Leader–follower-based dynamic trajectory planning for multirobot formation

Robotica ◽  
2013 ◽  
Vol 31 (8) ◽  
pp. 1351-1359 ◽  
Author(s):  
Shuang Liu ◽  
Dong Sun
Keyword(s):  
Analytical Solution ◽  
Objective Function ◽  
Collision Avoidance ◽  
Trajectory Planning ◽  
Trajectory Optimization ◽  
Optimal Trajectory ◽  
Dynamic Environment ◽  
Current Paper ◽  
New Approach ◽  
Dynamic Trajectory

SUMMARYThe present paper presents a new approach to a leader–follower-based dynamic trajectory planning for multirobot formation. A near-optimal trajectory is generated for each robot in a decentralized manner. The main contributions of the current paper are the proposal of a new objective function that considers both collision avoidance and formation requirement for the trajectory generation, and an analytical solution of trajectory parameters in the trajectory optimization. Simulations and experiments on multirobots are performed to demonstrate the effectiveness of the proposed approach to the multirobot formation in a dynamic environment.

A new approach to time-optimal trajectory planning with torque and jerk limits for robot

2021 ◽  
Vol 140 ◽  
pp. 103744
Author(s):  
Jian-wei Ma ◽  
Song Gao ◽  
Hui-teng Yan ◽  
Qi Lv ◽  
Guo-qing Hu
Keyword(s):  
Trajectory Planning ◽  
Optimal Trajectory ◽  
New Approach ◽  
Time Optimal ◽  
Optimal Trajectory Planning

Vehicle Collision Avoidance Maneuvers With Limited Lateral Acceleration Using Optimal Trajectory Control

2013 ◽  
Vol 135 (4) ◽  
Author(s):  
Damoon Soudbakhsh ◽  
Azim Eskandarian ◽  
David Chichka
Keyword(s):  
Collision Avoidance ◽  
Trajectory Optimization ◽  
Optimal Trajectory ◽  
Optimization Technique ◽  
Lateral Acceleration ◽  
Optimal Controller ◽  
Tire Model ◽  
Vehicle Collision ◽  
Acceleration Profile

In this paper, the possibility of performing severe collision avoidance maneuvers using trajectory optimization is investigated. A two degree of freedom vehicle model was used to represent dynamics of the vehicle. First, a linear tire model was used to calculate the required steering angle to perform the desired evasive maneuver, and a neighboring optimal controller was designed. Second, direct trajectory optimization algorithm was used to find the optimal trajectory with a nonlinear tire model. To evaluate the results, the calculated steering angles were fed to a full vehicle dynamics model. It was shown that the neighboring optimal controller was able to accommodate the introduced disturbances. Comparison of the resultant trajectories with other desired trajectories showed that it results in a lower lateral acceleration profile and a smaller maximum lateral acceleration; thus the time to perform an obstacle avoidance maneuver can be reduced using this method. A simulation case study of a limited lateral acceleration with constrained direct trajectory optimization shows that using the proposed trajectory optimization technique requires less time than that of trapezoidal acceleration profile for a lane change maneuver.

scholarly journals Ship's Trajectory Planning for Collision Avoidance at Sea Based on Ant Colony Optimisation

2014 ◽  
Vol 68 (2) ◽  
pp. 291-307 ◽  
Author(s):  
Agnieszka Lazarowska
Keyword(s):  
Control Systems ◽  
Trajectory Planning ◽  
Dynamic Environment ◽  
Research Problem ◽  
Obstacle Detection ◽  
Ant Colony ◽  
Ant Colony Optimisation ◽  
New Approach ◽  
Guidance And Control ◽  
And Control

Swarm Intelligence (SI) constitutes a rapidly growing area of research. At the same time trajectory planning in a dynamic environment still constitutes a very challenging research problem. This paper presents a new approach to path planning in dynamic environments based on Ant Colony Optimisation (ACO). Assumptions, a concise description of the method developed and results of real navigational situations (case studies with comments) are included. The developed solution can be applied in decision support systems on board a ship or in an intelligent Obstacle Detection and Avoidance system, which constitutes a component of Unmanned Surface Vehicle (USV) Navigation, Guidance and Control systems.

scholarly journals Solving the Time-Jerk Optimal Trajectory Planning Problem of a Robot Using Augmented Lagrange Constrained Particle Swarm Optimization

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Shaotian Lu ◽  
Jingdong Zhao ◽  
Li Jiang ◽  
Hong Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Objective Function ◽  
Trajectory Planning ◽  
Optimal Trajectory ◽  
Particle Swarm ◽  
Planning Problem ◽  
Swarm Optimization ◽  
Best Value ◽  
Augmented Lagrange Multiplier ◽  
Optimal Trajectory Planning

The problem of minimum time-jerk trajectory planning for a robot is discussed in this paper. The optimal objective function is composed of two segments along the trajectory, which are the proportional to the total execution time and the proportional to the integral of the squared jerk (which denotes the derivative of the acceleration). The augmented Lagrange constrained particle swarm optimization (ALCPSO) algorithm, which combines the constrained particle swarm optimization (CPSO) with the augmented Lagrange multiplier (ALM) method, is proposed to optimize the objective function. In this algorithm, falling into a local best value can be avoided because a new particle swarm is generated per initial procedure, and the best value gained from the former generation is saved and delivered to the next generation during the iterative search procedure to enable the best value to be found more easily and more quickly. Finally, the proposed algorithm is tested on a planar 3-degree-of-freedom (DOF) robot; the simulation results show that the algorithm is effective, offering a solution to the time-jerk optimal trajectory planning problem of a robot under nonlinear constraints.

Optimal Trajectory Planning for Parallel Robots Considering Time-Jerk

2013 ◽  
Vol 390 ◽  
pp. 471-477 ◽  
Author(s):  
Sajad Rashidnejhad ◽  
Amir Hossein Asfia ◽  
Kambiz Ghaemi Osgouie ◽  
Ali Meghdari ◽  
Aydin Azizi
Keyword(s):  
Objective Function ◽  
Trajectory Planning ◽  
Execution Time ◽  
Optimal Trajectory ◽  
Parallel Robots ◽  
Total Execution Time ◽  
Planning Techniques ◽  
Kinematic Constraints ◽  
Upper Limits ◽  
Optimal Trajectory Planning

A method for optimization in trajectory planning of 3RUU robot manipulators is presented in this paper. At first, to get the optimal trajectory, position analyses has been done on the 3RUU robot, then an objective function which have two terms is minimized: first term relevant to the total execution time and another one relevant to the integral of the squared jerk (defined as the derivative of the acceleration toward time) along the trajectory and this Guarantees that the obtained trajectory is smooth. This technique let to calculate the kinematic constraints on the motion of the robot, defined as upper limits on the absolute values of velocity, acceleration and jerk. , the total execution time does not require to be set priori. The algorithm has been tested in simulation and in comparison with other important trajectory planning techniques it has been given good results.

scholarly journals Modelling of Ship’s Trajectory Planning in Collision Situations by Hybrid Genetic Algorithm

2018 ◽  
Vol 25 (3) ◽  
pp. 14-25 ◽  
Author(s):  
Shengke Ni ◽  
Zhengjiang Liu ◽  
Yao Cai ◽  
Xin Wang
Keyword(s):  
Genetic Algorithm ◽  
Collision Avoidance ◽  
Trajectory Planning ◽  
Optimal Trajectory ◽  
Hybrid Genetic Algorithm ◽  
Test Cases ◽  
Open Sea ◽  
Restricted Condition ◽  
Ship Collision ◽  
Optimal Trajectory Planning

Abstract Ship collision-avoidance trajectory planning aims at searching for a theoretical safe-critical trajectory in accordance with COLREGs and good seamanship. In this paper, a novel optimal trajectory planning based on hybrid genetic algorithm is presented for ship collision avoidance in the open sea. The proposed formulation is established based on the theory of the Multiple Genetic Algorithm (MPGA) and Nonlinear Programming, which not only overcomes the inherent deficiency of the Genetic Algorithm (GA) for premature convergence, but also guarantees the practicality and consistency of the optimal trajectory. Meanwhile, the encounter type as well as the obligation of collision avoidance is determined according to COLREGs, which is then considered as the restricted condition for the operation of population initialization. Finally, this trajectory planning model is evaluated with a set of test cases simulating various traffic scenarios to demonstrate the feasibility and superiority of the optimal trajectory.

Task space-based dynamic trajectory planning for digging process of a hydraulic excavator with the integration of soil–bucket interaction

2018 ◽  
Vol 233 (3) ◽  
pp. 598-616 ◽  
Author(s):  
Zhihong Zou ◽  
Jin Chen ◽  
Xiaoping Pang
Keyword(s):  
Trajectory Planning ◽  
Trajectory Optimization ◽  
Inverse Kinematics ◽  
Field Experiments ◽  
Hydraulic Excavator ◽  
Task Space ◽  
Working Mechanism ◽  
Motion Parameters ◽  
Self Motion ◽  
Dynamic Trajectory

In this paper, a task space-based methodology for dynamic trajectory planning for digging process of a hydraulic excavator is presented, with the integration of soil–bucket interaction. An extended soil–bucket interaction model, which adds the resistive moment compared to the previous models, is provided in this research. This improved model is validated by comparing with the measurement data taken from field experiments before integrating it into a dynamic model of an excavator. Further, Newton–Euler method is used for the derivation of the dynamics of each link of the excavator to determine the joint forces, which can cause the machine damage. The position and orientation trajectories of the bucket in the task space are parameterized by using the B-splines, so as to achieve the task-oriented operations and ensure the operation flexibility. The joint space motion characteristics are obtained by solving the inverse kinematics of the working mechanism of an excavator. Moreover, to avoid the operation uncertainty for a given bucket tip position trajectory and reduce the computational effort, the self-motion parameters are introduced when solving the inverse kinematics of the redundant working mechanism. All these self-motion parameters are taken as a set of design variables in the trajectory optimization problem. Also, the limits on the hydraulic driving forces, joint angles, angular velocities and accelerations, as well as bucket capacity are considered as the optimization constraints for the digging process. Finally, optimization examples of two typical digging categories (i.e. level digging work and slope digging work) are given to demonstrate and verify the capabilities of the new methodology proposed in this research. The results show that the proposed method can effectively generate the optimal trajectories satisfying the following criteria: time efficiency, energy efficiency, and least machine damage. This work lays a solid foundation for motion planning and autonomous control of an excavator.

A Trajectory Planning Algorithm for Autonomous Overtaking Maneuvers

2013 ◽  
Vol 347-350 ◽  
pp. 3494-3499 ◽  
Author(s):  
Su Min Zhang ◽  
Hao Sun ◽  
Yu Wang
Keyword(s):  
Trajectory Planning ◽  
Autonomous Vehicles ◽  
Dynamic Environment ◽  
Vehicle Model ◽  
Surrounding Environment ◽  
Planning Algorithm ◽  
Planning Algorithms ◽  
Short Time ◽  
Dynamic Trajectory ◽  
Traffic Rules

Autonomous overtaking maneuver is one of the toughest challenges in the field of autonomous vehicles. A key issue of autonomous overtaking maneuver is to find a dynamically feasible trajectory to avoid collision with the overtaken vehicle and surrounding hazards. Traditional trajectory planning algorithms assume that the initial and final vehicle states are given before and generate a trajectory for the whole overtaking process. However, overtaking maneuver is generally a time consuming process. Those assumptions may be invalid in highly dynamic environment. This paper tries to present a dynamic trajectory planning algorithm for autonomous overtaking maneuvers. The whole overtaking maneuver trajectory is made up of several short-time trajectories. Each short-time trajectory is generated by a kinematic vehicle model and taken into account of the surrounding environment and traffic rules. The concept presented in this paper is demonstrated through simulation and the results are discussed.

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