضافة إلى ذلك، كان من الممكن تحسين البحث من خلال استخدام لغات برمجة أخرى أكثر كفاءة لتحسين أداء المحاكاة. ومع ذلك، فإن البحث قدم مساهمة قيمة في مجال الروبوتات الذاتية التحكم وتجنب العوائق، ويعد خطوة مهمة نحو تحسين تقنيات تتبع المسار في البيئات المعقدة.
Questions related to the research
  1. ما هي الخوارزميات الثلاث التي تم دراستها في البحث لتتبع مسار العربة الروبوتية؟

    الخوارزميات الثلاث هي: خوارزمية ملاحقة الجزرة (Follow The Carrot)، خوارزمية الملاحقة الصافية (Pure Pursuit)، وخوارزمية ملاحقة الماضي (Follow The Past).

  2. ما هي الأداة البرمجية التي تم استخدامها لمحاكاة حركة العربة الروبوتية؟

    تم استخدام برنامج MATLAB لمحاكاة حركة العربة الروبوتية.

  3. ما هي المشاكل الرئيسية التي تعاني منها خوارزمية ملاحقة الجزرة؟

    تعاني خوارزمية ملاحقة الجزرة من أخطاء كبيرة في الموقع والانحراف عن المسار، بالإضافة إلى ميل العربة إلى أخذ مسارات مختصرة بدلاً من اتباع الانحناءات في المسار.

  4. ما هي التوصيات التي قدمها البحث لتحسين المحاكاة في الدراسات المستقبلية؟

    قدم البحث توصيات لتحسين المحاكاة في الدراسات المستقبلية، مثل دراسة تأثير الضجيج على تحديد موقع العربة واستخدام لغات برمجة أخرى لتحسين أداء المحاكاة.


References used
MATTHEW J.BARTON. Controller Development and Implementation for Path Planning and Following in an Autonomous Urban Vehicle. Undergraduate thesis, University of Sydney, November 2001
R.CRAIG COULTER. Implementation of the Pure Pursuit Path Tracking Algorithms. Technical Report CMU-RI-TR-92-01, Robotic Institute, Carnegie Mellon University, Pittsburgh، PA, January 1992
THOMAS HELLSTROM, OLA RINGDAHL. Follow the Past- a Path Tracking Algorithm for Autonomous Forest Vehicle. SE-901 87, University of Ume، Sweden, April 2004
THOMAS HELLSTROM. Autonomous Navigation for Forest Machines. Pre-study, University of Umea, Aug 200
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