With the promotion of the strategy "Made in China 2025", as well as the issues of population aging and labor shortage becoming increasingly prominent, the needs to replace manual operations with machines, increase the flexible production capacity of the manufacturing and promote the transformation of traditional manufacturing to smart manufacturing have become increasingly urgent. The industrial manipulator is one of the core components of manufacturing transformation and upgrading, so improving its programming efficiency is the key to speeding up the iteration of the production line and realizing flexible production. Compared with the traditional teaching programming, the offline programming system incorporating intelligent planning algorithms of autonomous obstacle avoidance for manipulators can significantly improve its programming efficiency. However, due to the non-linear and high-degree-of-freedom characteristics of the manipulator and various task constraints in complex environments, obstacle avoidance motion planning of the manipulator faces many problems in industrial applications. Therefore, studying obstacle avoidance motion planning methods of manipulators under various constraints is of great significance to improve the flexible production capacity of manufacturing and realize intelligent manufacturing.
The thesis is supported by the key technology research and industrialization application of robots under Grant No.161100210300, and researches deeply on the key issues such as kinematics modeling, collision detection and distance calculation, and obstacle avoidance motion planning of manipulators, and proposes multiple obstacle avoidance motion planning methods for manipulators with optimization properties. At the same time, the functions of environment perception and collision detection are integrated to develop a 6R industrial manipulator control system with independent intellectual property rights. The main work completed in this topic is as follows:
(1) Considering the problems of high trajectory redundancy and long time consuming for obstacle avoidance motion planning with narrow space constraints, an obstacle avoidance motion planning method based on stagnation detection and shared multi-heuristic A* search is proposed. First, the method designs multiple motion primitives in the configuration space to ensure a complete search; second, construct heuristic guidance in the environment space to improve the guidance efficiency; then, in order to avoid the unnecessary auxiliary guidance to increase the search burden, a shared multi-heuristic A* search mechanism based on stagnation detection is designed, which takes the joint path length as the optimization goal; finally, in order to achieve complete motion planning, a path post-processing method based on backtracking mechanism and cubic polynomial interpolation is studied. Simulation and experimental prove the effectiveness of the method. At the same time, for the special scenario of narrow space, it has obvious advantages in planning efficiency and reducing joint path length.
(2) Considering the problems of low end tracking accuracy and high trajectory redundancy for obstacle avoidance motion planning with end path constraints, a random sampling optimization algorithm based on inverse kinematics of redundant manipulators is proposed. First, the algorithm uses the gradient projection method to expand the nodes, and ensures the probability is complete by random sampling in zero space; second, it uses the gradient reduction method to generate joint trajectories, and introduces a negative feedback mechanism to ensure that the end path constraint is met while achieving high-order smoothing of the joint trajectories; finally, with the joint path length and end tracking accuracy as the optimization goals, an optimized structure is introduced in the process of expanding the random search tree to improve the trajectory quality. Simulation and experimental results show that the algorithm can directly plan a high-order smooth obstacle avoidance trajectory that satisfies the end path constraints, and effectively reduces the trajectory redundancy in joint space.
(3) Considering the problems of randomly giving goal pose and poor planning trajectory quality for the existing algorithms with irregular goal region constraints, an objective optimization algorithm based on Gaussian process trajectory description is proposed. First, the algorithm constructs a symbolic distance field for the irregular goal position region, and derives the corresponding distance calculation method and gradient form; second, for the goal posture constraint, the Lie Group and Lie algebras is used to describe the posture error, and a local linear perturbation model with additive properties is derived; on this basis, the goal region constrained likelihood is defined, and combined with the obstacle constrained likelihood and the Gaussian prior distribution, the planning problem is transformed into a maximum posterior probability problem; finally, numerical optimization is used to quickly plan a smooth obstacle avoidance trajectory. Simulation results show that the algorithm can optimize the end effector to an optimal or suboptimal pose in the goal region, and improve the planned trajectory quality.
(4) Considering the problems of poor replanning trajectory connectivity and low replanning efficiency for obstacle avoidance motion planning under dynamic goal constraints, a beat-time online motion replanning method based on factor graph is proposed. First, the method studies the trajectory optimization algorithm for the goal pose constraints, and describes it as a factor graph structure; then for the unknown dynamic goals, a beat-time online motion replanning algorithm based on iSam2 graph optimizer is studied. Considering the time required for actual goal perception and trajectory replanning, the algorithm introduces a beat-time mechanism and accelerates the replanning speed to improve the success rate of dynamic goal tracking. The simulation results show that this method can realize the obstacle avoidance and dynamic goal tracking, at the same time, perform motion re-planning quickly, and realize the smooth connection of multiple replanning trajectories.
(5) Considering the requirements of autonomous obstacle avoidance motion planning for industrial manipulators, a 6R industrial manipulator control system is developed, including a motion controller, a handheld teach pendant, and an offline programming system based on ROS. By integrating functions such as environmental perception and collision detection into the offline programming system, and combining the planning algorithms studied above, the autonomous obstacle avoidance motion planning of manipulators under given task constraints is realized. The experimental results verify the practicability of the proposed algorithms and the entire control system.