Study on path planning strategies for search and rescue

2016 ◽  
pp. 937-942
Author(s):  
J Zhang ◽  
A Teixeira ◽  
C Soares ◽  
X Yan
Keyword(s):  
Path Planning ◽  
Search And Rescue ◽  
Planning Strategies

Path planning strategies for laser line forming

2003 ◽  
Author(s):  
Edward Reutzel ◽  
Kevin Gombotz ◽  
Richard Martukanitz ◽  
Panagiotis Michaleris
Keyword(s):  
Path Planning ◽  
Laser Line ◽  
Planning Strategies

Coverage path planning for maritime search and rescue using reinforcement learning

2021 ◽  
Vol 241 ◽  
pp. 110098
Author(s):  
Bo Ai ◽  
Maoxin Jia ◽  
Hanwen Xu ◽  
Jiangling Xu ◽  
Zhen Wen ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Search And Rescue ◽  
Coverage Path Planning ◽  
Maritime Search And Rescue

Autonomous Parking Strategy

2008 ◽  
Author(s):  
Nikolai Moshchuk ◽  
Shih-Ken Chen
Keyword(s):  
Path Planning ◽  
Parking Space ◽  
Novice Drivers ◽  
Parallel Parking ◽  
Parking Spot ◽  
Innovative System ◽  
Planning Strategies ◽  
Vehicle Configuration

Parallel parking can be a difficult task for novice drivers or drivers who seldom drive in congested city where parking space is limited. Parking Assist is an innovative system designed to aid the driver in performing sometimes difficult parallel parking maneuvers. Many companies are developing such systems with major automakers, such as Valeo, Aisin Seiki, Hella, Robert Bosch, and TRW. For example, Toyota IPA (Intelligent Parking Assist) system uses a rear view camera and automatically steer the vehicle into the parking spot with driver controlling braking. This paper describes the development of parking path planning strategies based on available parking space. A virtual turn center will first be defined and derived based on vehicle configuration. Required parking space for one or two cycle parking maneuver will then be determined. Path planning strategies for both one and two turn parking maneuvers will be developed next. Finally CarSim simulation will be performed to verify the design.

Tow Path Planning Strategies for Fiber Steered Laminates

2022 ◽  
pp. 1-13
Author(s):  
Ifat Jahangir ◽  
Darun Barazanchy ◽  
Floris-Jan van Zanten ◽  
Michel van Tooren
Keyword(s):  
Path Planning ◽  
Planning Strategies

scholarly journals Learning Innovative Routes for Mobile Robots in Dynamic Partially Unknown Environments

10.5772/5787 ◽  
2005 ◽  
Vol 2 (3) ◽  
pp. 21
Author(s):  
Kristo Heero ◽  
Alvo Aabloo ◽  
Maarja Kruusmaa
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Shortest Path ◽  
Dynamic Environments ◽  
Distance Transform ◽  
Test Results ◽  
Approach To Learning ◽  
Planning Strategy ◽  
Unknown Environments ◽  
Planning Strategies

This paper examines path planning strategies in partially unknown dynamic environemnts and introduces an approach to learning innovative routes. The approach is verified against shortest path planning with a distance transform algorithm, local and global replanning and suboptimal route following in unknown, partially unknown, static and dynamic environments. We show that the learned routes are more reliable and when traversed repeatedly the robot's behaviour becomes more predictable. The test results also suggest that the robot's behaviour depends on knowledge about the environemnt but not about the path planning strategy used.

On the development of computer bases path planning strategies for Robotic Spray Glazing

1992 ◽  
Vol 23 (1-4) ◽  
pp. 15-18 ◽  
Author(s):  
Vivek Narayanan ◽  
Bopaya Bidanda ◽  
Jacob Rubinovitz
Keyword(s):  
Path Planning ◽  
Planning Strategies

Search and Rescue Robot Path Planning in Unknown Environment

2012 ◽  
Vol 241-244 ◽  
pp. 1682-1687 ◽  
Author(s):  
Tao Pang ◽  
Xiao Gang Ruan ◽  
Er Shen Wang ◽  
Rui Yuan Fan
Keyword(s):  
Path Planning ◽  
Learning Algorithm ◽  
Optimal Path ◽  
Local Environment ◽  
Search And Rescue ◽  
Planning Problem ◽  
Self Organizing Map ◽  
Initial State ◽  
Rescue Robot ◽  
Unknown Environment

For the path planning problem of search and rescue robot in unknown environment, a bionic learning algorithm was proposed. The GSOM (Growing Self-organizing Map) algorithm was used to build the environment cognitive map. The heuristic search A* algorithm was used to find the global optimal path from initial state to target state. When the local environment was changed, reinforcement learning algorithm based on sensor information was used to guide the search and rescue robot behavior of local path planning. Simulation results show the method effectiveness.

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