Reinforcement learning in robotic applications: a comprehensive survey
In recent trends, artificial intelligence (AI) is used for the creation of complex automated
control systems. Still, researchers are trying to make a completely autonomous system that …
control systems. Still, researchers are trying to make a completely autonomous system that …
Review of deep reinforcement learning for robot manipulation
Reinforcement learning combined with neural networks has recently led to a wide range of
successes in learning policies in different domains. For robot manipulation, reinforcement …
successes in learning policies in different domains. For robot manipulation, reinforcement …
Obstacle avoidance and tracking control of redundant robotic manipulator: An RNN-based metaheuristic approach
In this article, we present a metaheuristic-based control framework, called beetle antennae
olfactory recurrent neural network, for simultaneous tracking control and obstacle avoidance …
olfactory recurrent neural network, for simultaneous tracking control and obstacle avoidance …
Convergent multiagent formation control with collision avoidance
J Hu, H Zhang, L Liu, X Zhu, C Zhao… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
A key problem in the formation control of homogeneous multiagent systems is the collision-
free convergence of the agent positions into a desired formation. It is a typical NP-hard …
free convergence of the agent positions into a desired formation. It is a typical NP-hard …
A Q-learning approach to flocking with UAVs in a stochastic environment
SM Hung, SN Givigi - IEEE transactions on cybernetics, 2016 - ieeexplore.ieee.org
In the past two decades, unmanned aerial vehicles (UAVs) have demonstrated their efficacy
in supporting both military and civilian applications, where tasks can be dull, dirty …
in supporting both military and civilian applications, where tasks can be dull, dirty …
Autonomous uav navigation using reinforcement learning
Unmanned aerial vehicles (UAV) are commonly used for missions in unknown
environments, where an exact mathematical model of the environment may not be available …
environments, where an exact mathematical model of the environment may not be available …
A distributed control framework of multiple unmanned aerial vehicles for dynamic wildfire tracking
Wild-land fire fighting is a hazardous job. A key task for firefighters is to observe the “fire
front” to chart the progress of the fire and areas that will likely spread next. Lack of …
front” to chart the progress of the fire and areas that will likely spread next. Lack of …
Modified primal-dual neural networks for motion control of redundant manipulators with dynamic rejection of harmonic noises
In recent decades, primal-dual neural networks, as a special type of recurrent neural
networks, have received great success in real-time manipulator control. However, noises are …
networks, have received great success in real-time manipulator control. However, noises are …
Dynamic task allocation in multi-robot coordination for moving target tracking: A distributed approach
A new coordination control is developed in this paper for multiple non-holonomic robots in a
competitive manner for target tracking with limited communications. In this proposed control …
competitive manner for target tracking with limited communications. In this proposed control …
Cooperative motion generation in a distributed network of redundant robot manipulators with noises
In this paper, a distributed scheme is proposed for the cooperative motion generation in a
distributed network of multiple redundant manipulators. The proposed scheme can …
distributed network of multiple redundant manipulators. The proposed scheme can …