Flexible multi-unmanned ground vehicles (MUGVs) in intersection coordination based on ε-constraint probability collectives algorithm

The test system for technical abilities of unmanned vehicles is gradually developed from the single test to comprehensive test. The pre-established test and evaluation system can promote the development of unmanned ground vehicles. The 2009 Future Challenge: Intelligent Vehicles and Beyond (FC’09) pushed China's unmanned vehicles out of laboratories. This paper proposed to design a more scientific and comprehensive test system for future competitions to better guide and regulate the development of China's unmanned vehicles. According to the design idea of stage by stage and level by level, the hierarchical test content from simple to advanced, from local to overall is designed. Then the hierarchic test environment is established according to the levels of test content. The test method based on multi-platform and multi-sensor is put forward to ensure the accuracy of test results. The testing criterion framework is set up to regulate future unmanned vehicle contests and to assess the unmanned vehicles scientifically and accurately.

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Development of military unmanned ground vehicles

10.1117/12.2601876 ◽

2021 ◽

Author(s):

Ying Qian ◽

Feitong Wang ◽

Cheng Lin ◽

Shengye Huang ◽

Xuejia Guo

Keyword(s):

Unmanned Ground Vehicles ◽

Ground Vehicles

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A Perception-Based Fuzzy Route Planing Algorithm for Autonomous Unmanned Ground Vehicles

Unmanned Systems ◽

10.1142/s2301385018500073 ◽

2018 ◽

Vol 06 (04) ◽

pp. 251-266

Author(s):

Phillip J. Durst ◽

Christopher T. Goodin ◽

Cindy L. Bethel ◽

Derek T. Anderson ◽

Daniel W. Carruth ◽

...

Keyword(s):

Path Planning ◽

Urban Area ◽

Route Planning ◽

Unmanned Ground Vehicles ◽

Membership Functions ◽

Ground Vehicles ◽

Ground Vehicle ◽

Sensor Error ◽

Planning Algorithm ◽

Error Costs

Path planning plays an integral role in mission planning for ground vehicle operations in urban areas. Determining the optimum path through an urban area is a well-understood problem for traditional ground vehicles; however, in the case of autonomous unmanned ground vehicles (UGVs), additional factors must be considered. For an autonomous UGV, perception algorithms rather than platform mobility will be the limiting factor in operational capabilities. For this study, perception was incorporated into the path planning process by associating sensor error costs with traveling through nodes within an urban road network. Three common perception sensors were used for this study: GPS, LIDAR, and IMU. Multiple set aggregation operators were used to blend the sensor error costs into a single cost, and the effects of choice of aggregation operator on the chosen path were observed. To provide a robust path planning ability, a fuzzy route planning algorithm was developed using membership functions and fuzzy rules to allow for qualitative route planning in the case of generalized UGV performance. The fuzzy membership functions were then applied to several paths through the urban area to determine what sensors were optimized in each path to provide a measure of the UGV’s performance capabilities. The research presented in this paper shows the impacts that sensing/perception has on ground vehicle route planning by demonstrating a fuzzy route planning algorithm constructed by using a robust rule set that quantifies these impacts.

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