Visual identification and similarity measures used for on-line motion planning of autonomous robots in unknown environments

2017 ◽  
Author(s):  
Fredy Martínez ◽  
Fernando Martínez ◽  
Edwar Jacinto
Keyword(s):  
Motion Planning ◽  
Autonomous Robots ◽  
Similarity Measures ◽  
Visual Identification ◽  
Unknown Environments ◽  
On Line

scholarly journals On-line Elastic Similarity Measures for time series

2019 ◽  
Vol 88 ◽  
pp. 506-517 ◽  
Author(s):  
Izaskun Oregi ◽  
Aritz Pérez ◽  
Javier Del Ser ◽  
Jose A. Lozano
Keyword(s):  
Time Series ◽  
Similarity Measures ◽  
On Line

scholarly journals Deep Reinforcement Learning Approach with Multiple Experience Pools for UAV’s Autonomous Motion Planning in Complex Unknown Environments

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1890 ◽  
Author(s):  
Zijian Hu ◽  
Kaifang Wan ◽  
Xiaoguang Gao ◽  
Yiwei Zhai ◽  
Qianglong Wang
Keyword(s):  
Reinforcement Learning ◽  
Motion Planning ◽  
Predictive Control ◽  
Experimental Testing ◽  
The Novel ◽  
Simulation Environment ◽  
Unknown Environments ◽  
Autonomous Motion ◽  
Policy Gradient ◽  
Speed Up

Autonomous motion planning (AMP) of unmanned aerial vehicles (UAVs) is aimed at enabling a UAV to safely fly to the target without human intervention. Recently, several emerging deep reinforcement learning (DRL) methods have been employed to address the AMP problem in some simplified environments, and these methods have yielded good results. This paper proposes a multiple experience pools (MEPs) framework leveraging human expert experiences for DRL to speed up the learning process. Based on the deep deterministic policy gradient (DDPG) algorithm, a MEP–DDPG algorithm was designed using model predictive control and simulated annealing to generate expert experiences. On applying this algorithm to a complex unknown simulation environment constructed based on the parameters of the real UAV, the training experiment results showed that the novel DRL algorithm resulted in a performance improvement exceeding 20% as compared with the state-of-the-art DDPG. The results of the experimental testing indicate that UAVs trained using MEP–DDPG can stably complete a variety of tasks in complex, unknown environments.

scholarly journals A new effective algorithm for on-line robot motion planning

2013 ◽  
pp. 121-130 ◽  
Author(s):  
Hassan Jafarzadeh ◽  
Saiedeh Gholami ◽  
Reza Bashirzadeh
Keyword(s):  
Motion Planning ◽  
Robot Motion ◽  
Robot Motion Planning ◽  
Effective Algorithm ◽  
On Line

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.

On-line motion planning: Case of a planar rod

1991 ◽  
Vol 3 (1) ◽  
pp. 1-20 ◽  
Author(s):  
James Cox ◽  
Chee-Keng Yap
Keyword(s):  
Motion Planning ◽  
On Line
Sign in / Sign up

Export Citation Format

Share Document