[8] proposed an improved APF method to solve the problems of large swinging trajectories and easily falling into local minima that are encountered by the classic APF method in unmanned aerial vehicle (UAV) trajectory planning. [PERSON] et al. [9] proposed a path planning method for multiple underwater unmanned vehicles (UUVs) in a three-dimensional environment based on the \"domain\", which solves the disadvantages of unreachable targets near obstacles, local minima, and oscillations encountered in the classic APF method. [PERSON] et al. [10] proposed an overall configuration planning method of continuum hyper-redundant manipulators (CHRMs) based on an improved APF method that avoids complicated inverse kinematics and vastly reduces the computational complexity. [PERSON] et al. [11] proposed a path planning method for mobile mechanical arms based on the sparse node RRT algorithm that solves the problem of excessively searching in the local space and reduces the number of invalid nodes. [PERSON] et al. [12] proposed a free-floating space robot (FFSR) trajectory planning method based on the dynamic RRT* algorithm, which can rapidly generate a feasible robot movement trajectory. [PERSON] et al. [13] proposed a 1-0 goal-bias RRT algorithm to reduce the computational time and complexity, even in complex environments. [PERSON] et al. [14] proposed the potential function-based RRT* (P-RRT*) method by incorporating the APF algorithm into the RRT* method. The proposed algorithm allows a considerable decrease in the number of iterations and thus leads to more efficient memory utilization and an accelerated convergence rate. [PERSON] et al. [15] proposed Quick-RRT* (Q-RRT*), a modified RRT* algorithm that generates a better initial solution and converges to the optimal solution faster than RRT*. Q-RRT* enlarges the set of possible parent vertices by considering not only a set of vertices contained in a hyper-sphere, as in RRT*, but also their ancestry up to a user-defined parameter, thus resulting in paths with less cost than those of RRT*. [PERSON] et al. [16] proposed a novel learning-based multi-RRT (LM-RRT) approach for robot path planning in narrow passages. The LM-RRT approach models the tree selection process as a multi-armed bandit problem and uses a reinforcement learning algorithm that learns action values and selects actions with an improved epsilon-greedy (epsilon (t)-greedy) strategy. [PERSON] et al. [17] proposed a motion planning algorithm by exploiting RRT stars (RRT stars) and dynamic movement primitives (DMPs). [PERSON] et al. [18] proposed a Dubins-RRT* algorithm to involve the construction of a recovery path for an agricultural mobile robot (AMR) under kinematic constraints. The planned path avoids obstacles and incurs the minimum cost from a rendezvous point to the recovery platform. However, in general, the problems in the above research are as follows. (1) The improved APF algorithms cannot readily solve the problem of local minima in the search process, and the local minima cannot be adjusted in a timely manner. In addition, when there are obstacles at both the target point and the starting point, the obstacles cannot be effectively avoided to reach the target point quickly, and the generated path is relatively tortuous. (2) The improved RRT algorithms cannot quickly find a reliable path in a complex,multi-obstacle environment. Moreover, the algorithm cannot be adjusted well for different environmental conditions, which is not conducive to improving the algorithm efficiency, and the generated path is not sufficiently smooth, causing the mechanical arm to undergo impacts and become damaged in actual operation and severely shortening the service life of the mechanical arm. In response to the above problems, the present paper proposes an improved hybrid three-dimensional path planning algorithm for mechanical arms that combines the APF method and the RRT algorithm. The proposed algorithm is used to solve the path planning problem of the manipulator in an environment with complex obstacles.