Gaussian process motion planning for robots based on random restarts
In complex obstacle environments,mobile robots using the Gaussian Path Motion Planning(GPMP2)algorithm suffer from the problems of falling into local optimums and poor obstacle avoidance performance,a method based on stochastic restart and obstacle avoidance improvement was proposed Gaussian Process Motion Planning with Stochastic Restart and Obstacle Avoid-ance Improvement(GPMP2-SROAI).Firstly,the random restart mechanism in the Covariant Hamiltonian Optimisation for Motion Planning(CHOMP)was introduced to apply perturbations to the trajectory to jump out of the local optimum and improve the efficiency and robustness of the trajectory optimization.Then,a Model Predictive Control with Control Barrier Function(MPC-CBF)approach based on the barrier function was introduced to avoid collisions by predicting the range of motion of the robot dur-ing the optimisation process.Simulation results show that,the improved algorithm achieves a success rate of 92.6％in path plan-ning,which is 24.9％higher than that of GPMP2,12.5％higher than the path shortest probability,and 4.8％higher than the average smoothing degree,and also achieves a better quality of trajectory planning compared with the mainstream algorithms,with smoother trajectories and better obstacle avoidance effects.
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