## 1 Introduction  
Trajectory planning is the basis of robot motion control and is related to the stability of robot motion, work efficiency, energy consumption, etc. [1; 2; 3; 4]. Robot trajectory planning determining the trajectory of the end movement of a robotic arm to meet process requirements and ensure high performance links the space and time domains of the robot in the process of movement [5]. Optimized trajectory planning can help meet process requirements, reduce quick jerk and improve construction quality to enable smooth motion. For example, [PERSON] et al. [6] used a non-dominated neighborhood immune genetic algorithm to optimize the multi-objective trajectory function for a robot. They determined the optimal position, velocity, acceleration and acceleration planning curves for each joint of the robot. [PERSON] et al. [7] used five-order B-spline interpolation to interpolate the trajectory in the joint space. They then used an elite non-dominated ranking genetic algorithm (NSGA-II) to optimize the two objectives--i.e., motion time and average acceleration--for the entire trajectory. [PERSON] et al. [8] carried out time-jerk optimal trajectory planning for an excavator using an SQP algorithm with the constraints of joint angular velocity, angular acceleration and angular acceleration by interpolating in the joint space using three-order B-spline interpolation. [PERSON] et al. [9] proposed a method based on a segmented polynomial interpolation function and the local chaotic particle swarm optimization (LCPSO) algorithm to achieve robotic-arm time optimality. Different trajectory planning methods can achieve different planning effects, and among the many planning algorithms, the B-spline curve [10] has outstanding advantages: its trajectory is smooth and can guarantee velocity, acceleration and acceleration continuity simultaneously; however, it requires substantial computational power compared to other methods. At the same time, single optimal trajectory planning methods cannot meet the comprehensive requirements of modern industrial applications. The integrated consideration of two or more optimality methods is more suitable for trajectory planning in practical applications [11]. Complex planning methods and multi-objective optimization require large amounts of computational power and consideration of the complex constraints brought about by actual working conditions, all of which place higher demands on the accuracy and speed with which the solution is obtained in the optimization algorithm. Chicken swarm optimization (CSO), proposed by [PERSON] et al. [12], is a novel swarm intelligence optimization algorithm. It can be used to optimize various specific problems by simulating the intelligent behavior of a swarm composed of roosters, hens and chicks foraging for food. The chicken swarm optimization algorithm has a clear structure and robust searching ability, as well as being easy to understand and implement. Additionally, it has better convergence than the particle swarm algorithm, differential evolution algorithm and artificial bee swarm algorithm [12]. It has several research applications, such as task scheduling, route optimization, etc. [13], but suffers from the problem of falling into local optimality, leading to premature maturity and a low solution accuracy. To address the issues of the CSO algorithm, researchers have proposed improvement methods. [PERSON] et al. [14] proposed a multi-stage CSO algorithm based on the search characteristics of different chickens. In the first stage, the algorithm focuses on the hen update method to strengthen the global exploration ability; in the second stage, the algorithm focuses on the chick and rooster update methods to reflect the advantage of local exploitation.