Global Path and Local Motion Planning for Humanoid Climbing Robot Using Kinect Sensor

2021 ◽  
pp. 109-122
Author(s):  
Dung Anh Nguyen ◽  
Akira Shimada
Keyword(s):  
Motion Planning ◽  
Kinect Sensor ◽  
Climbing Robot ◽  
Local Motion

Local Motion Planning and Tracking of Autonomous Driving Vehicles*

2020 ◽  
Author(s):  
Wangwang Zhu ◽  
Xi Zhang ◽  
Baixuan Zhao ◽  
Shiwei Peng ◽  
Pengfei Guo ◽  
...  
Keyword(s):  
Motion Planning ◽  
Autonomous Driving ◽  
Local Motion

scholarly journals Motion Planning for Climbing Mobility with Implementation on a Wall-Climbing Robot

2019 ◽  
Author(s):  
Keenan Albee ◽  
Antonio Teran Espinoza ◽  
Kristina Andreyeva ◽  
Nathan Werner ◽  
Howei Chen ◽  
...  
Keyword(s):  
Motion Planning ◽  
Climbing Robot

A Continuous Local Motion Planning Framework for Unmanned Vehicles in Complex Environments

2011 ◽  
Vol 66 (4) ◽  
pp. 477-494 ◽  
Author(s):  
Andrew J. Berry ◽  
Jeremy Howitt ◽  
Da-Wei Gu ◽  
Ian Postlethwaite
Keyword(s):  
Motion Planning ◽  
Unmanned Vehicles ◽  
Complex Environments ◽  
Local Motion ◽  
Planning Framework

NRR: a nonholonomic random replanner for navigation of car-like robots in unknown environments

Robotica ◽  
2014 ◽  
Vol 32 (7) ◽  
pp. 1101-1123 ◽  
Author(s):  
Ellips Masehian ◽  
Hossein Kakahaji
Keyword(s):  
Motion Planning ◽  
Mobile Robots ◽  
Random Tree ◽  
Local Motion ◽  
Goal Region ◽  
Unknown Environments ◽  
Effectiveness And Efficiency ◽  
Path Lengths

SUMMARYIn this paper, a new sensor-based approach called nonholonomic random replanner (NRR) is presented for motion planning of car-like mobile robots. The robot is incrementally directed toward its destination using a nonholonomic rapidly exploring random tree (RRT) algorithm. At each iteration, the robot's perceived map of the environment is updated using sensor readings and is used for local motion planning. If the goal was not visible to the robot, an approximate path toward the goal is calculated and the robot traces it to an extent within its sensor range. The robot updates its motion to goal through replanning. This procedure is repeated until the goal lies within the scope of the robot, after which it finds a more precise path by sampling in a tighter Goal Region for the nonholonomic RRT. Three main replanning strategies are proposed to decide when to perform a visibility scan and when to replan a new path. Those are named Basic, Deliberative and Greedy strategies, which yield different paths. The NRR was also modified for motion planning of Dubin's car-like robots. The proposed algorithm is probabilistically complete and its effectiveness and efficiency were tested by running several simulations and the resulting runtimes and path lengths were compared to the basic RRT method.

Design and motion planning of an autonomous climbing robot with claws

2011 ◽  
Vol 59 (11) ◽  
pp. 1008-1019 ◽  
Author(s):  
Avishai Sintov ◽  
Tomer Avramovich ◽  
Amir Shapiro
Keyword(s):  
Motion Planning ◽  
Climbing Robot
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