Abstract
Obstacle avoidance is an important subject in the control of robot manipulators, but is remains challenging for robots with redundant degrees of freedom, especially when there exist complex physical constraints. In this paper, we propose a novel controller based on deep recurrent neural networks. By abstracting robots and obstacles into critical point sets respectively, the distance between the robot and obstacles can be described in a simpler way, then the obstacle avoidance strategy is established in form of inequality constraints by general class-K functions. Using minimal-velocity-norm (MVN) scheme, the control problem is formulated as a quadratic-programming case under multiple constraints. Then a deep recurrent neural network considering system models is established to solve the QP problem online. Theoretical conduction and numerical simulations show that the controller is capable of avoiding static or dynamic obstacles, while tracking the predefined trajectories under physical constraints.
Highlights
As industrial automation develops, robot manipulators have been used in a wide range of applications such as painting, welding, assembly, etc., (Cheng et al, 2009; Yang et al, 2018a)
Collaboration is required between human or other robots, as a result, the obstacle avoidance control is becoming a matter of urgency: robots need to achieve real-time avoidance of static or dynamic obstacles while completing given motion tasks
Motivated by the above observations, in this paper, we proposed a novel obstacle avoidance strategy based on deep recurrent neural networks
Summary
Robot manipulators have been used in a wide range of applications such as painting, welding, assembly, etc., (Cheng et al, 2009; Yang et al, 2018a). With the evolution of intelligent manufacturing, the way robot works is changing. In order to fulfill more difficult tasks in complex environment, the robot is required to have better execution capabilities (Pan et al, 2018). Obstacle avoidance is a core problem in the control of redundant manipulators, in order to realize human-machine collaboration and integration, robots no longer work in a separate environment that is completely isolated (Ge and Cui, 2000; Sugie et al, 2003; Lee and Buss, 2007). Collaboration is required between human or other robots, as a result, the obstacle avoidance control is becoming a matter of urgency: robots need to achieve real-time avoidance of static or dynamic obstacles while completing given motion tasks
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