scholarly journals Experimental Analysis of Receding Horizon Planning Algorithms for Marine Monitoring

2016 ◽  
pp. 31-44 ◽  
Author(s):  
Soo-Hyun Yoo ◽  
Andrew Stuntz ◽  
Yawei Zhang ◽  
Robert Rothschild ◽  
Geoffrey A. Hollinger ◽  
...  
Keyword(s):  
Experimental Analysis ◽  
Receding Horizon ◽  
Marine Monitoring ◽  
Planning Algorithms ◽  
Horizon Planning

Safe, Aggressive Quadrotor Flight via Reachability-Based Trajectory Design

2019 ◽  
Author(s):  
Shreyas Kousik ◽  
Patrick Holmes ◽  
Ram Vasudevan
Keyword(s):  
Tracking Error ◽  
Search And Rescue ◽  
The Other ◽  
Trajectory Design ◽  
Limited Information ◽  
Receding Horizon ◽  
Cluttered Environments ◽  
Online Planning ◽  
Infrastructure Inspection ◽  
Horizon Planning

Abstract Quadrotors can provide services such as infrastructure inspection and search-and-rescue, which require operating autonomously in cluttered environments. Autonomy is typically achieved with receding-horizon planning, where a short plan is executed while a new one is computed, because sensors receive limited information at any time. To ensure safety and prevent robot loss, plans must be verified as collision free despite uncertainty (e.g, tracking error). Existing spline-based planners dilate obstacles uniformly to compensate for uncertainty, which can be conservative. On the other hand, reachability-based planners can include trajectory-dependent uncertainty as a function of the planned trajectory. This work applies Reachability-based Trajectory Design (RTD) to plan quadrotor trajectories that are safe despite trajectory-dependent tracking error. This is achieved by using zonotopes in a novel way for online planning. Simulations show aggressive flight up to 5 m/s with zero crashes in 500 cluttered, randomized environments.

scholarly journals Path Planning for Automatic Guided Vehicles (AGVs) Fusing MH-RRT with Improved TEB

Actuators ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 314
Author(s):  
Jiayi Wang ◽  
Yonghu Luo ◽  
Xiaojun Tan
Keyword(s):  
Path Planning ◽  
Dynamic Environments ◽  
Receding Horizon ◽  
Elastic Band ◽  
Automatic Guided Vehicles ◽  
Planning Procedure ◽  
Local Path ◽  
Horizon Planning ◽  
State Tree ◽  
Short Time

In this paper, an AGV path planning method fusing multiple heuristics rapidly exploring random tree (MH-RRT) with an improved two-step Timed Elastic Band (TEB) is proposed. The modified RRT integrating multiple heuristics can search a safer, optimal and faster converge global path within a short time, and the improved TEB can optimize both path smoothness and path length. The method is composed of a global path planning procedure and a local path planning procedure, and the Receding Horizon Planning (RHP) strategy is adopted to fuse these two modules. Firstly, the MH-RRT is utilized to generate a state tree structure as prior knowledge, as well as the global path. Then, a receding horizon window is established to select the local goal point. On this basis, an improved two-step TEB is designed to optimize the local path if the current global path is feasible. Various simulations both on static and dynamic environments are conducted to clarify the performance of the proposed MH-RRT and the improved two-step TEB. Furthermore, real applicative experiments verified the effectiveness of the proposed approach.

A Recursive Receding Horizon Planning for Unmanned Vehicles

2015 ◽  
Vol 62 (5) ◽  
pp. 2912-2920 ◽  
Author(s):  
Bin Zhang ◽  
Liang Tang ◽  
Jonathan DeCastro ◽  
Michael J. Roemer ◽  
Kai Goebel
Keyword(s):  
Unmanned Vehicles ◽  
Receding Horizon ◽  
Horizon Planning

scholarly journals Learning image-based Receding Horizon Planning for manipulation in clutter

2021 ◽  
Vol 138 ◽  
pp. 103730
Author(s):  
Wissam Bejjani ◽  
Matteo Leonetti ◽  
Mehmet R. Dogar
Keyword(s):  
Receding Horizon ◽  
Horizon Planning
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