An Efficient Scheduling and Navigation Approach for Warehouse Multi-Mobile Robots

Zhao, Kang; Xu, Benlian; Lu, Mingli; Shi, Jing; Li, Zhen

doi:10.1007/978-3-031-09726-3_5

Cited by 4 publications

(1 citation statement)

References 12 publications

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“…There are many variants: the colored traveling salesman problem [ 1 ], the intermittent traveling salesman problem [ 2 ], the traveling salesman problem with release times [ 3 ], the traveling salesman problem with draft limits [ 4 ], and large-scale general colored traveling salesman [ 5 ]. As robotic technology advances, a mobile robot faces multi-target path planning problems in numerous circumstances [ 6 , 7 , 8 , 9 ]. The usual optimization criteria of the planning algorithm are the path length or the time cost [ 10 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Precision-Driven Multi-Target Path Planning and Fine Position Error Estimation on a Dual-Movement-Mode Mobile Robot Using a Three-Parameter Error Model

Zhao

Misyurin

et al. 2023

Sensors

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The multi-target path planning problem is a universal problem to mobile robots and mobile manipulators. The two movement modes of forward movement and rotation are universally implemented in integrated, commercially accessible mobile platforms used in logistics robots, construction robots, etc. Localization error in multi-target path tracking is one of the crucial measures in mobile robot applications. In this article, a precision-driven multi-target path planning is first proposed. According to the path’s odometry error evaluation function, the precision-optimized path can be discovered. Then, a three-parameter odometry error model is proposed based on the dual movement mode. The error model describes localization errors in terms of the theoretical motion command values issued to the mobile robot, the forward moving distances, and the rotation angles. It appears that the three error parameters follow the normal distribution. The error model is finally validated using a mobile robot prototype. The error parameters can be identified by analyzing the actual moving trajectory of arbitrary movements. The experimental localization error is compared to the simulated localization error in order to validate the proposed error model and the precision-driven path planning method. The OptiTrack motion capture device was used to capture the prototype mobile robot’s pose and position data.

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