An Optimized Energy and Time Constraints-Based Path Planning for the Navigation of Mobile Robots Using an Intelligent Particle Swarm Optimization Technique

Abstract

Mobile robots (MRs) typically require running for many hours on one charge of the battery. Electric autonomous mobile robots (AMRs) have become increasingly common in the manufacturing process in the last few years. MRs must often complete difficult assignments while gathering information across an unknown area involving energy constraints and time-sensitive preferences. This paper estimates the information collection assignment for surveillance as a multi-objective optimization dilemma with both energy and time constraints. In this study, three main objectives during acquiring data are taken into consideration, including the greatest quantity of data acquired for surveillance, following a path where obstacles are least likely to be experienced, and traveling the smallest feasible path. To obtain the optimal path for an MR by addressing the presented issue, this approach presents an intelligent particle swarm optimization (PSO) technique that determines fitness value by simplifying the optimization task for achieving the shortest path for MR navigation. It allows particles to execute variable operations while maintaining most of the prior search information. The findings of the simulation show that this technique of the PSO algorithm can realize swift convergence and high accuracy when compared with different benchmark functions derived for PSO. A comparative discussion on various energy-efficient navigation techniques for MRs is also provided. Lastly, this study describes the possible future research directions.

Discussion on different controllers used for the navigation of mobile robot

“…For future studies, various groups of objectives, such as motion consistency, multi-robot frameworks, sensor vibration, and several more, need to be taken into consideration. Real-world MR navigation regularly experiences uncertainty and sensor noises [75]. The most recent trend indicates that the commercialization of autonomous vehicles because of advances in technology in MRs will drive the economy over the period to come, while the rise of open-source systems combined with a decline in the cost of sensors is anticipated to boost the popularity of domestic MRs, including lawn mowers and vacuum cleaners.…”

Dynamic Path Planning for Mobile Robots by Integrating Improved Sparrow Search Algorithm and Dynamic Window Approach

“…Zheng et al [19] utilized the actual shortest distance traversed by ants to update heuristic information and introduced reward and penalty rules to optimize the local pheromone update strategy, thus enhancing the convergence speed and optimal solution capability of the ant colony algorithm. Raj et al [20] addressed the issue of autonomous mobile robots collecting information in unknown areas with energy constraints and time-sensitive preferences. They proposed an intelligent PSO technique, which determines fitness values by simplifying optimization tasks and improves the velocity update formula for each particle.…”

Global Time-Varying Path Planning Method Based on Tunable Bezier Curves

“…Path planning is a mapping from perceptual space to behavioral space and the planning method is one of the research hotspots at present. There are a variety of path planning methods commonly used, such as the potential energy method [1], heuristic search algorithm [2], Dijkstra algorithm [3], LPA* algorithm (Life Planning A*) [4], Floyd algorithm [5], PRM algorithm [6], RRT algorithm [7], unit division method [8] and intelligent algorithm [9][10][11]. However, these path planning algorithms cannot satisfy global adjustment, real-time change and multi-obstacle avoidance at the same time.…”