Robot manipulators perform a point-point task under kinematic and dynamic constraints.Due to multi-degreeof-freedom coupling characteristics,it is difficult to find a better desired trajectory.In this paper,a multi-ob...Robot manipulators perform a point-point task under kinematic and dynamic constraints.Due to multi-degreeof-freedom coupling characteristics,it is difficult to find a better desired trajectory.In this paper,a multi-objective trajectory planning approach based on an improved elitist non-dominated sorting genetic algorithm(INSGA-II)is proposed.Trajectory function is planned with a new composite polynomial that by combining of quintic polynomials with cubic Bezier curves.Then,an INSGA-II,by introducing three genetic operators:ranking group selection(RGS),direction-based crossover(DBX)and adaptive precision-controllable mutation(APCM),is developed to optimize travelling time and torque fluctuation.Inverted generational distance,hypervolume and optimizer overhead are selected to evaluate the convergence,diversity and computational effort of algorithms.The optimal solution is determined via fuzzy comprehensive evaluation to obtain the optimal trajectory.Taking a serial-parallel hybrid manipulator as instance,the velocity and acceleration profiles obtained using this composite polynomial are compared with those obtained using a quintic B-spline method.The effectiveness and practicability of the proposed method are verified by simulation results.This research proposes a trajectory optimization method which can offer a better solution with efficiency and stability for a point-to-point task of robot manipulators.展开更多
With the rapid development of intelligent navigation technology,efficient and safe path planning for mobile robots has become a core requirement.To address the challenges of complex dynamic environments,this paper pro...With the rapid development of intelligent navigation technology,efficient and safe path planning for mobile robots has become a core requirement.To address the challenges of complex dynamic environments,this paper proposes an intelligent path planning framework based on grid map modeling.First,an improved Safe and Smooth A*(SSA*)algorithm is employed for global path planning.By incorporating obstacle expansion and cornerpoint optimization,the proposed SSA*enhances the safety and smoothness of the planned path.Then,a Partitioned Dynamic Window Approach(PDWA)is integrated for local planning,which is triggered when dynamic or sudden static obstacles appear,enabling real-time obstacle avoidance and path adjustment.A unified objective function is constructed,considering path length,safety,and smoothness comprehensively.Multiple simulation experiments are conducted on typical port grid maps.The results demonstrate that the improved SSA*significantly reduces the number of expanded nodes and computation time in static environmentswhile generating smoother and safer paths.Meanwhile,the PDWA exhibits strong real-time performance and robustness in dynamic scenarios,achieving shorter paths and lower planning times compared to other graph search algorithms.The proposedmethodmaintains stable performance across maps of different scales and various port scenarios,verifying its practicality and potential for wider application.展开更多
This paper investiga tes a trajectory planning algorithm to reduce the manipulator’s working time.A t ime-optimal trajectory planning(TOTP)is conducted based on improved ad aptive genetic algorithm(IAGA)and combined ...This paper investiga tes a trajectory planning algorithm to reduce the manipulator’s working time.A t ime-optimal trajectory planning(TOTP)is conducted based on improved ad aptive genetic algorithm(IAGA)and combined with cubic triangular Bezier spline(CTBS).The CTBS based trajectory planning we did before can achieve continuous second and third derivation,hence it meets the stability requirements of the m anipulator.The working time can be greatly reduced by applying IAGA to the puma 560 trajectory planning when considering physical constraints such as angular ve locity,angular acceleration and jerk.Simulation experiments in both Matlab and ADAMS illustrate that TOTP based on IAGA can give a time optimal result with sm oothness and stability.展开更多
基金Supported by the Zhejiang Provincial Natural Science Foundation for Distinguished Young Scientists(Grant No.LR18E050003)the National Natural Science Foundation of China(Grant Nos.51975523,51905481)+2 种基金Natural Science Foundation of Zhejiang Province(Grant No.LY22E050012)the Students in Zhejiang Province Science and Technology Innovation Plan(Xinmiao Talents Program)(Grant No.2020R403054)the China Postdoctoral Science Foundation(Grant No.2020M671784)。
文摘Robot manipulators perform a point-point task under kinematic and dynamic constraints.Due to multi-degreeof-freedom coupling characteristics,it is difficult to find a better desired trajectory.In this paper,a multi-objective trajectory planning approach based on an improved elitist non-dominated sorting genetic algorithm(INSGA-II)is proposed.Trajectory function is planned with a new composite polynomial that by combining of quintic polynomials with cubic Bezier curves.Then,an INSGA-II,by introducing three genetic operators:ranking group selection(RGS),direction-based crossover(DBX)and adaptive precision-controllable mutation(APCM),is developed to optimize travelling time and torque fluctuation.Inverted generational distance,hypervolume and optimizer overhead are selected to evaluate the convergence,diversity and computational effort of algorithms.The optimal solution is determined via fuzzy comprehensive evaluation to obtain the optimal trajectory.Taking a serial-parallel hybrid manipulator as instance,the velocity and acceleration profiles obtained using this composite polynomial are compared with those obtained using a quintic B-spline method.The effectiveness and practicability of the proposed method are verified by simulation results.This research proposes a trajectory optimization method which can offer a better solution with efficiency and stability for a point-to-point task of robot manipulators.
文摘With the rapid development of intelligent navigation technology,efficient and safe path planning for mobile robots has become a core requirement.To address the challenges of complex dynamic environments,this paper proposes an intelligent path planning framework based on grid map modeling.First,an improved Safe and Smooth A*(SSA*)algorithm is employed for global path planning.By incorporating obstacle expansion and cornerpoint optimization,the proposed SSA*enhances the safety and smoothness of the planned path.Then,a Partitioned Dynamic Window Approach(PDWA)is integrated for local planning,which is triggered when dynamic or sudden static obstacles appear,enabling real-time obstacle avoidance and path adjustment.A unified objective function is constructed,considering path length,safety,and smoothness comprehensively.Multiple simulation experiments are conducted on typical port grid maps.The results demonstrate that the improved SSA*significantly reduces the number of expanded nodes and computation time in static environmentswhile generating smoother and safer paths.Meanwhile,the PDWA exhibits strong real-time performance and robustness in dynamic scenarios,achieving shorter paths and lower planning times compared to other graph search algorithms.The proposedmethodmaintains stable performance across maps of different scales and various port scenarios,verifying its practicality and potential for wider application.
基金Fund of Taishan Scholar in Shandong Province,Shandong University of Science and Technology Research Fund(No.2010KYTD101)
文摘This paper investiga tes a trajectory planning algorithm to reduce the manipulator’s working time.A t ime-optimal trajectory planning(TOTP)is conducted based on improved ad aptive genetic algorithm(IAGA)and combined with cubic triangular Bezier spline(CTBS).The CTBS based trajectory planning we did before can achieve continuous second and third derivation,hence it meets the stability requirements of the m anipulator.The working time can be greatly reduced by applying IAGA to the puma 560 trajectory planning when considering physical constraints such as angular ve locity,angular acceleration and jerk.Simulation experiments in both Matlab and ADAMS illustrate that TOTP based on IAGA can give a time optimal result with sm oothness and stability.
