DeepAI AI Chat
Log In Sign Up

A Reinforcement Learning based Path Planning Approach in 3D Environment

by   Geesara Kulathunga, et al.

Optimal trajectory planning involves obstacles avoidance in which path planning is the key to success in optimal trajectory planning. Due to the computational demands, most of the path planning algorithms can not be employed for real-time based applications. Model-based Reinforcement Learning approaches for path planning got certain success in the recent past. Yet, most of such approaches do not have deterministic output due to the nature of those approaches. We analyzed several types of reinforcement learning-based approaches for path planning. One of them is a deterministic tree-based approach and the other two approaches are based on Q-learning and approximate policy gradient, respectively. We tested preceding approaches on two different type of simulators. Each of which consists of a set of random obstacles which could be changed or moved dynamically. After analysing the result and computation time, we concluded that the deterministic tree search approach provides a highly accurate result. However, the computational time is considerably higher than the other two approaches. Finally, the comparative results are provided in terms of accuracy and computational time as evidence.


Model predictive approach to integrated path planning and tracking for autonomous vehicles

In the path planning problem of autonomous application, the existing stu...

New Auction Algorithms for Path Planning, Network Transport, and Reinforcement Learning

We consider some classical optimization problems in path planning and ne...

Two-Step Online Trajectory Planning of a Quadcopter in Indoor Environments with Obstacles

This paper presents a two-step algorithm for online trajectory planning ...

Generation of Paths in a Maze using a Deep Network without Learning

Trajectory- or path-planning is a fundamental issue in a wide variety of...

Can Euclidean Symmetry be Leveraged in Reinforcement Learning and Planning?

In robotic tasks, changes in reference frames typically do not influence...

Adaptive Trajectory Estimation with Power Limited Steering Model under Perturbation Compensation

Trajectory estimation under regional correlations is applied in numerous...

Multi-Agent Path Planning based on MPC and DDPG

The problem of mixed static and dynamic obstacle avoidance is essential ...