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

• Published in: IEEE access Vol. 9; pp. 38132 - 38143
• Main Authors: Baziyad, Mohammed, Saad, Mohamed, Fareh, Raouf, Rabie, Tamer, Kamel, Ibrahim
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Computational efficiency; Computer networks; Computing time; Heuristic algorithms; Heuristic methods; kinect; Linear programming; Machine learning algorithms; objective function; Optimization; Optimization techniques; Particle swarm optimization; Path planning; Potential fields; probabilistic roadmap; Protocol (computers); Real time; Real-time systems; Robotics; Robots; Routing (telecommunications); Shortest-path problems; Smoothness; Tradeoffs; Trajectory planning
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3058121

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.