Robotics is an ever-expanding field and intelligent planning continues to play a major role. Given that the intention of mobile robots is to carry out tasks independent from human aid, robot intelligence is needed to make and plan out decisions based on various sensors. Planning is the fundamental activity that implements this intelligence into the mobile robots to complete such tasks. Understanding problems, challenges, and solutions to path planning and how it fits in is important to the realm of robotics.
Intelligent Planning for Mobile Robotics: Algorithmic Approaches presents content coverage on the basics of artificial intelligence, search problems, and soft computing approaches. This collection of research provides insight on both robotics and basic algorithms and could serve as a reference book for courses related to robotics, special topics in AI, planning, applied soft computing, applied AI, and applied evolutionary computing. It is an ideal choice for research students, scholars, and professors alike.
The many academic areas covered in this publication include, but are not limited to:
Tiwari, Anupam Shukla (both Indian Institute of Information Technology and Management), and Rahul Kala (U. of Reading, Britain) focus on the planning stage of creating mobile autonomous robots, emphasizing the basics of artificial intelligence, search problems, and soft computing approaches. They cover graph-based path planning, common planning techniques, evolutionary robotics, behavioral path planning, hybrid graph-based methods, hybrid evolutionary methods, and multi-robot systems.