Real Time Path Planning of Robot using Deep Reinforcement Learning
| Autor: | Ramkrishna Pasumarthy, Bharath Bhikkaji, Jayadev P. Satya, P V Srihari, Jeevan Raajan |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Computer science 020208 electrical & electronic engineering Sampling (statistics) 02 engineering and technology 020901 industrial engineering & automation Control and Systems Engineering Position (vector) Path (graph theory) 0202 electrical engineering electronic engineering information engineering Reinforcement learning Robot State (computer science) Motion planning Finite set Algorithm |
| Zdroj: | IFAC-PapersOnLine. 53:15602-15607 |
| ISSN: | 2405-8963 |
| DOI: | 10.1016/j.ifacol.2020.12.2494 |
| Popis: | This paper considers finding a path in real time for a robot from the given initial position to the goal position. The environment is assumed to be mapped (known completely) and the resulting path should avoid all the obstacles, both static and dynamic in the mapped environment. The robot’s (agent) dynamics is assumed to be discrete LTI with process noise and is controlled with a finite set of inputs. An MDP formulation and a solution based on Deep Reinforcement Learning framework are presented for the problem. Numerical experiments are performed for the proposed method using Deep Q-Network algorithm and the results are compared with the state of the art sampling based path planning algorithms for both static and dynamic environments. It is shown that even though the proposed algorithm provides a sub-optimal path, the computational time is shown to be significantly faster compared to the traditional methods of path planning. |
| Databáze: | OpenAIRE |
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