Continuum robotic arms present themselves as a promising alternative to rigid robotic arms, offering benefits in terms of their flexibility and ability to adapt to complex workspaces. However, for these high degree-of-freedom (DoF) continuum robotic arms to be widely utilized in intricate environments with multiple obstacles, the development of an efficient inverse kinematics solver is crucial. Current inverse kinematics methods, like the Jacobian method, encounter difficulties in terms of computational cost and ensuring solutions on high DoF continuum robotic arms, especially in intricate workspaces with numerous obstacles. To overcome these challenges, we propose an obstacle aided approach to solving the inverse kinematics problem for continuum robotic arms. Our approach integrates an optimization-based path planning method with an artificial obstacle potential field and an evolution process, which incorporates fitting curves using arc splines to generate feasible solutions in intricate workspaces. Through simulations, we demonstrate the flexibility, efficiency, and robust performance of our method for increasing DoF and navigating highly unstructured workspaces filled with multiple obstacles.