In robotics, contemporary strategies are learning-based, characterized by a complex black-box nature and a lack of interpretability, which may pose challenges in ensuring stability and safety. To address these issues, we propose integrating a collision-free trajectory planner based on deep reinforcement learning (DRL) with a novel auto-tuning low-level control strategy, all while actively engaging in the learning phase through interactions with the environment. This approach circumvents the control performance and complexities associated with computations while addressing nonrepetitive reaching tasks in the presence of obstacles. First, a model-free DRL agent is employed to plan velocity-bounded motion for a manipulator with 'n' degrees of freedom (DoF), ensuring collision avoidance for the end-effector through joint-level reasoning. The generated reference motion is then input into a robust subsystem-based adaptive controller, which produces the necessary torques, while the cuckoo search optimization (CSO) algorithm enhances control gains to minimize the stabilization and tracking error in the steady state. This approach guarantees robustness and uniform exponential convergence in an unfamiliar environment, despite the presence of uncertainties and disturbances. Theoretical assertions are validated through the presentation of simulation outcomes.

翻译：在机器人领域，当前策略多基于学习模型，具有复杂的黑箱特性且缺乏可解释性，可能对确保系统稳定性与安全性构成挑战。针对上述问题，我们提出将基于深度强化学习（DRL）的无碰撞轨迹规划器与新型自整定低层控制策略相结合，并在学习阶段通过环境交互主动参与优化过程。该方法在规避控制性能与计算复杂度问题的同时，解决了存在障碍物的非重复性目标触及任务。首先，采用无模型DRL智能体为具有n自由度的操作器规划速度受限运动，通过关节级推理确保末端执行器的避碰能力。生成的参考运动随后输入至基于子系统的鲁棒自适应控制器以产生所需力矩，同时采用布谷鸟搜索优化（CSO）算法优化控制增益，最小化稳态下的稳定与跟踪误差。即使存在不确定性与扰动，该方法仍能保证在陌生环境中的鲁棒性与一致指数收敛性。通过仿真结果验证了理论论断的有效性。