Speeding up single-query sampling-based algorithms using case-based reasoning - Abdelwahed, Mustafa F.; Saleh, Mohamed; Mohamed, Amr E. (Expert systems with applications, 2018)
•Motion planning is an essential task for an autonomous vehicle.•Traditional motion planning algorithms ignore experience when serving new queries.•Experience-based algorithms use multiple thread execution for serving queries.•Motion planning belongs to P-SPACE hard problem class.•Case-based reasoning is an AI approach for problem-solving using stored experiences. We present an extension to the single-query sampling-based algorithm for improving its response time using Case-Based Reasoning (CBR) technique. Unlike traditional experience-based planners, CBR depends on a single thread execution which reduces the required computation power. Additionally, it is always biased towards exploration rather than exploitation to overcome experience-based algorithms drawbacks. Results indicate that CBR extension has significantly improved sampling-based response time for similar served queries.