Robot Time-Optimal Trajectory Planning Based on Quintic Polynomial Interpolation and Improved Harris Hawks Algorithm

Abstract

Time-optimal trajectory planning is one of the most important ways to improve work efficiency and reduce cost and plays an important role in practical application scenarios of robots. Therefore, it is necessary to optimize the running time of the trajectory. In this paper, a robot time-optimal trajectory planning method based on quintic polynomial interpolation and an improved Harris hawks algorithm is proposed. Interpolation with a quintic polynomial has a smooth angular velocity and no acceleration jumps. It has widespread application in the realm of robot trajectory planning. However, the interpolation time is usually obtained by testing experience, and there is no unified criterion to determine it, so it is difficult to obtain the optimal trajectory running time. Because the Harris hawks algorithm adopts a multi-population search strategy, compared with other swarm intelligent optimization algorithms such as the particle swarm optimization algorithm and the fruit fly optimization algorithm, it can avoid problems such as single population diversity, low mutation probability, and easily falling into the local optimum. Therefore, the Harris hawks algorithm is introduced to overcome this problem. However, because some key parameters in HHO are simply set to constant or linear attenuation, efficient optimization cannot be achieved. Therefore, the nonlinear energy decrement strategy is introduced in the basic Harris hawks algorithm to improve the convergence speed and accuracy. The results show that the optimal time of the proposed algorithm is reduced by 1.1062 s, 0.5705 s, and 0.3133 s, respectively, and improved by 33.39%, 19.66%, and 12.24% compared with those based on particle swarm optimization, fruit fly algorithm, and Harris hawks algorithms, respectively. In multiple groups of repeated experiments, compared with particle swarm optimization, the fruit fly algorithm, and the Harris hawks algorithm, the computational efficiency was reduced by 4.7019 s, 1.2016 s, and 0.2875 s, respectively, and increased by 52.40%, 21.96%, and 6.30%. Under the optimal time, the maximum angular displacement, angular velocity, and angular acceleration of each joint trajectory meet the constraint conditions, and their average values are only 75.51%, 38.41%, and 28.73% of the maximum constraint. Finally, the robot end-effector trajectory passes through the pose points steadily and continuously under the cartesian space optimal time.

A novel trajectory planning method for meal-assistance robot-based interpolation

“…Thus, the application of this technique in planning the ready-to-scoop and reset segments can significantly decrease the computational workload of the entire meal-assistance planning process. The most common method for joint space trajectory planning is the quintic polynomial interpolation [9] method. This method avoids the problem of discontinuous acceleration of the cubic polynomial interpolation and is less computationally intensive than the seventh polynomial interpolation, so in this paper we use this method for joint space trajectory planning in the AB and EB (EA) segments.…”

Humanoid path planning on even and uneven terrains using an efficient memory-based gravitational search algorithm and evolutionary learning strategy

“…The proposed controller is activated whenever the robot encounters an obstacle within its threshold distance (=30 cm). The controller then generates N-virtual agents/masses to obtain the optimal global agent, which can provide an optimal fitness function value described in equation (25). The current work uses a sensor-based reactive model, where the NAOs only knows source and goal locations.…”

“…The overall objective is to minimize the final objective function, which is the weighted sum of the individual functions in equation (25). Weights are…”

“…Based on the training of the unfit agent/mass, the position of the final best mass is selected, and the robot takes the turn based on the position of the optimal agent. The optimal agent has the minimum value of the fitness function defined by equation (25). Once the robot is out of the threshold range of the obstacle, the algorithm again generates the best turning angle corresponding to the location of the goal.…”

“…Farhat et al 24 used the weighted mean of vectors (INFO) to solve the power distribution problem in energy generation. Xu et al 25 implemented the hybrid of Harris hawk optimization (HHO) to solve the path planning problem. However, the model is a single objective optimization tool that requires modification to solve multi-objective problems.…”