Optimisation-based verification process of obstacle avoidance systems for unicycle-like mobile robots
journal contributionposted on 2012-12-07, 14:18 authored by Sivaranjini Srikanthakumar, Wen-Hua ChenWen-Hua Chen
This paper presents an optimisation-based verification process for obstacle avoidance systems of a unicycle-like mobile robot. It is a novel approach for the collision avoidance verification process. Local and global optimisation based verification processes are developed to find the worst-case parameters and the worst-case distance between the robot and an obstacle. The kinematic and dynamic model of the unicycle-like mobile robot is first introduced with force and torque as the inputs. The design of the control system is split into two parts. One is velocity and rotation using the robot dynamics, and the other is the incremental motion planning for robot kinematics. The artificial potential field method is chosen as a path planning and obstacle avoidance candidate technique for verification study as it is simple and widely used. Different optimisation algorithms are applied and compared for the purpose of verification. It is shown that even for a simple case study where only mass and inertia variations are considered, a local optimization based verification method may fail to identify the worst case. Two global optimisation methods have been investigated: genetic algorithms (GAs) and GLOBAL algorithms. Both of these methods successfully find the worst case. The verification process confirms that the obstacle avoidance algorithm functions correctly in the presence of all the possible parameter variations.
- Aeronautical, Automotive, Chemical and Materials Engineering
- Aeronautical and Automotive Engineering
CitationSRIKANTHAKUMAR, S. and CHEN, W-H, 2011. Optimisation-based verification process of obstacle avoidance systems for unicycle-like mobile robots. International Journal of Automation and Computing, 8 (3), pp. 340 - 347
Publisher© Springer Verlag and Institute of Automation, Chinese Academy of Sciences
- AM (Accepted Manuscript)
NotesThis article was published in the International Journal of Automation and Computing [© Springer Verlag and the Institute of Automation, Chinese Academy of Sciences ]. The definitive version is available at: http://link.springer.com/article/10.1007/s11633-011-0590-4. The final publication is available at www.springerlink.com.