Knowledge-driven path planning for mobile robots: relative state tree

Abstract

Path planning is important in the field of mobile robot. However, traditional path planning techniques optimize the navigation path solely based on the models of the robot and the environments. Owing to the time-varying environment, the robot is expected to launch the replanning procedure in real-time continuously. It is slow and wastes computing resources for repeated decisions. In this study, a new perspective is adopted which utilizes a knowledge-driven approach for path planning. The concept of relative state tree is proposed to develop an incremental learning method based on a path planning knowledge base. The knowledge library, which stores a collection of the mappings from environmental information to robot decisions, can be established by offline or online learnings. As the robot plans online, its movement is guided by the optimal decision that is retrieved from the library based on the information which matches mostly the current environment. A large number of simulations are executed to verify the proposed method. When Communicated by G. Acampora.

A Hybrid Approach to Process Planning: The Urban Traffic Controller Example

“…These algorithms are then used alongside an action model representing the domain specific knowledge. Also, the emerging development in the field of automated planning with constraints processing has facilitated the deployment of deliberative reasoning to real-time control applications (Heinrich et al, 2015;Chen et al, 2015). There are many successful applications of domain-independent planning to realworld problems.…”