Enhancing Path Quality of Real-Time Path Planning Algorithms for Mobile Robots: A Sequential Linear Paths Approach

The traditional trade-off between execution speed and path quality has forced real-time robotic path planning algorithms to sacrifice path quality in order to execute in real-time. Producing a path planning algorithm that targets enhancing both, the path quality and swiftness is a challenging problem. However, this article proposes a novel path planning strategy that aims to break this traditional trade-off, by targeting both, increasing the swiftness, and enhancing the path quality represented by the path length and smoothness. The proposed strategy is based on the observation that most path planning algorithms waste the processing efforts in less critical areas of the map. Therefore, the proposed path planning strategy tends to focus on critical areas such as the areas around obstacles and areas around the goal point, and exhausts the processing power on these critical areas. This is done by neglecting all static obstacles that do not lie between the robot and the destination. For obstacles that intersect with the linear line from the robot to the destination, a basis traditional path planning algorithm such as A*, D* or the Probabilistic RoadMap (PRM) technique is only implemented around the obstacles in order to find a feasible path around each selected obstacle. This procedure would minimize the computational efforts compared to applying the basis algorithm on the whole map. Finally, the path quality is enhanced by finding any linear shortcuts between any two points in the path and fix these shortcuts as the final path from the starting point to the goal point. The proposed path planning strategy was tested on a P3-DX Pioneer mobile robot using a kinematic controller. The experimental results have proven that the path planning strategy was able to show a superior advantage over other path planning techniques in both aspects, computational time (reached up to 97.05% improvement) and path quality (reached up to 16.21% improvement for path length and 98.50% for smoothness).