Multi-Objective Optimal Path Planning for Autonomous Robots with Moving Obstacles Avoidance in Dynamic Environments

Abstract

Path planning is vital for robust autonomous robot navigation. Driving in dynamic environments is particularly difficult. The majority of the work is based on the premise that a robot possesses a comprehensive and precise representation of its surroundings prior to its starting. The problem of partially knowing and dynamic environments has received little attention. This circumstance occurs when an exploratory robot or a robot without a floor plan or terrain map must move to its destination. Existing approaches for dynamic-path-planning design a preliminary path based-on known knowledge of the environment, then adjust locally by replanning the total path as obstacles are discovered by the robot's sensors, thereby sacrificing either optimality or computational efficacy. This paper presents a novel algorithm. A Near-Optimal Multi-Objective Path Planner (NO-MOPP), capable of planning time-efficient, near-optimal, and drivable paths in partially known and dynamic environments. It is an expansion of our earlier research contributions called "A Multi-Objective Hybrid Collision-free Optimal Path Finder (MOHC-OPF) for Autonomous Robots in known static environments" and "A Multi-Objective Hybrid Collision-free Near-Optimal Path Planner (MOHC-NODPP) for Autonomous Robots in Dynamic environments". In the environment, a mix of static and moving dynamic obstacles are present, both of which are expressed by a hybrid, discrete configuration space in an occupancy-grid map. The proposed approach is executed at two distinct levels. Using our earlier method, A Multi-Objective Collision-free Optimal Path Finder (MOHC-OPF), the initial optimal path is found in environment that includes only known stationery obstacles at the Global-path-planning level. On the second level, known as Local Re-planning, this optimal path is continuously refined by online re-planning to account for the movement of obstacles in the environment. The proposed method, A Near-Optimal Multi-Objective Path Planner (NO-MOPP), is used to keep the robot's sub-paths optimum while also avoiding dynamic obstacles. This is done while still obeying the robot's non-holonomic restrictions. The proposed technique is tested in simulation using a collection of standard maps. The simulation findings demonstrate the proposed method's ability to avoid static as well as dynamic obstacles, as well as its capacity to find a near-optimal-path to a goal location in environments that are constantly changing without collision. The optimal-path is determined by taking into account several performance measures, including path length, collision-free path, execution time, and smooth paths. 90% of studies utilizing the proposed method demonstrate that it is more effective than other methods for determining the shortest length and time-efficient smooth drivable paths. The proposed technique reduced average 15% path length and execution time compared to the existing methods.