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The Manipulator Song Free Download
In actual welding scenarios, an effective path planner is needed to find a collision-free path in the configuration space for the welding manipulator with obstacles around. However, as a state-of-the-art method, the sampling-based planner only satisfies the probability completeness and its computational complexity is sensitive with state dimension. In this paper, we propose a path planner for welding manipulators based on deep reinforcement learning for solving path planning problems in high-dimensional continuous state and action spaces. Compared with the sampling-based method, it is more robust and is less sensitive with state dimension. In detail, to improve the learning efficiency, we introduce the inverse kinematics module to provide prior knowledge while a gain module is also designed to avoid the local optimal policy, we integrate them into the training algorithm. To evaluate our proposed planning algorithm in multiple dimensions, we conducted multiple sets of path planning experiments for welding manipulators. The results show that our method not only improves the convergence performance but also is superior in terms of optimality and robustness of planning compared with most other planning algorithms.
In the field of welding, the term path planning may be formulated as a constrained Traveling Salesman Problem (TSP) [3] while in this paper we study a new method to find an elegant collision-free path in configuration space of the welding manipulator which connects the initial configuration and the target configuration [4].
In this section, we give an overview of the background theories for the proposed Deep-RL-based collision-free path planner, including the kinematics modeling of a welding manipulator used in this research, the Sequential Decision-Making model, and the model-free Deep-RL algorithm: DDPG.
Task analysis is the soul of state design. In this research, the goal of the collision-free path planning task is to train an agent that drives the joints of the welding manipulator without any collision to minimize the distance between the end-effector of the welding manipulator and its target position. In the light of the given task, the state observed from the environment at time step t is designed as a stacked vector:
To be honest, training a policy model for a path planning problem from scratch using the DDPG algorithm is irrational. Thus, in this paper, we aim to speed up the DDPG training process for the collision-free path planning problem via a hybrid method of DDPG and inverse kinematics of the welding manipulator as shown in Fig. 3.
Second, we need to confirm whether the final deterministic policy model mentioned above can be used to generate a collision-free path that is a solution to the respective specified task. The collision-free path in configuration space of the welding manipulator can be obtained indirectly by way of sampling a trajectory \(\varvecs_0,\varveca_0,\varvecs_1,\ldots ,\varveca_T,\varvecs_T\) using the final policy model and then construct the solution \(\left\ \varvecq_i\right\ \), \(i=0,1,\ldots ,T\) from \(\left\ \varvecs_i\right\ \) where \(\varvecs_0\) is equivalent to the state when the welding manipulator is at the configuration of \(\varvecq_s\). Consistent with the previous assumption, the policy model trained by our method described in Algorithm 3 which employs both the gain module \(\mathcal G(t)=\ln (t+1)\) and inverse kinematic module can generate a unique collision-free path in the configuration space which fulfills respective task requirements. We render the path intuitively and succinctly in the simulator CoppeliaSim as Fig. 6 shows.
In this research, we propose a hybrid algorithm of DDPG and Inverse kinematics as an alternative global and single-query path planning algorithm for industry welding manipulator. Simulation results in high-fidelity virtual scenario show that our improvements to the original algorithm DDPG have greatly increased its convergence performance and convergence speed and the well-trained policy model is able to generate a collision-free path for a specified task. In industry robot systems like automatic welding robot systems that require extremely high stability and safety, our algorithm may be competitive comparing with other sampling-based algorithms because the sampling-based algorithm can not guarantee 100\(\%\) success rate of planning. Furthermore, our algorithm can also produce path planning results comparable to those of optimality or progressive optimality algorithms in terms of path length. In summary, our algorithm provides an alternative solution for teaching-free automatic path planning of actual welding robots or other industrial robots. 2ff7e9595c
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