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 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.

An Innovative Motion Planning for 4-DOF PKM

2014 ◽  
Vol 619 ◽  
pp. 200-208
Author(s):  
Wen Hu Nan ◽  
Xiao Qi Tang ◽  
Bao Song
Keyword(s):  
Path Planning ◽  
Motion Planning ◽  
Optimal Path ◽  
Physical Constraints ◽  
Optimal Path Planning ◽  
Time Optimal ◽  
Planning Algorithm ◽  
Sphere Radius ◽  
Parallel Kinematic ◽  
Optimal Trajectory Planning

This paper presents an innovative motion planning algorithm for pick and place operations in the industry. The new technique is to seek optimal dynamics pattern for PKM(parallel kinematic manipulator), this is achieved by two parts: First, to achieve optimal path planning, a center sphere method based on virtual obstacle path planning method is proposed to solve such path planning with Particle Swarm Optimization (PSO); Second, to enhance the efficiency of the robot, time optimal trajectory planning is finished with respect to robotic physical constraints. Then, the method applied to a 4-DOF PKM is tested by numerical simulations, some examples are presented making comparisons on same tasks assigned by different sphere radius, the simulation results show the efficiency and the effectiveness of the proposed approach to determine the optimal motion pattern for 4-DOF PKM in obstacle-free workspace.

A Novel Algorithm for Time Optimal Trajectory Planning

2014 ◽  
Author(s):  
Mingxing Yuan ◽  
Bin Yao ◽  
Dedong Gao ◽  
Xiaocong Zhu ◽  
Qingfeng Wang
Keyword(s):  
Trajectory Planning ◽  
Optimal Trajectory ◽  
Optimal Solution ◽  
Problem Of Time ◽  
High Productivity ◽  
Hard Constraints ◽  
Time Optimal ◽  
Rate Optimization ◽  
Feed Rate Optimization ◽  
Optimal Trajectory Planning

Time optimal trajectory planning under various hard constraints plays a significant role in simultaneously meeting the requirements on high productivity and high accuracy in the fields of both machining tools and robotics. In this paper, the problem of time optimal trajectory planning is first formulated. A novel back and forward check algorithm is subsequently proposed to solve the minimum time feed-rate optimization problem. The basic idea of the algorithm is to search the feasible solution in the specified interval using the back or forward operations. Four lemmas are presented to illustrate the calculating procedure of optimal solution and the feasibility of the proposed algorithm. Both the elliptic curve and eight profile are used as case studies to verify the effectiveness of the proposed algorithm.

An optimal trajectory planning method for path tracking of industrial robots

Robotica ◽  
2018 ◽  
Vol 37 (3) ◽  
pp. 502-520 ◽  
Author(s):  
Xianxi Luo ◽  
Shuhui Li ◽  
Shubo Liu ◽  
Guoquan Liu
Keyword(s):  
Trajectory Planning ◽  
Optimal Trajectory ◽  
Optimal Path ◽  
Path Tracking ◽  
Industrial Robots ◽  
Planning Method ◽  
Coordinate Frame ◽  
Optimization Approach ◽  
Cartesian Coordinate ◽  
Optimal Trajectory Planning

SUMMARYThis paper presents an optimal trajectory planning method for industrial robots. The paper specially focuses on the applications of path tracking. The problem is to plan the trajectory with a specified geometric path, while allowing the position and orientation of the path to be arbitrarily selected within the specific ranges. The special contributions of the paper include (1) an optimal path tracking formulation focusing on the least time and energy consumption without violating the kinematic constraints, (2) a special mechanism to discretize a prescribed path integration for segment interpolation to fulfill the optimization requirements of a task with its constraints, (3) a novel genetic algorithm (GA) optimization approach that transforms a target path to be tracked as a curve with optimal translation and orientation with respect to the world Cartesian coordinate frame, (4) an integration of the interval analysis, piecewise planning and GA algorithm to overcome the challenges for solving the special trajectory planning and path tracking optimization problem. Simulation study shows that it is an insufficient condition to define a trajectory just based on the consideration that each point on the trajectory should be reachable. Simulation results also demonstrate that the optimal trajectory for a path tracking problem can be obtained effectively and efficiently using the proposed method. The proposed method has the properties of broad adaptability, high feasibility and capability to achieve global optimization.

Time-optimal trajectory planning method for six-legged robots under actuator constraints

2019 ◽  
Vol 233 (14) ◽  
pp. 4990-5002
Author(s):  
Zhijun Chen ◽  
Feng Gao
Keyword(s):  
Trajectory Planning ◽  
Optimal Trajectory ◽  
Optimization Problem ◽  
The Body ◽  
Planning Method ◽  
Legged Robots ◽  
Legged Robot ◽  
Time Optimal ◽  
Actuator Constraints ◽  
Optimal Trajectory Planning

Current studies on time-optimal trajectory planning centers on cases with fixed base and only one end-effector. However, the free-floating body and the multiple legs of the legged robot make the current methods inapplicable. This paper proposes a time-optimal trajectory planning method for six-legged robots. The model of the optimization problem for six-legged robots is built by considering the base and the end-effectors separately. Both the actuator constraints and the gait cycle constraints are taken into account. A novel two-step optimization method is proposed to solve the optimization problem. The first step solves the time-optimal trajectory of the body and the second step solves the time-optimal trajectory of the swinging legs. Finally, the method is applied to a six-parallel-legged robot and validated by experiments on the prototype. The results show that the velocity of the optimized gait is improved by 17.8% in contrast to the non-optimized one.

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