scholarly journals Curve-based Approach for Optimal Trajectory Planning with Optimal Energy Consumption: application to Wheeled Mobile Robots

2020 ◽  
Vol 53 (2) ◽  
pp. 9670-9675
Author(s):  
Inderjeet Singh ◽  
Manarshhjot Singh ◽  
Ismail Bensekrane ◽  
Othman Lakhal ◽  
Rochdi Merzouki
Keyword(s):  
Energy Consumption ◽  
Mobile Robots ◽  
Trajectory Planning ◽  
Optimal Trajectory ◽  
Wheeled Mobile Robots ◽  
Optimal Energy ◽  
Optimal Trajectory Planning

scholarly journals Optimal Trajectory Planning for Wheeled Mobile Robots under Localization Uncertainty and Energy Efficiency Constraints

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 335
Author(s):  
Xiaolong Zhang ◽  
Yu Huang ◽  
Youmin Rong ◽  
Gen Li ◽  
Hui Wang ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Mobile Robots ◽  
Cost Function ◽  
Trajectory Planning ◽  
Optimal Trajectory ◽  
Planning Method ◽  
Wheeled Mobile Robots ◽  
The Cost ◽  
Localization Uncertainty ◽  
Optimal Trajectory Planning

With the rapid development of robotics, wheeled mobile robots are widely used in smart factories to perform navigation tasks. In this paper, an optimal trajectory planning method based on an improved dolphin swarm algorithm is proposed to balance localization uncertainty and energy efficiency, such that a minimum total cost trajectory is obtained for wheeled mobile robots. Since environmental information has different effects on the robot localization process at different positions, a novel localizability measure method based on the likelihood function is presented to explicitly quantify the localization ability of the robot over a prior map. To generate the robot trajectory, we incorporate localizability and energy efficiency criteria into the parameterized trajectory as the cost function. In terms of trajectory optimization issues, an improved dolphin swarm algorithm is then proposed to generate better localization performance and more energy efficiency trajectories. It utilizes the proposed adaptive step strategy and learning strategy to minimize the cost function during the robot motions. Simulations are carried out in various autonomous navigation scenarios to validate the efficiency of the proposed trajectory planning method. Experiments are performed on the prototype “Forbot” four-wheel independently driven-steered mobile robot; the results demonstrate that the proposed method effectively improves energy efficiency while reducing localization errors along the generated trajectory.

scholarly journals Navigation of Autonomous Light Vehicles Using an Optimal Trajectory Planning Algorithm

2021 ◽  
Vol 13 (3) ◽  
pp. 1233
Author(s):  
Ángel Valera ◽  
Francisco Valero ◽  
Marina Vallés ◽  
Antonio Besa ◽  
Vicente Mata ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Trajectory Planning ◽  
Autonomous Navigation ◽  
Optimal Trajectory ◽  
Computational Cost ◽  
Initial Point ◽  
Target Point ◽  
Software Components ◽  
Planning Algorithm ◽  
Optimal Trajectory Planning

Autonomous navigation is a complex problem that involves different tasks, such as location of the mobile robot in the scenario, robotic mapping, generating the trajectory, navigating from the initial point to the target point, detecting objects it may encounter in its path, etc. This paper presents a new optimal trajectory planning algorithm that allows the assessment of the energy efficiency of autonomous light vehicles. To the best of our knowledge, this is the first time in the literature that this is carried out by minimizing the travel time while considering the vehicle’s dynamic behavior, its limitations, and with the capability of avoiding obstacles and constraining energy consumption. This enables the automotive industry to design environmentally sustainable strategies towards compliance with governmental greenhouse gas (GHG) emission regulations and for climate change mitigation and adaptation policies. The reduction in energy consumption also allows companies to stay competitive in the marketplace. The vehicle navigation control is efficiently implemented through a middleware of component-based software development (CBSD) based on a Robot Operating System (ROS) package. It boosts the reuse of software components and the development of systems from other existing systems. Therefore, it allows the avoidance of complex control software architectures to integrate the different hardware and software components. The global maps are created by scanning the environment with FARO 3D and 2D SICK laser sensors. The proposed algorithm presents a low computational cost and has been implemented as a new module of distributed architecture. It has been integrated into the ROS package to achieve real time autonomous navigation of the vehicle. The methodology has been successfully validated in real indoor experiments using a light vehicle under different scenarios entailing several obstacle locations and dynamic parameters.

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