Self-configuring robot path planning with obstacle avoidance via deep reinforcement learning
B Sangiovanni, GP Incremona… - IEEE Control Systems …, 2020 - ieeexplore.ieee.org
IEEE Control Systems Letters, 2020•ieeexplore.ieee.org
This letter proposes a hybrid control methodology to achieve full body collision avoidance in
anthropomorphic robot manipulators. The proposal improves classical motion planning
algorithms by introducing a Deep Reinforcement Learning (DRL) approach trained ad hoc
for performing obstacle avoidance, while achieving a reaching task in the operative space.
More specifically, a switching mechanism is enabled whenever a condition of proximity to
the obstacles is met, thus conferring to the dual-mode architecture a self-configuring …
anthropomorphic robot manipulators. The proposal improves classical motion planning
algorithms by introducing a Deep Reinforcement Learning (DRL) approach trained ad hoc
for performing obstacle avoidance, while achieving a reaching task in the operative space.
More specifically, a switching mechanism is enabled whenever a condition of proximity to
the obstacles is met, thus conferring to the dual-mode architecture a self-configuring …
This letter proposes a hybrid control methodology to achieve full body collision avoidance in anthropomorphic robot manipulators. The proposal improves classical motion planning algorithms by introducing a Deep Reinforcement Learning (DRL) approach trained ad hoc for performing obstacle avoidance, while achieving a reaching task in the operative space. More specifically, a switching mechanism is enabled whenever a condition of proximity to the obstacles is met, thus conferring to the dual-mode architecture a self-configuring capability in order to cope with objects unexpectedly invading the workspace. The proposal has been finally tested relying on a realistic robot manipulator simulated in a V-REP environment.
ieeexplore.ieee.org
以上显示的是最相近的搜索结果。 查看全部搜索结果