A Reverse Path Planning Approach for Enhanced Performance of Multi-Degree-of-Freedom Industrial Manipulators

In the domain of autonomous industrial manipulators,precise positioning and appropriate posture selection in path planning are pivotal for tasks involving obstacle avoidance,such as handling,heat sealing,and stacking.While Multi-Degree-of-Freedom(MDOF)manipulators offer kinematic redundancy,aiding in the derivation of optimal inverse kinematic solutions to meet position and posture requisites,their path planning entails intricate multiobjective optimization,encompassing path,posture,and joint motion optimization.Achieving satisfactory results in practical scenarios remains challenging.In response,this study introduces a novel Reverse Path Planning(RPP)methodology tailored for industrial manipulators.The approach commences by conceptualizing the manipulator’s end-effector as an agent within a reinforcement learning(RL)framework,wherein the state space,action set,and reward function are precisely defined to expedite the search for an initial collision-free path.To enhance convergence speed,the Q-learning algorithm in RL is augmented with Dyna-Q.Additionally,we formulate the cylindrical bounding box of the manipulator based on its Denavit-Hartenberg(DH)parameters and propose a swift collision detection technique.Furthermore,the motion performance of the end-effector is refined through a bidirectional search,and joint weighting coefficients are introduced to mitigate motion in high-power joints.The efficacy of the proposed RPP methodology is rigorously examined through extensive simulations conducted on a six-degree-of-freedom(6-DOF)manipulator encountering two distinct obstacle configurations and target positions.Experimental results substantiate that the RPP method adeptly orchestrates the computation of the shortest collision-free path while adhering to specific posture constraints at the target point.Moreover,itminimizes both posture angle deviations and joint motion,showcasing its prowess in enhancing the operational performance of MDOF industrial manipulators.

Predicting Total Dwell Time of IPSA Plan Based on Machine Learning and Dose Calculation Models

Objective To establish two models based on machine learning and dose calculation algorithm that can be used for the prediction of the total dwell time and rapid quality control of brachytherapy plans.Methods A total of 1042 cases of treated gynecologic oncology patients were selected,of which 512 were used as training data to establish the model and the rest were used as test data.Each treatment plan optimized by inverse planning simulated annealing with all three catheters of the Fletcher applicator.The source strength Sk,prescription dose D,source dwell time t,and tumor volume V were recorded for each case.RV was defined as Sk·t/D.In accordance with the prescription dosage calculation formula in the planning system and machine learning method,the following equations were established:RV=kV2/3 and RV=a+b · V+c·V2.The R2 correlation coefficient represents the accuracy of the results.Result The dose calculation algorithm-based model is RV=1272×V2/3,R2=0.959,whereas the machine learning-based model is RV=258.8× V-0.359× V2+5110,R2=0.961.The treatment time prediction of the two models,each having 13 and 15 cases,respectively,has an error rate of more than10%,and the dose calculation algorithm-based method is more accurate.Conclusion The treatment time can be quickly predicted according to the planning target volume,and the two prediction models can be used as a way of quality control.

Establishment of a Fast Quality Assurance Method for Three-dimensional Afterloading Treatment Plan

Objective:To study the correlation between tumor size,radiation source intensity,prescription dose,and source dwell time in afterloading treatment plan,and to establish a rapid quality control method for afterloading treatment plan.Methods:A total of 181 patients with gynecological tumor were enrolled in our hospital.A total of 84 patients were installed with three tubes of Fletcher'applicator,58 patients with single uterine tube and 39 patients with vaginal applicator.Each patient was scanned with CT before treatment,and the target area and organs were delineated by doctors.The treatment plan was optimized by IPSA.The planned source intensity,prescription dose,source residence time and tumor volume of each case were recorded and the CI,RV,and k value were calculated,The CI distribution characteristics and the relationship with RV value were analyzed.In addition,46 cases of gynecological tumor patients'afterloading plan used this method for quality control verification.Results:The CI of the three kinds of applicators was normal distribution.The average Ci of Fletcher applicator was 0.720±0.067,k=1394,r=0.894,the average CI of Fletcher applicator was 0.697±0.076,k=1428,r=0.940,the average CI of vaginal applicator was 0.742±0.067,k=1362,r=0.909.Conclusion:Using this method,we could quickly evaluate the target volume,radiation source intensity,prescription dose and treatment time,to determine the cause of deviation according to the feedback results,ensuring that the afterloading treatment plan can be implemented efficiently quickly,and accurately in accordance with the clinical requirements.