Path planning is a critical combinatorial problem essential for the navigation of a mobile robot. Several research initiatives, aiming at providing optimized solutions to this problem, have emerged. Ant Colony Optimization (ACO) and Genetic Algorithms (GA) are the two most widely used heuristics that have shown their effectiveness in solving such a problem. This paper presents, smartPATH, a new hybrid ACO-GA algorithm to solve the global robot path planning problem. The algorithm consists of a combination of an improved ACO algorithm (IACO) for efficient and fast path selection, and a modified crossover operator for avoiding falling into a local minimum. Our system model incorporates a Wireless Sensor Network (WSN) infrastructure to support the robot navigation, where sensor nodes are used as signposts that help locating the mobile robot, and guide it towards the target location. We found out smartPATH outperforms classical ACO (CACO) and GA algorithms (as defined in the literature without modification) for solving the path planning problem both and Bellman-Ford shortest path method. We demonstrate also that smartPATH reduces the execution time up to 64.9% in comparison with Bellman-Ford exact method and improves the solution quality up to 48.3% in comparison with CACO.