One of the main challenges when developing mobile robots is path planning. Efficient and robust algorithms are needed to produce plans for the movements of the robot. Many classical path planning algorithms depend on geometrically simple environments to achieve good performance, otherwise the paths produced tend to be far from ideal especially when the paths are to be optimized for multiple obj...

Many robotic tasks require solutions that maximize multiple performance objectives. For example, in path-planning, these objectives often include finding short paths that avoid risk and maximize the information obtained by the robot. Although there exist many algorithms for multi-objective optimization, few of these algorithms apply directly to robotic path-planning and fewer still are capable ...

Many sequential decision-making problems require an agent to reason about both multiple objectives and uncertainty regarding the environment’s state. Such problems can be naturally modelled as multi-objective partially observable Markov decision processes (MOPOMDPs). We propose optimistic linear support with alpha reuse (OLSAR), which computes a bounded approximation of the optimal solution set...

How an autonomous underwater vehicle (AUV) performs fully automated task allocation and achieves satisfactory mission planning effects during the search for potential threats deployed in space is focus of paper. First, assignment problem defined as a traveling salesman (TSP) with specific distinct starting ending points. Two competitive non-commensurable optimization goals, total sailing distan...

Abstract Distributed power can effectively alleviate the problems of environmental pollution and energy shortage. Based on consideration low-carbon benefits DG, a multi-objective mathematical model with minimal system carbon dioxide emissions, minimum annual network loss cost, voltage deviation distribution is established for multi-type DG access location capacity. This solved using MOPSO algor...