Geometric motion planning and formation optimization for a fleet of nonholonomic wheeled mobile robots

Author(s):  
R. Bhatt ◽  
Chin Pei Tang ◽  
V. Krovi
Author(s):  
Eric Heiden ◽  
Luigi Palmieri ◽  
Leonard Bruns ◽  
Kai Oliver Arras ◽  
Gaurav Sukhatme ◽  
...  

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3517
Author(s):  
Mohammad Mohammadpour ◽  
Lotfi Zeghmi ◽  
Sousso Kelouwani ◽  
Marc-André Gaudreau ◽  
Ali Amamou ◽  
...  

In recent years, the use of electric Autonomous Wheeled Mobile Robots (AWMRs) has dramatically increased in transport of the production chain. Generally, AWMRs must operate for several hours on a single battery charge. Since the energy density of the battery is limited, energy efficiency becomes a key element in improving material transportation performance during the manufacturing process. However, energy consumption is influenced by the navigation stages, because the type of motion necessary for the AWMR to perform during a mission is totally defined by these stages. Therefore, this paper analyzes methods of energy efficiency that have been studied recently for AWMR navigation stages. The selected publications are classified into planning and motion control categories in order to identify research gaps. Unlike other similar studies, this work focuses on these methods with respect to their implications for the energy consumption of AWMRs. In addition, by using an industrial Self-Guided Vehicle (SGV), we illustrate the direct influence of the motion planning stage on global energy consumption by means of several simulations and experiments. The results indicate that the reaction of the SGV in response to unforeseen obstacles can affect the amount of energy consumed. Hence, energy constraints must be considered when developing the motion planning of AWMRs.


2019 ◽  
Vol 52 (5-6) ◽  
pp. 317-325 ◽  
Author(s):  
Bo You ◽  
Zhi Li ◽  
Liang Ding ◽  
Haibo Gao ◽  
Jiazhong Xu

Wheeled mobile robots are widely utilized for environment-exploring tasks both on earth and in space. As a basis for global path planning tasks for wheeled mobile robots, in this study we propose a method for establishing an energy-based cost map. Then, we utilize an improved dual covariant Hamiltonian optimization for motion planning method, to perform point-to-region path planning in energy-based maps. The method is capable of efficiently handling high-dimensional path planning tasks with non-convex cost functions through applying a robust active set algorithm, that is, non-monotone gradient projection algorithm. To solve the problem that the path planning process is locked in weak minima or non-convergence, we propose a randomized variant of the improved dual covariant Hamiltonian optimization for motion planning based on simulated annealing and Hamiltonian Monte Carlo methods. The results of simulations demonstrate that the final paths generated can be time efficient, energy efficient and smooth. And the probabilistic completeness of the method is guaranteed.


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