scholarly journals Tree-Search-Based Any-Time Time-Optimal Path-Constrained Trajectory Planning With Inadmissible Island Constraints

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 1040-1051
Author(s):  
Peiyao Shen ◽  
Xuebo Zhang ◽  
Yongchun Fang
Keyword(s):  
Trajectory Planning ◽  
Optimal Path ◽  
Tree Search ◽  
Time Optimal ◽  
Island Constraints

scholarly journals Dual-optimization trajectory planning based on parametric curves for a robot manipulator

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092004
Author(s):  
Yong-Lin Kuo ◽  
Chun-Chen Lin ◽  
Zheng-Ting Lin
Keyword(s):  
Path Planning ◽  
Trajectory Planning ◽  
Robot Manipulator ◽  
Optimal Path ◽  
Inequality Constraints ◽  
Parametric Curves ◽  
Optimal Velocity ◽  
Time Optimal ◽  
Planning Algorithm ◽  
Motion Profile

This article presents a dual-optimization trajectory planning algorithm, which consists of the optimal path planning and the optimal motion profile planning for robot manipulators, where the path planning is based on parametric curves. In path planning, a virtual-knot interpolation is proposed for the paths required to pass through all control points, so the common curves, such as Bézier curves and B-splines, can be incorporated into it. Besides, an optimal B-spline is proposed to generate a smoother and shorter path, and this scheme is especially suitable for closed paths. In motion profile planning, a generalized formulation of time-optimal velocity profiles is proposed, which can be implemented to any types of motion profiles with equality and inequality constraints. Also, a multisegment cubic velocity profile is proposed by solving a multiobjective optimization problem. Furthermore, a case study of a dispensing robot is investigated through the proposed dual-optimization algorithm applied to numerical simulations and experimental work.

Online time-optimal path and trajectory planning for robotic multipoint assembly

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yi Liu ◽  
Meng Joo Er ◽  
Chen Guo
Keyword(s):  
Dynamic Programming ◽  
Path Planning ◽  
Trajectory Planning ◽  
Optimal Path ◽  
Adaptive Dynamic Programming ◽  
Problem Model ◽  
Content Type ◽  
Markov Decision ◽  
Time Optimal ◽  
Planning Algorithm

Purpose The purpose of this paper is to propose an efficient path and trajectory planning method to solve online robotic multipoint assembly. Design/methodology/approach A path planning algorithm called policy memorized adaptive dynamic programming (PM-ADP) combines with a trajectory planning algorithm called adaptive elite genetic algorithm (AEGA) for online time-optimal path and trajectory planning. Findings Experimental results and comparative study show that the PM-ADP is more efficient and accurate than traditional algorithms in a smaller assembly task. Under the shortest assembly path, AEGA is used to plan the time-optimal trajectories of the robot and be more efficient than GA. Practical implications The proposed method builds a new online and efficient path planning arithmetic to cope with the uncertain and dynamic nature of the multipoint assembly path in the Cartesian space. Moreover, the optimized trajectories of the joints can make the movement of the robot continuously and efficiently. Originality/value The proposed method is a combination of time-optimal path planning with trajectory planning. The traveling salesman problem model of assembly path is established to transfer the assembly process into a Markov decision process (MDP). A new dynamic programming (DP) algorithm, termed PM-ADP, which combines the memorized policy and adaptivity, is developed to optimize the shortest assembly path. GA is improved, termed AEGA, which is used for online time-optimal trajectory planning in joints space.

Near Time Optimal Path Control of Kinematically Redundant Manipulators with Bounded Joint Velocities and Torques

2011 ◽  
Vol 110-116 ◽  
pp. 1547-1555
Author(s):  
Mohammad Hassan Ghasemi ◽  
Navvab Kashiri ◽  
Morteza Dardel ◽  
Mohammad Hadi Pashaei
Keyword(s):  
Linear Programming ◽  
Trajectory Planning ◽  
Optimal Path ◽  
Phase Plane Analysis ◽  
Programming Technique ◽  
Plane Analysis ◽  
Path Control ◽  
Time Optimal ◽  
Planning Algorithm ◽  
Optimal Control Scheme

here, a time optimal control scheme for trajectory planning of kinematically manipulators subjects to actuator torque limits is proposed by using the phase plane analysis and linear programming technique. In addition, the limit on joint velocities is considered. In order to affect the constraint of joint velocities, this constraint is converted to constraint on joint acceleration and it is affected linear programming problem as an additional constraint. Also, an explicit algorithm for finding the switching points is presented. To this end, some simulations are given to demonstrate the efficiency of proposed trajectory planning algorithm.

Time Optimal Path Planning of Palletizing Robot

2013 ◽  
Vol 470 ◽  
pp. 658-662
Author(s):  
Yong Pan Xu ◽  
Ying Hong
Keyword(s):  
Trajectory Planning ◽  
Optimal Trajectory ◽  
Optimal Path ◽  
Straight Path ◽  
Optimal Path Planning ◽  
Quintic Polynomial ◽  
Time Optimal ◽  
Acceleration And Deceleration ◽  
Planning Algorithm ◽  
Optimal Trajectory Planning

In order to improve the efficiency and reduce the vibration of Palletizing Robot, a new optimal trajectory planning algorithm is proposed. This algorithm is applied to the trajectory planning of Palletizing manipulators. The S-shape acceleration and deceleration curve is adopted to interpolate joint position sequences. Considering constraints of joint velocities, accelerations and jerks, the traveling time of the manipulator is minimized. The joint interpolation confined by deviation is used to approximate the straight path, and the deviation is decreased significantly by adding only small number of knots. Traveling time is solved by using quintic polynomial programming strategy between the knots, and then time-jerk optimal trajectories which satisfy constraints are planned. The results show that the method can avoid the problem of manipulator singular points and improve the palletize efficiency.

scholarly journals Aircraft Parking Trajectory Planning in Semistructured Environment Based on Kinodynamic Safety RRT ∗

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xianglei Meng ◽  
Nengjian Wang ◽  
Qinhui Liu
Keyword(s):  
Trajectory Planning ◽  
Specific Problem ◽  
Optimal Path ◽  
Performance Goal ◽  
Simulation Experiments ◽  
Sampling Points ◽  
Time Optimal ◽  
Path Planner ◽  
Fast Collision ◽  
Safe Path

To improve the safety and effectiveness of autonomous towing aircraft aboard the carrier deck, this study proposes a velocity-restricted path planner algorithm named as kinodynamic safety optimal rapidly exploring random tree (KS-RRT ∗ ) to plan a near time-optimal path. First, a speed map is introduced to assign different maximum allowable velocity for the sampling points in the workspace, and the traverse time is calculated along the kinodynamic connection of two sampling points. Then the near time-optimal path in the tree-structured search map can be obtained by the rewiring procedures, instead of a distance-optimal path in the original RRT ∗ algorithm. In order to enhance the planner’s performance, goal biasing scheme and fast collision checking technique are adopted in the algorithm. Since the sampling-based methods are sensitive to their parameters, simulation experiments are first conducted to determine the optimal input settings for the specific problem. The effectiveness of the proposed algorithm is validated in several common aircraft parking scenarios. Comparing with standard RRT ∗ and human heuristic driving, KS-RRT ∗ demonstrates a higher success rate, as well as shorter computation and trajectory time. In conclusion, KS-RRT ∗ algorithm is suitable to generate a near time-optimal safe path for autonomous high density parking in semistructured environment.

scholarly journals AlphaPilot: autonomous drone racing

2021 ◽  
Author(s):  
Philipp Foehn ◽  
Dario Brescianini ◽  
Elia Kaufmann ◽  
Titus Cieslewski ◽  
Mathias Gehrig ◽  
...  
Keyword(s):  
Real Time ◽  
Trajectory Planning ◽  
Nonlinear Filtering ◽  
Optimal Trajectory ◽  
Optimal Path ◽  
Data Abstraction ◽  
State Estimate ◽  
World Championship ◽  
Time Optimal ◽  
Optimal Trajectory Planning

AbstractThis paper presents a novel system for autonomous, vision-based drone racing combining learned data abstraction, nonlinear filtering, and time-optimal trajectory planning. The system has successfully been deployed at the first autonomous drone racing world championship: the 2019 AlphaPilot Challenge. Contrary to traditional drone racing systems, which only detect the next gate, our approach makes use of any visible gate and takes advantage of multiple, simultaneous gate detections to compensate for drift in the state estimate and build a global map of the gates. The global map and drift-compensated state estimate allow the drone to navigate through the race course even when the gates are not immediately visible and further enable to plan a near time-optimal path through the race course in real time based on approximate drone dynamics. The proposed system has been demonstrated to successfully guide the drone through tight race courses reaching speeds up to $${8}\,{\hbox {m}/\hbox {s}}$$ 8 m / s and ranked second at the 2019 AlphaPilot Challenge.

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