Motion Planning In Environments Research Articles

Deep-Reinforcement-Learning-Based Motion Planning for a Wide Range of Robotic Structures

The use of robot manipulators in engineering applications and scientific research has significantly increased in recent years. This can be attributed to the rise of technologies such as autonomous robotics and physics-based simulation, along with the utilization of artificial intelligence techniques. The use of these technologies may be limited due to a focus on a specific type of robotic manipulator and a particular solved task, which can hinder modularity and reproducibility in future expansions. This paper presents a method for planning motion across a wide range of robotic structures using deep reinforcement learning (DRL) algorithms to solve the problem of reaching a static or random target within a pre-defined configuration space. The paper addresses the challenge of motion planning in environments under a variety of conditions, including environments with and without the presence of collision objects. It highlights the versatility and potential for future expansion through the integration of OpenAI Gym and the PyBullet physics-based simulator.

Vector Field-based Collision Avoidance for Moving Obstacles with Time-Varying Elliptical Shape

This paper presents an algorithm for local motion planning in environments populated by moving elliptical obstacles whose velocity, shape and size are fully known but may change with time. We base the algorithm on a collision avoidance vector field (CAVF) that aims to steer an agent to a desired final state whose motion is described by a double integrator kinematic model. In addition to handling multiple obstacles, the method is applicable in bounded environments for more realistic applications (e.g., motion planning inside a building). We also incorporate a method to deal with agents whose control input is limited so that they safely navigate around the obstacles. To showcase our approach, extensive simulations results are presented in 2D and 3D scenarios.