A Max-Min Ant System Approach to Autonomous Navigation

被引：0
|
作者：
Luo, Chaomin [1 ]
Alarabi, Saleh [2 ]
Bi, Zhuming [3 ]
Jan, Gene Eu [4 ]
机构：
[1] Mississippi State Univ, Dept Elect & Comp Engn, Mississippi State, MS 39762 USA
[2] Univ Detroit Mercy, Dept Elect & Comp Engn, Detroit, MI 48221 USA
[3] Purdue Univ, Dept Civil & Mech Engn, Ft Wayne, IN USA
[4] Tainan Natl Univ Arts, Grad Inst Animat & Film Art, Tainan, Taiwan
来源：
2019 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) | 2019年
关键词：
Max-min ant system; multi-goal navigation; histogram-based local navigator; mapping; foraging-enabled trail; NEURAL-NETWORK;
D O I：
10.1109/cec.2019.8790076
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Multi-goal navigation of an autonomous mobile robot seeks to reach multiple goals with a planned shortest trajectory in some real-world applications such as search and rescue robots, agricultural harvesting robots, resource prospecting robots and service robots, etc. An improved max-min ant system (MMAS) approach is proposed in this paper for realtime concurrent multi-goal navigation and map building. A global trajectory is planned by the proposed MMAS approach, in which a foraging-enabled trail is generated to guide the robot to the multiple goals. A histogram -based local navigator is utilized to guide the robot to follow the trail planned by the global path planner, with obstacle avoidance. In this paper, simulation results demonstrate that the real-time mapping and multi-goal navigation of an autonomous robot is successfully carried out under unknown environments.
引用
收藏
页码：1982 / 1987
页数：6
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