维普期刊资源整合服务平台（V6.5）

维普中文期刊产品整合服务

2篇 您的检索式：作者名="Chengchen Zhuge"

选中清除导出查看参考文献查看引证文献引用追踪共1页首页上一页第1页下一页末页 /1 跳转

		题名	作者	年代	出处	被引量
	1	A Novel Dynamic Obstacle Avoidance Algorithm Based on Collision Time Histogram显示文摘Robot path planning in uncertain dynamic environment is a hot issue in the field of Unmanned ground vehicle(UGV).Starting from the practical demands of UGV,we propose a novel dynamic obstacle avoidance algorithm based on Collision time histogram(CTH).Given current steering angle,an effective collision check model,which is called Collision check circles(CCC),is firstly calculated.The local environment information is then combined with CCC to generate the proposed CTH.The nonholonomic nature of the vehicle is embedded in this process.Finally,the proposed algorithm calculates the executing steering angle by considering both the CTH and the target point.Extensive experiments and comparisons are conducted to evaluate the performance of the proposed algorithm.Simulation experiments are firstly conducted to verify its feasibility.Furthermore,real-world experiment is conducted to verify its effectiveness.Experimental results demonstrate the practical value of the proposed algorithm.	ZHUGE Chengchen CAI Yunfei TANG Zhenmin	2017	Chinese Journal of Electronics2017，26，3：	2
	2	An Improved Q-RRT^() Algorithm Based on Virtual Light显示文摘The Rapidly-exploring Random Tree(RRT)algorithm is an efficient path-planning algorithm based on random sampling.The RRT^()algorithm is a variant of the RRT algorithm that can achieve convergence to the optimal solution.However,it has been proven to take an infinite time to do so.An improved Quick-RRT^()(Q-RRT^())algorithm based on a virtual light source is proposed in this paper to overcome this problem.The virtual light-based Q-RRT^()(LQRRT^())takes advantage of the heuristic information generated by the virtual light on the map.In this way,the tree can find the initial solution quickly.Next,the LQRRT^()algorithm combines the heuristic information with the optimization capability of the Q-RRT^()algorithm to find the approximate optimal solution.LQRRT^()further optimizes the sampling space compared with the Q-RRT^()algorithm and improves the sampling efficiency.The efficiency of the algorithm is verified by comparison experiments in different simulation environments.The results show that the proposed algorithm can converge to the approximate optimal solution in less time and with lower memory consumption.	Chengchen Zhuge Qun Wang Jiayin Liu Lingxiang Yao	2021	Computer Systems Science & Engineering2021，39，10：	0

返回顶部每页显示：

共1页首页上一页第1页下一页末页 /1 跳转

goolge学术搜索-维普

网站首页 | 关于我们 | 联系我们 | 产品服务 | 客服中心 | 广告服务 | 版权声明 | 网站联盟 | 友情链接 | 售卡网点

版权所有© 渝B2-20050021-1　渝公网安备 50019002500403号　违法和不良信息举报中心

互联网出版许可证新出网证(渝)字10号全国400电话 - 免长途话费