Multi-target pursuit by a decentralized heterogeneous uav swarm using deep multi-agent reinforcement learning
M Kouzeghar, Y Song, M Meghjani… - … on Robotics and …, 2023 - ieeexplore.ieee.org
Multi-agent pursuit-evasion tasks involving intelligent targets are notoriously challenging
coordination problems. In this paper, we investigate new ways to learn such coordinated …
coordination problems. In this paper, we investigate new ways to learn such coordinated …
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 …
Multitask learning for object localization with deep reinforcement learning
In object localization, methods based on a top-down search strategy that focus on learning a
policy have been widely researched. The performance of these methods relies heavily on …
policy have been widely researched. The performance of these methods relies heavily on …
[HTML][HTML] A deep multi-agent reinforcement learning framework for autonomous aerial navigation to grasping points on loads
Deep reinforcement learning, by taking advantage of neural networks, has made great
strides in the continuous control of robots. However, in scenarios where multiple robots are …
strides in the continuous control of robots. However, in scenarios where multiple robots are …
Hybrid localization using model-and learning-based methods: Fusion of Monte Carlo and E2E localizations via importance sampling
This paper proposes a hybrid localization method that fuses Monte Carlo localization (MCL)
and convolutional neural network (CNN)-based end-to-end (E2E) localization. MCL is based …
and convolutional neural network (CNN)-based end-to-end (E2E) localization. MCL is based …
Multiagent path finding using deep reinforcement learning coupled with hot supervision contrastive loss
Multiagent path finding (MAPF) is employed to find collision-free paths to guide agents
traveling from an initial to a target position. The advanced decentralized approach utilizes …
traveling from an initial to a target position. The advanced decentralized approach utilizes …
A behavior-based mobile robot navigation method with deep reinforcement learning
Deep reinforcement learning-based mobile robot navigation has attracted some recent
interest. In the single-agent case, a robot can learn to navigate autonomously without a map …
interest. In the single-agent case, a robot can learn to navigate autonomously without a map …
Multi-agent collaborative exploration through graph-based deep reinforcement learning
Autonomous exploration by a single or multiple agents in an unknown environment leads to
various applications in automation, such as cleaning, search and rescue, etc. Traditional …
various applications in automation, such as cleaning, search and rescue, etc. Traditional …
Hierarchical multi-robot navigation and formation in unknown environments via deep reinforcement learning and distributed optimization
L Chang, L Shan, W Zhang, Y Dai - Robotics and Computer-Integrated …, 2023 - Elsevier
Compared with a single robot, Multi-robot Systems (MRSs) can undertake more challenging
tasks in complex scenarios benefiting from the increased transportation capacity and fault …
tasks in complex scenarios benefiting from the increased transportation capacity and fault …
Localization with sampling-argmax
Soft-argmax operation is commonly adopted in detection-based methods to localize the
target position in a differentiable manner. However, training the neural network with soft …
target position in a differentiable manner. However, training the neural network with soft …