Dynamic Path Planning: D*-ACO Framework with Local Trajectory Optimization for Autonomous Mobile Robots in Dynamic Environments

The path planning process for Autonomous Mobile Robots (AMRs) in the changing ecosystem is still one of the challenging issues to achieve both adaptability and optimality in terms of time and computation. In the case of conventional graph-search algorithms like A* and Dijkstra, the shortest paths can be found reliably, yet these may not prove to yield successful solutions in the changing environments. In contrast, while using D* replanning approach, smooth paths are not obtained. On the other hand, swarm-based methods like ACO can produce smooth trajectories due to computational efficiency, these methods cannot be used in real-time navigation applications.

In this paper proposes a novel Hybrid D-ACO Path Planning Framework with Local Optimization for autonomous mobile robot navigation in dynamic environments. The proposed framework integrates the rapid incremental replanning capability of D with the global optimization characteristics of ACO while introducing a localized optimization strategy to reduce computational overhead. In contrast to other hybrid strategies that use optimization globally, the new strategy uses ACO in a selective manner only when the path region changes dynamically. In addition, local smoothing has been introduced to remove unnecessary turns from the path.

The proposed framework is analyzed in a simulation setup that consists of both static and dynamic obstacles. The analysis includes comparison of this proposed framework with A, D, RRT* algorithms, and traditional ACO algorithm through different criteria such as path length, runtime, smoothness of the path, computational complexity, and success rate.