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Path quality and computational time have formed together a well-known trade-off problem for path planning techniques. Due to this trade-off, contributions were usually considering improving only one of the two aspects, either increasing the swiftness as in real-time robotic path planning algorithms or enhancing the path quality as in shortest path query algorithms. Producing a path planning technique that targets both aspects is a challenging problem for robotic systems. However, this paper proposes a novel path planning framework that controls the motion of robotic systems and aims to overcome this traditional trade-off challenge, by targeting both, decreasing the computational time, and improving the path quality represented by the path length and smoothness. The shortest path is obtained by minimizing a novel objective function inspired by the artificial potential field methodology. To accelerate the execution, the Particle Swarm Optimization (PSO) technique is adopted to obtain the optimal solution in a real-time hop-by-hop manner imitating the procedures performed by computer network routing protocols. Testbed experimental results have proven the effectiveness of the proposed technique and showed superior performance over other meta-heuristic optimization techniques and over classical path planning approaches such as A*, D*, and PRM.

Addressing Real-Time Demands for Robotic Path Planning Systems: A Routing Protocol Approach