Time-optimal smooth-path motion planning for a mobile robot with kinematic constraints

Robotica ◽  
1997 ◽  
Vol 15 (5) ◽  
pp. 547-553 ◽  
Author(s):  
K. Jiang ◽  
L.D. Seneviratne ◽  
S.W.E. Earles

This paper presents a novel time-optimal motion planning strategy for a mobile robot with kinematic constraints. The method works in environments in presence of obstacles, without needing to generate the configuration space for the robot. Further, it derives a minimum time first derivative smooth path, as opposed to a minimum distance path which is commonly given by various present solution techniques. The problem is solved in three stages: (i) A reduced visibility graph for a point object is obtained. (ii) The reduced visibility graph is converted into a feasible reduced visibility graph accounting for the size and kinematic constraints of the robot. (iii) The A* algorithm is used to search the feasible reduced visibility graph with the cost function being the time of travel, to obtain a safe, time-optimal, smooth path. The algorithm runs in polynomial time. The method has been tested in computer simulations and test results are presented

2021 ◽  
Author(s):  
Hung Hoang ◽  
Anh Khoa Tran ◽  
Lam Nhat Thai Tran ◽  
My-Ha Le ◽  
Duc-Thien Tran

Author(s):  
Ákos Nagy ◽  
István Vajk

Time-optimal motion-planning has been a topic of active research in the literature for a while. This paper presents a new approach for velocity profile generation, which is a subproblem in motion-planning. In the case of simplified constraints, profile generation can be translated to a convex optimization problem. However, some practical constraints (e.g., velocity-dependent torque, viscous friction) destroy the convexity. The proposed method can obtain the global optimum of the nonconvex optimization problem. The experimental results with a three degrees-of-freedom (DOF) robot manipulator are also presented in this paper.


2020 ◽  
Vol 5 (2) ◽  
pp. 2216-2223 ◽  
Author(s):  
Dominik Kaserer ◽  
Hubert Gattringer ◽  
Andreas Muller

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