Heuristic Solutions to Route Optimization for Autonomous Robot Assistant

This study explores the application of heuristic algorithms to the salesman problem solution for optimizing the mobile robot assistant's route. Utilizing the branch-and-bound method for exact solutions and heuristic methods such as simulated annealing and the ant algorithm, the research assesses their performance across networks of different sizes. Simulation results reveal the ant algorithm's superior efficiency for smaller networks (up to 100 nodes), outperforming simulated annealing in terms of average route length and calculation time. However, scalability challenges emerge with larger networks (500 nodes). Detailed algorithm parameters are presented, emphasizing the significance of tailored settings for optimal performance. The study concludes that while heuristic algorithms are advantageous for rapid route generation, exact algorithms are preferred for precision, offering insights for route planning optimization for a robot assistant.