Target localization using multi-agent deep reinforcement learning with proximal policy optimization
Target localization refers to identifying a target location based on sensory data readings
gathered by sensing agents (robots, UAVs), surveying a certain area of interest. Existing …
gathered by sensing agents (robots, UAVs), surveying a certain area of interest. Existing …
Multi-agent deep reinforcement learning with demonstration cloning for target localization
In target localization applications, readings from multiple sensing agents are processed to
identify a target location. The localization systems using stationary sensors use data fusion …
identify a target location. The localization systems using stationary sensors use data fusion …
Reinforcement learning framework for UAV-based target localization applications
Smart environmental monitoring has gained prominence, where target localization is of
utmost importance. Employing UAVs for localization tasks is appealing owing to their low …
utmost importance. Employing UAVs for localization tasks is appealing owing to their low …
Iot sensor selection for target localization: A reinforcement learning based approach
Internet of things (IoT) is a key enabler for target localization, where IoT-based sensors work
towards identifying target's location in an area of interest (AoI). Appropriate selection of IoT …
towards identifying target's location in an area of interest (AoI). Appropriate selection of IoT …
Autonomous UAV trajectory for localizing ground objects: A reinforcement learning approach
Disaster management, search and rescue missions, and health monitoring are examples of
critical applications that require object localization with high precision and sometimes in a …
critical applications that require object localization with high precision and sometimes in a …
A deep learning framework for target localization in error-prone environment
The use of Internet of Things (IoT) in environment monitoring has led to the development of
Smart Environmental Monitoring (SEM) paradigm. Target or source localization, that …
Smart Environmental Monitoring (SEM) paradigm. Target or source localization, that …
Autonomous navigation of UAVs in large-scale complex environments: A deep reinforcement learning approach
In this paper, we propose a deep reinforcement learning (DRL)-based method that allows
unmanned aerial vehicles (UAVs) to execute navigation tasks in large-scale complex …
unmanned aerial vehicles (UAVs) to execute navigation tasks in large-scale complex …
End-to-end deep reinforcement learning for multi-agent collaborative exploration
Exploring an unknown environment by multiple autonomous robots is a major challenge in
robotics domains. As multiple robots are assigned to explore different locations, they may …
robotics domains. As multiple robots are assigned to explore different locations, they may …
[HTML][HTML] Deep reinforcement learning for drone navigation using sensor data
Mobile robots such as unmanned aerial vehicles (drones) can be used for surveillance,
monitoring and data collection in buildings, infrastructure and environments. The importance …
monitoring and data collection in buildings, infrastructure and environments. The importance …
Self-learning exploration and mapping for mobile robots via deep reinforcement learning
Mapping and exploration ofa prioriunknown environments is a crucial capability for mobile
robot autonomy. A state-of-the-art approach for mobile robots equipped with range sensors …
robot autonomy. A state-of-the-art approach for mobile robots equipped with range sensors …