Semantic Local Planning for Mobile Robots through Path Optimization Services on the Edge: a Scenario-based Evaluation

SummaryWorking with mobile robots, prior to execute the local planning stage, they must know the environment where they are moving. For that reason the perception and mapping stages must be performed previously. This paper presents a survey in the state of the art in detection and tracking of moving obstacles (DATMO). The aim of what follows is to provide an overview of the most remarkable methods at each field specially in indoor environments where dynamic obstacles can be potentially more dangerous and unpredictable. We are going to show related DATMO methods organized in three approaches: model-free, model-based and grid-based. In addition, a comparison between them and conclusions will be presented.

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Simultaneous intermittent communication control and path optimization in networks of mobile robots

2016 IEEE 55th Conference on Decision and Control (CDC) ◽

10.1109/cdc.2016.7798525 ◽

2016 ◽

Cited By ~ 7

Author(s):

Yiannis Kantaros ◽

Michael M. Zavlanos

Keyword(s):

Mobile Robots ◽

Path Optimization ◽

Communication Control ◽

Intermittent Communication

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Path Optimization and Re-generation Algorithm for Indoor Mobile Robots in Obstacle Environments

Journal of Institute of Control Robotics and Systems ◽

10.5302/j.icros.2019.19.0090 ◽

2019 ◽

Vol 25 (7) ◽

pp. 633-640

Author(s):

Hyeonbeom Lee

Keyword(s):

Mobile Robots ◽

Path Optimization ◽

Generation Algorithm

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Modeling Power Requirements for Skid-Steer Mobile Robots in Manufacturing Environments

Volume 5A: 40th Mechanisms and Robotics Conference ◽

10.1115/detc2016-60152 ◽

2016 ◽

Cited By ~ 2

Author(s):

Stephen L. Canfield ◽

Tristan W. Hill ◽

Stephen G. Zuccaro

Keyword(s):

Energy Consumption ◽

Mobile Robots ◽

Steel Surface ◽

Primary Source ◽

Path Selection ◽

Path Optimization ◽

Design Parameters ◽

Function Evaluation ◽

Modeling Power ◽

Manufacturing Environments

This paper demonstrates an approach for predicting and optimizing energy consumption in skid-steer mobile robots (SSMRs) conducting manufacturing tasks. This work is unique in that it considers the energy associated with real-time predictions of slipping in the SSMR and further considers a specific application in which the SSMR is operating in an inverted (climbing) configuration on metal surfaces with homogeneous properties. The approach is based on a dynamic model that provides estimates of SSMR slipping motion during simulation. The model is used to estimate the underlying components of energy and will serve as the tool for objective function evaluation. The approach will follow previous path optimization strategies, parameterizing the path to provide design parameters and using appropriate optimization tools. A method to select the desired trajectory prior to conducting a manufacturing task is demonstrated. This paper primarily focuses on a scenario in which a climbing SSMR maneuvers on a steel surface by means of magnetic-based tracks with strong adhering forces. For this case, the friction due to slipping represents the primary source of energy consumption. This implies that the path selection is the most important parameter for the optimization.

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Comparison of Local Planning Algorithms for Mobile Robots

Modelling and Simulation for Autonomous Systems - Lecture Notes in Computer Science ◽

10.1007/978-3-319-22383-4_15 ◽

2015 ◽

pp. 196-208 ◽

Cited By ~ 1

Author(s):

Miroslav Kulich ◽

Viktor Kozák ◽

Libor Přeučil

Keyword(s):

Mobile Robots ◽

Local Planning ◽

Planning Algorithms

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Real-time path optimization of mobile robots based on improved genetic algorithm

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering ◽

10.1177/0959651820952207 ◽

2020 ◽

pp. 095965182095220

Author(s):

Minchuan Wang

Keyword(s):

Genetic Algorithm ◽

Mobile Robots ◽

Real Time ◽

Simulation Analysis ◽

Fitness Function ◽

Optimal Path ◽

Path Optimization ◽

Improved Genetic Algorithm ◽

Time Path ◽

Improved Algorithm

The emergence of intelligent mobile robots has liberated the human labor to a certain extent, especially their abilities to work in harsh environments in place of humans. For intelligent mobile robots, how to achieve fast path optimization is an important issue. In this article, the model establishment method of environmental information collected by robot sensors and the genetic algorithm for real-time optimization of running paths are briefly introduced first, the crossover, mutation probability, and fitness function are improved based on the shortcomings of the traditional genetic algorithm, and then the simulation analysis of the two algorithms is carried out using matrix laboratory (MATLAB) software. The results show that the improved algorithm obtains a smaller length of optimal path, fewer inflection points, and a smaller turning angle, which also converges faster and has a greater degree of fitness. It takes 0.053 s for the traditional algorithm to calculate the optimal path, while the improved algorithm needs 0.013 s. In summary, the improved genetic algorithm can quickly and efficiently calculate the optimal path, which is suitable for real-time path optimization of mobile robots.

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