Mobile robot path planning refers to the design of the safely collision-free path with shortest distance and least time-consuming from the starting point to the end point by a mobile robot autonomously. In this paper, a systematic review of mobile robot path planning techniques is presented. Firstly, path planning is classified into global path planning and local path planning according to the mastery of environmental information. In the global path planning, environment modeling methods and path evaluation method are introduced. The methods of environment modeling include grid method, topology method, geometric feature method and mixed representation method. In the local path planning, we introduce the sensors commonly used in the detection environment, including laser radar and visual sensor. Next, according to the characteristics of algorithms, mobile robot path planning algorithms are divided into three categories: classical algorithms, bionic algorithms and artificial intelligence algorithms. Among the classical algorithms, we introduce the cell decomposition method, sampling based method, graph search algorithm, artificial potential field method and dynamic window method. In the algorithm based on bionics, we introduce genetic algorithm, ant colony algorithm, gray wolf algorithm, etc. in detail. In artificial intelligence algorithm, we introduce neural network algorithm and fuzzy logic. Finally, we compare the key technologies of mobile robot path planning in the form of graphs and charts based on the classification statistics of the collected literature to provide references for future research. 

Path planning techniques for mobile robots: Review and prospect

FAGV is a kind of heavy equipment in the storage environment. Its path needs to be simple and smooth and should be able to avoid sudden obstacles in the process of driving. According to the environmental characteristics of intelligent storage and the task requirements of FAGV, this paper proposed a hybrid dynamic path planning algorithm for FAGV based on improved A* and improved DWA. The improved A* algorithm can plan the global optimal path more suitable for FAGV. The improved evaluation function of DWA can ensure that the local path of FAGV is closer to the global path. DWA combines the rolling window method for local path planning to avoid sudden unknown static and dynamic obstacles. In addition, this paper verifies the effectiveness of the algorithm through simulation. The simulation results show that the algorithm can avoid obstacles dynamically without being far away from the global optimal path.

/pdf/dynamic-path-planning-for-forklift-agv-based-on-smoothing-a-2o73iwiz.pdf

Dynamic Path Planning for Forklift AGV Based on Smoothing A* and Improved DWA Hybrid Algorithm

With the recent development of artificial intelligence (AI) and information and communication technology, manned vehicles operated by humans used on the ground, air, and sea are evolving into unmanned vehicles (UVs) that operate without human intervention. In particular, unmanned marine vehicles (UMVs), including unmanned underwater vehicles (UUVs) and unmanned surface vehicles (USVs), have the potential to complete maritime tasks that are unachievable for manned vehicles, lower the risk of man power, raise the power required to carry out military missions, and reap huge economic benefits. The aim of this review is to identify past and current trends in UMV development and present insights into future UMV development. The review discusses the potential benefits of UMVs, including completing maritime tasks that are unachievable for manned vehicles, lowering the risk of human intervention, and increasing power for military missions and economic benefits. However, the development of UMVs has been relatively tardy compared to that of UVs used on the ground and in the air due to adverse environments for UMV operation. This review highlights the challenges in developing UMVs, particularly in adverse environments, and the need for continued advancements in communication and networking technologies, navigation and sound exploration technologies, and multivehicle mission planning technologies to improve UMV cooperation and intelligence. Furthermore, the review identifies the importance of incorporating AI and machine learning technologies in UMVs to enhance their autonomy and ability to perform complex tasks. Overall, this review provides insights into the current state and future directions for UMV development.

/pdf/survey-on-the-developments-of-unmanned-marine-vehicles-1ndmlind.pdf

Survey on the Developments of Unmanned Marine Vehicles: Intelligence and Cooperation

Isotropic porous materials retain the same physical properties in any direction, regardless of the measurement direction. Therefore, mechanical metamaterials with isotropic properties are widely welcomed in engineering. Most of the existing artificial isotropic structures are composed of isotropic solid materials, and the design approach is singular and limited. There are numerous anisotropic materials in nature. Can we expand our approach by using anisotropic porous materials to obtain isotropic microstructures? This study answers this question, which focuses on the topological design of microstructures with multiple mechanical properties using given anisotropic materials. The considered mechanical properties include the maximum bulk modulus, shear modulus, equivalent elastic modulus, and multi-objective design. Smooth evolutionary method is adopted to find the final micro-topologies, with both the isotropic and volume fraction constraints imposed simultaneously. The macroscopic equivalent properties of the matrix materials and the obtained isotropic materials are evaluated using the homogenization method. Finally, the feasibility and effectiveness of the proposed method are verified by four numerical examples. Many interesting isotropic metamaterials are obtained, and some of the final structures are manufactured through 3D printing. This method can provide ideas for the design of other intelligent material microstructures with isotropic properties. 

Topological design for isotropic metamaterials using anisotropic material microstructures

Multi-robot systems are popularly distributed in logistics, transportation, and other fields. We propose a distributed multi-mobile robot obstacle-avoidance algorithm to coordinate the path planning and motion navigation among multiple robots and between robots and unknown territories. This algorithm fuses the ant colony optimization (ACO) and the dynamic window approach (DWA) to coordinate a multi-robot system through a priority strategy. Firstly, to ensure the optimality of robot motion in complex terrains, we proposed the dual-population heuristic functions and a sort ant pheromone update strategy to enhance the search capability of ACO, and the globally optimal path is achieved by a redundant point deletion strategy. Considering the robot’s path-tracking accuracy and local target unreachability problems, an adaptive navigation strategy is presented. Furthermore, we propose the obstacle density evaluation function to improve the robot’s decision-making difficulty in high-density obstacle environments and modify the evaluation function coefficients adaptively by combining environmental characteristics. Finally, the robots’ motion conflict is resolved by combining our obstacle avoidance and multi-robot priority strategies. The experimental results show that this algorithm can realize the cooperative obstacle avoidance of AGVs in unknown environments with high safety and global optimality, which can provide a technical reference for distributed multi-robot in practical applications.

/pdf/distributed-multi-mobile-robot-path-planning-and-obstacle-3hs8zvn9.pdf

Distributed Multi-Mobile Robot Path Planning and Obstacle Avoidance Based on ACO–DWA in Unknown Complex Terrain

To further improve the path planning of the mobile robot in complex dynamic environments, this paper proposes an enhanced hybrid algorithm by considering the excellent search capability of the ant colony optimization (ACO) for global paths and the advantages of the dynamic window approach (DWA) for local obstacle avoidance. Firstly, we establish a new dynamic environment model based on the motion characteristics of the obstacles. Secondly, we improve the traditional ACO from the pheromone update and heuristic function and then design a strategy to solve the deadlock problem. Considering the actual path requirements of the robot, a new path smoothing method is present. Finally, the robot modeled by DWA obtains navigation information from the global path, and we enhance its trajectory tracking capability and dynamic obstacle avoidance capability by improving the evaluation function. The simulation and experimental results show that our algorithm improves the robot’s navigation capability, search capability, and dynamic obstacle avoidance capability in unknown and complex dynamic environments.

/pdf/an-effective-dynamic-path-planning-approach-for-mobile-1700fxkl.pdf

An Effective Dynamic Path Planning Approach for Mobile Robots based on Ant Colony Fusion Dynamic Windows

Cooperative strategy for pursuit-evasion problem with collision avoidance

Multi-condition optimisation design of a hydrofoil based on deep belief network

Hierarchical Numbering-Up of Modular Reactors: A Multi-Objective Optimization Approach

With the background of limited energy storage of robots and considering the high coupling problem of multi-agent path finding (MAPF), we propose a priority-free ant colony optimization (PFACO) to plan conflict-free and energy-efficient paths, reducing multi-robots motion cost in the rough ground environment. First, a dual-resolution grid map considering obstacles and ground friction factors is designed to model the unstructured rough terrain. Second, an energy-constrained ant colony optimization (ECACO) is proposed to achieve energy-optimal path planning for a single robot, in which we improve the heuristic function based on the combined effects of path length, path smoothness, ground friction coefficient and energy consumption, and consider multiple energy consumption metrics during robot motion to improved pheromone update strategy. Finally, considering multiple collision conflict cases among multiple robots, we incorporate a prioritized conflict-free strategy (PCS) and a route conflict-free strategy (RCS) based on ECACO to achieve MAPF with low-energy and conflict-free in a rough environment. Simulation and experimental results show that ECACO can achieve better energy saving for single robot motion under all three common neighborhood search strategies. PFACO achieves both the conflict-free path and energy-saving planning for robots in complex scenarios, and the study has some reference value for solving practical problems.

Conflict-free and energy-efficient path planning for multi-robots based on priority free ant colony optimization.

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Ping Li

Author Tools

Chat about Author