Zobrazeno 1 - 10
of 426
pro vyhledávání: '"Dynamic obstacle avoidance"'
Publikováno v:
Alexandria Engineering Journal, Vol 105, Iss , Pp 538-548 (2024)
Dynamic obstacle avoidance is crucial in autonomous driving, ensuring vehicle safety by preventing collisions and enhancing driving efficiency. Dynamic obstacle avoidance algorithms have made significant progress due to deep learning. However, video-
Externí odkaz:
https://doaj.org/article/ad482961f7494788b909ee48997d7c34
Publikováno v:
Zhihui kongzhi yu fangzhen, Vol 46, Iss 1, Pp 21-29 (2024)
A dynamic obstacle avoidance algorithm is proposed for the obstacle avoidance problem in the cruise task of unmanned boat cluster. Firstly, the square grid trajectory cell (SGTC) situation matrix of the waters around the unmanned surface vessle is ob
Externí odkaz:
https://doaj.org/article/1e38204134de48378a3509768b5812e8
Publikováno v:
IEEE Access, Vol 12, Pp 145496-145510 (2024)
This research presents a novel approach to train autonomous agents in complex and unknown environments, focusing on scene-specific learning, dynamic obstacle avoidance, and target tracking. Traditional reinforcement learning (RL) methods often suffer
Externí odkaz:
https://doaj.org/article/7f721f2b9b004cafb9529175e4087021
Autor:
Bong Seok Park, Sung Jin Yoo
Publikováno v:
IEEE Access, Vol 12, Pp 94393-94406 (2024)
This paper presents a resilient adaptive formation control design strategy to achieve guaranteed dynamic obstacle avoidance within limited inter-agent communication ranges in nonholonomic multi-robot systems. Unlike existing literature, the main cont
Externí odkaz:
https://doaj.org/article/54eb186df266474bbfe6162902f2337b
Publikováno v:
Applied Sciences, Vol 14, Iss 23, p 11086 (2024)
To address the shortcomings of traditional bionic algorithms in path planning, such as inefficient search processes, extended planning distances and times, and suboptimal dynamic obstacle avoidance, this paper introduces a fusion algorithm called NRB
Externí odkaz:
https://doaj.org/article/cd4d7b2ccaed4fbf9e28af3c874bed2b
Autor:
Siya Sun, Sirui Mao, Xusheng Xue, Chuanwei Wang, Hongwei Ma, Yifeng Guo, Haining Yuan, Hao Su
Publikováno v:
Sensors, Vol 24, Iss 21, p 6866 (2024)
At present, China’s coal mine permanent tunneling support commonly uses mechanized drilling and anchoring equipment; there are low support efficiency, labor intensity, and other issues. In order to further improve the support efficiency and liberat
Externí odkaz:
https://doaj.org/article/3038368ce874466ca2acaf5b6be1058b
Autor:
Sivashangaran, Shathushan
Operation of Autonomous Mobile Robots (AMRs) of all forms that include wheeled ground vehicles, quadrupeds and humanoids in dynamically changing GPS denied environments without a-priori maps, exclusively using onboard sensors, is an unsolved problem
Externí odkaz:
https://hdl.handle.net/10919/119290
Publikováno v:
e-Prime: Advances in Electrical Engineering, Electronics and Energy, Vol 8, Iss , Pp 100581- (2024)
Developing an autonomous vehicle in safe navigated open environments through self-learning is a formidable challenge. So, in this paper, an autonomous navigation implementation for urban environments with dynamic obstacles using a double DQN algorith
Externí odkaz:
https://doaj.org/article/1a8657f8b40b484882002ed83ffd779e
Publikováno v:
World Electric Vehicle Journal, Vol 15, Iss 7, p 320 (2024)
In this paper, a novel and complete navigation system is proposed for mobile robots in a park environment, which can achieve safe and stable navigation as well as robust dynamic obstacle avoidance. The navigation system includes a global planning lay
Externí odkaz:
https://doaj.org/article/fb9c00f528f64a6ba76e471d1eff6aaa
Publikováno v:
Complex & Intelligent Systems, Vol 10, Iss 1, Pp 1149-1166 (2023)
Abstract Robot navigation in crowded environments has recently benefited from advances in deep reinforcement learning (DRL) approaches. However, it still presents a challenge to designing socially compliant robot behavior. Avoiding collisions and the
Externí odkaz:
https://doaj.org/article/76ff0d3c05fc4fb6afa5abaddc9cc5b7