HCTNav: A path planning algorithm for low-cost autonomous robot navigation in indoor environments

© 2013 by MDPI (http://www.mdpi.org). Reproduction is permitted for noncommercial purposes.Low-cost robots are characterized by low computational resources and limited energy supply. Path planning algorithms aim to find the optimal path between two points so the robot consumes as little energy as possible. However, these algorithms were not developed considering computational limitations (i.e., processing and memory capacity). This paper presents the HCTNav path-planning algorithm (HCTLab research group’s navigation algorithm). This algorithm was designed to be run in low-cost robots for indoor navigation. The results of the comparison between HCTNav and the Dijkstra’s algorithms show that HCTNav’s memory peak is nine times lower than Dijkstra’s in maps with more than 150,000 cells.This work has been partially supported by the Spanish “Ministerio de Ciencia e Innovación”, under project TEC2009-09871

High-Speed shortest path co-processor design

Shortest path algorithms are significant in graph theory and have been applied in many applications such as transportation and networking. Most of the shortest path calculation is performed on general purpose processor where instructions must be run to read the input, compute the result, and set the output which later on will slow down the overall performance. Therefore, the authors proposed a hardware approach which implements FPGA technology to find the shortest path between two nodes. The FPGA approach will demonstrate how parallelism can be used to significantly reduce calculation steps compared to sequential effort. In this paper, A-Star algorithm has been chosen for the shortest path calculation since it can achieve superior time running based on its heuristic behavior