MOD* Lite: An Incremental Path Planning Algorithm Taking Care of Multiple Objectives

Abstract

The need for determining a path from an initial location to a target one is a crucial task in many applications, such as virtual simulations, robotics, and computer games. Almost all of the existing algorithms are designed to find optimal or suboptimal solutions considering only a single objective, namely path length. However, in many real life application path length is not the sole criteria for optimization, there are more than one criteria to be optimized that cannot be transformed to each other. In this paper, we introduce a novel multiobjective incremental algorithm, multiobjective D* lite (MOD* lite) built upon a well-known path planning algorithm, D* lite. A number of experiments are designed to compare the solution quality and execution time requirements of MOD* lite with the multiobjective A* algorithm, an alternative genetic algorithm we developed multiobjective genetic path planning and the strength Pareto evolutionary algorithm.

A Multiobjective Optimization Approach for COLREGs-Compliant Path Planning of Autonomous Surface Vehicles Verified on Networked Bridge Simulators

“…In practice, depending on mission requirements, a number of objectives/preferences such as safety, efficiency, short-distance and comfort, may need to be considered when planning a path for ASVs [5]- [7]. For instance, it is desired that an ASV for mine counter measure operations does not deviate far from pre-specified areas, and an ASV used as cruise ship should manoeuvre smoothly without abrupt course/speed changes.…”

“…On the other hand, a number of multiobjective evolutionary algorithms have been developed [5]- [7], [19] to tackle multiple objectives simultaneously in path planning. These results are developed either as general robotic motion planning algorithms [5], [7], or applied in other kinds of autonomous vehicles including aerial and ground vehicles [6], [19]. One direct challenge of adapting existing multiobjective optimisation approaches for ASVs' path planning lies in how to incorporate COLREGs into the optimisation framework.…”

Mobile Robot Path Planning with a Non-Dominated Sorting Genetic Algorithm

“…Therefore, the weighting method is not proper. On the other hand, some researchers extended the deterministic heuristic A* algorithm to multi-objective cases [22][23][24]. In these algorithms, the heuristic function of any node in the graph search space is a vector.…”

Smooth Path Planning for Robot Docking in Unknown Environment with Obstacles

“…To address this problem, docking robots to the deployed stations and recharging them attract more and more attention of the researchers in practical applications for their convenience and efficiency [10,11]. However, if the surrounding environment of the docking station is unknown or partially unknown beforehand, especially for nonholonomic robots such as autonomous underwater vehicles (AUVs) and unmanned ground vehicles, the safety of the robot in docking process may be endangered by the existing obstacles [12][13][14][15][16]. Furthermore, short-sighted collision avoidance maneuvers may also lead to the failure of docking if the docking pose adjustment is not fully considered [17][18][19].…”