Research On Trajectory Planning Of Robotic Arms Based On Energy Consumption Optimisation

The 14 th Five-Year Plan for the Development of the Robotics Industry proposes that by2035 robots will become an important component of China’s economic development,people’s lives and social governance.As a typical representative of robots,robotic arms are widely used in industrial production.Research on robotic arms includes structural design,trajectory planning and motion control,of which trajectory planning is the basis and focus for its completion of the corresponding operational tasks.The current trajectory planning of robotic arms is mostly based on time-optimal methods,and when the emphasis is on low-energy work,the existing trajectory planning methods will limit the working capacity of robotic arms.Considering the above problems,this paper proposes a robotic arm trajectory planning method based on energy optimization with the JAKA Zu3 robotic arm as the research object,and the main research contents are as follows.(1)The forward kinematic model of the knuckle card Zu3 is established by using the improved DH parameter method,and its inverse kinematic solution is completed by using the analytical method to realise the interconversion of the robotic arm parameters in Cartesian space and joint space.A model of the robot arm dynamics is also established by applying Lagrangian functions.(2)To address the problem that the robot arm dynamics model is complex and the realtime energy consumption model is difficult to solve,a radial basis function(RBF)neural network-based modeling method for the power consumption of the robot arm is proposed.A physical simulation model of the robotic arm is established in Simulink,and the robotic arm motion data is collected and used for the training of the RBF neural network model to obtain a neural network model with the angle,angular velocity and angular acceleration of the robotic arm as input and the total power as output,so as to achieve an accurate description of the realtime energy consumption of the robotic arm.(3)To address the problem of large energy consumption in traditional robotic arm path planning by continuously passing through multiple path points,a path planning method based on inverse solution of path points and interpolated time series optimization is proposed.The4-3-4 polynomial interpolation method is selected as the base trajectory planning method;afterwards,the base trajectory is analysed to determine the path point inverse solution and polynomial interpolation time series as the main factors affecting the energy consumption of the robotic arm.Particle swarm optimization(PSO)is used to optimize the path point inverse solution and the interpolated time series to achieve the optimal energy consumption trajectory planning for continuous multi-path point paths.(4)The Coppelia Sim simulation experiment platform is built on the example of a wheeled-arm inspection robot performing gate inspection at a high-speed railway passenger station.The path point of the robot arm inspection work is determined in Coppelia Sim environment,and the robot arm trajectory is planned by the algorithm proposed in this paper,and the robot arm trajectory tracking simulation experiment is carried out.The simulation results of Simulink and Coppelia Sim show that the power consumption model of the robotic arm established in this paper can describe the real-time power consumption of the robotic arm more accurately,and the proposed optimization algorithm has optimized the selection of the inverse solution of the path point and the adjustment of the interpolation time series in different degrees,which can effectively reduce the energy consumption of the robotic arm movement.