Mobile robots contributed significantly to the intelligent development of human society, and
the motion-planning policy is critical for mobile robots. This paper reviews the methods …

In this paper, we propose a model-free reinforcement learning method to synthesize control
policies for mobile robots modeled as Markov Decision Process (MDP) with unknown …

Deep reinforcement learning has great potential to acquire complex, adaptive behaviors for
autonomous agents automatically. However, the underlying neural network polices have not …

Deep reinforcement learning (RL) is being actively studied for robot navigation due to its
promise of superior performance and robustness. However, most existing deep RL …

This paper presents a sensor-level mapless collision avoidance algorithm for use in mobile
robots that map raw sensor data to linear and angular velocities and navigate in an …

Path planning is one of the key technologies for mobile robot applications. However, the
traditional robot path planner has a slow planning response, which leads to a long …

We propose to predict the future trajectories of observed agents (eg, pedestrians or vehicles)
by estimating and using their goals at multiple time scales. We argue that the goal of a …

In this letter, we present an autonomous navigation system for goal-driven exploration of
unknown environments through deep reinforcement learning (DRL). Points of interest (POI) …

This paper presents an improved deep deterministic policy gradient algorithm based on a
six-DOF (six multi-degree-of-freedom) arm robot. First, we build a robot model based on the …

An important challenge for air–ground unmanned systems achieving autonomy is
navigation, which is essential for them to accomplish various tasks in unknown …