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Mobile Robot Motion Planning using Bug Algorithms

تخطيط حركة روبوت متنقل باستخدام خوارزميات BUG

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 Publication date 2017
and research's language is العربية
 Created by Shamra Editor




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Motion Planning is an important and potential issue in Robotics, because it gives the robot the ability to reach its target automatically with collision free, which increase the robot performance and reduce its operational cost. The robot motion planning is commonly divide into two approaches: finding the appropriate path and making the robot tracking this path until it reaches its goal. This research depends on one of the most common obstacles avoidance path planningtechniques (Bug algorithms) to find global path of robot. And make the Boe-Bot robot (differential drive robot) tracking the paths generated by these algorithms in a certain environment.


Artificial intelligence review:
Research summary
تناول البحث موضوع تخطيط حركة الروبوتات المتنقلة باستخدام خوارزميات Bug، وهي من أشهر الخوارزميات المستخدمة لتجنب العوائق. يهدف البحث إلى تحسين أداء الروبوتات وتقليل تكاليف التشغيل من خلال تمكين الروبوت من الوصول إلى هدفه بشكل آلي ودون اصطدام بالعوائق. تم تطبيق هذه الخوارزميات على روبوت Boe-Bot التفاضلي القيادة واختبارها في بيئة معينة. يتضمن البحث شرحاً مفصلاً لخوارزميات Bug1 وBug2 وTangentBug، بالإضافة إلى كيفية تنفيذها وتقييم أدائها. أظهرت النتائج أن خوارزمية Bug1 توفر مساراً أقصر، بينما خوارزمية Bug2 تعتبر أسرع في الوصول إلى الهدف. كما تم استخدام تقنية التنعيم لتحسين مسارات الروبوت وتقليل التعرجات الصغيرة التي قد تؤدي إلى اصطدام الروبوت بالعوائق.
Critical review
دراسة نقدية: يعد هذا البحث مهماً في مجال تخطيط حركة الروبوتات، إلا أنه يمكن تحسينه من خلال تناول بعض النقاط. أولاً، كان من الأفضل تقديم مقارنة أوسع بين خوارزميات Bug وخوارزميات أخرى لتجنب العوائق، مما يعطي رؤية أشمل لأداء هذه الخوارزميات. ثانياً، لم يتم التطرق بشكل كافٍ إلى تأثير العوامل البيئية المتغيرة على أداء الخوارزميات، وهو موضوع حيوي في تطبيقات الروبوتات في العالم الحقيقي. ثالثاً، كان من الممكن تحسين البحث من خلال تقديم تجارب إضافية على أنواع مختلفة من الروبوتات والبيئات، مما يعزز من موثوقية النتائج. وأخيراً، يمكن أن يكون هناك تركيز أكبر على تحسين آلية الانتقال بين مراحل تتبع الحدود والسعي نحو الهدف في خوارزميات Bug لتحسين كفاءتها.
Questions related to the research
  1. ما هي أهمية تخطيط حركة الروبوتات؟

    تخطيط حركة الروبوتات مهم لأنه يمكن الروبوت من الوصول إلى هدفه بشكل آلي ودون اصطدام بالعوائق، مما يزيد من أداء الروبوت ويقلل من تكاليف التشغيل.

  2. ما هي خوارزميات Bug المستخدمة في البحث؟

    البحث تناول خوارزميات Bug1 وBug2 وTangentBug، وهي خوارزميات تستخدم لتجنب العوائق وتخطيط مسار الروبوت في بيئات غير معروفة بشكل كامل.

  3. ما هي النتائج الرئيسية التي توصل إليها البحث؟

    النتائج أظهرت أن خوارزمية Bug1 توفر مساراً أقصر، بينما خوارزمية Bug2 تعتبر أسرع في الوصول إلى الهدف. كما أن تقنية التنعيم ساعدت في تحسين مسارات الروبوت وتقليل التعرجات الصغيرة.

  4. ما هي التوصيات التي قدمها البحث لتحسين أداء الروبوتات؟

    البحث أوصى بزيادة كفاءة الخوارزميات من خلال تحسين آلية الانتقال بين مراحل تتبع الحدود والسعي نحو الهدف، واختيار الاتجاه الأنسب لتتبع الحدود، وتقسيم المسار إلى أهداف جزئية لضمان اجتياز العوائق بشكل أفضل.


References used
S. M. LAVALLE, Planning Algorithms, New York, USA. : Cambridge University Press, 2006
P. K. DAS, H. S. BEHERA, P. K. JENA AND B. K. PANIGRAHI, "Multi-robot path planning in a dynamic environment using improved gravitational search algorithm," Journal of Electrical Systems and Information Technology, 2016,vol. 3, pp. 295-313
L. FENG-LI, "Tracking and Following Algorithms of Mobile Robots for Service Activities in Dynamic Environments," International Journal of Automation and Smart Technology, 2015,vol. 4, no. 4, pp. 39-48
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