Among the emerging concepts in the context of IoT, crowdsourcing, and crowdsensing are
known as two critical building blocks on the intersection point of things and human-based …

We present a learning-based mapless motion planner by taking the sparse 10-dimensional
range findings and the target position with respect to the mobile robot coordinate frame as …

This paper investigates exploration strategies of Deep Reinforcement Learning (DRL)
methods to learn navigation policies for mobile robots. In particular, we augment the normal …

As an infrastructure-free positioning and navigation method, pedestrian dead reckoning
(PDR) is still a research hotspot in the field of indoor localization. Step detection (SD) and …

Facing the intensification of global carbon emissions and the increasingly severe pressure
of environmental pollution, listed companies urgently need to promote green innovation …

Opportunistic signals (eg, WiFi, magnetic fields, and ambient light) have been extensively
studied for low-cost indoor localization, especially via fingerprinting. We present an …

We propose a deep reinforcement learning method based on Double Q-learning Network
(DDQN) to enable mobile robots to learn collision avoidance and navigation capabilities …

WiFi-based indoor localization systems are popular due to the WiFi ubiquity and availability
in commodity smartphone devices for network communication. The majority of these systems …

Due to the sparse rewards and high degree of environmental variation, reinforcement
learning approaches, such as deep deterministic policy gradient (DDPG), are plagued by …

VLC-Based Localization Page 1 This article has been accepted for inclusion in a future issue
of this journal. Content is final as presented, with the exception of pagination. 2 • IEEE …