This paper proposes an obstacle avoidance trajectory generation method that provides a smooth trajectory in real time. The trajectory is generated from an environmental top-view image, where a fisheye lens is used to capture a wide area at low height. Corners of the obstacles are detected and corrected using the log-polar transform and are used to generate a simple configuration space that reduces the computation time. An optimal path is computed by using the A algorithm and replaced by a smooth trajectory generated based on piecewise quintic Bézier curves. Based on the established goal and visual information, a method for generating the first and second derivatives at the start and end points of each Bézier segment is proposed to generate a continuous curvature trajectory. The method is simple and easy to implement and has an average computation time of 1.17s on a PC (CPU: 1.4 GHz) for a workspace containing five to six obstacles. Experimental results verify that the proposed method is effective for real-time motion planning of autonomous mobile robots.
LeeAYChoiY. Smooth trajectory planning methods using physical limits. Proc IMechE, Part C: J Mechanical Engineering Science2014. DOI: 0954406214553982.
2.
Saudi A and Sulaiman J. Red-black strategy for mobile robot path planning. In: Proceedings of the international multiconference of engineers and computer scientists, Hong Kong, 2010.
3.
KimDHHanCSLeeJY. Sensor-based motion planning for path tracking and obstacle avoidance of robotic vehicles with nonholonomic constraints. Proc IMechE, Part C: J Mechanical Engineering Science2013; 227: 178–191.
4.
MohammedAWangLGaoRX. Integrated image processing and path planning for robotic sketching. Procedia CIRP2013; 12: 199–204.
5.
Uchiyama N, Hashimoto T, Sano S, et al. Obstacle avoidance control for a human-operated mobile robot. In: 10th IEEE international workshop on advanced motion control, AMC ’08, 2008, pp.468–473.
6.
UchiyamaNHashimotoTSanoS. Model-reference control approach to obstacle avoidance for a human-operated mobile robot. IEEE Trans Ind Electron2009; 56: 3892–3896.
7.
UchiyamaNSanoSYamamotoA. Sound source tracking considering obstacle avoidance for a mobile robot. Robotica2010; 28: 1057–1064.
8.
UchiyamaNDewiTSanoS. Collision avoidance control for a human-operated four-wheeled mobile robot. Proc IMechE, Part C: J Mechanical Engineering Science2014; 228: 2278–2284.
9.
ChandakAGosaviKGiriS. Path planning for mobile robot navigation using image processing. Int J Scient Eng Res2013; 4: 1490–1496.
10.
ChungWKimSChoiM. Safe navigation of a mobile robot considering visibility of environment. IEEE Trans Ind Electron2009; 56: 3941–3950.
11.
KimDHShinKHanCS. Sensor-based navigation of a car-like robot based on Bug family algorithms. Proc IMechE, Part C: J Mechanical Engineering Science2013; 227: 1224–1241.
12.
Ho YJ and Liu JS. Collision-free curvature-bounded smooth path planning using composite Bézier curve based on Voronoi diagram. In: IEEE international symposium on computational intelligence in robotics and automation (CIRA), Trento, Italy, 2009, pp.463–468.
13.
JollyKGSreerama KumarRVijayakumarR. A Bézier curve based path planning in a multi-agent robot soccer system without violating the acceleration limits. Robot Autonom Syst2009; 57: 23–33.
14.
Choi JW, Curry R and Elkaim G. Path planning based on Bézier curve for autonomous ground vehicles. In: World congress on engineering and computer science 2008, WCECS ’08, IAENG Special Edition of the advances in electrical and electronics engineering, San Francisco, USA, 2008, pp.158–166.
15.
Wang S, Chen L, Hu H, et al. Doorway passing of an intelligent wheelchair by dynamically generating Bézier curve trajectory. In: 2012 IEEE international conference on robotics and biomimetics (ROBIO), Guangzhou, China, 2012, pp.1206–1211.
16.
YangKSukkariehS. An analytical continuous-curvature path-smoothing algorithm. IEEE Trans Robot2010; 26: 561–568.
17.
Perez J, Godoy J, Villagra J, et al. Trajectory generator for autonomous vehicles in urban environments. In: IEEE international conference on robotics and automation (ICRA), 2013, pp.409–414.
18.
Moctezuma LEG, Lobov A and Lastra JLM. Free shape paths in industrial robots. In: IECON 2012 - 38th Annual conference on IEEE industrial electronics society, Montreal, Canada, 2012, pp.3739–3743.
19.
Aron O and Lozano-Pérez T. Visible decomposition: Real-time path planning in large planar environments. AI Memo, 1996.
20.
MasehianEAmin-NaseriMR. A Voronoi diagram-visibility graph-potential field compound algorithm for robot path planning. J Robot Syst2004; 21: 275–300.
21.
Simba KR, Uchiyama N and Sano S. Real-time trajectory generation for mobile robots in a corridor-like space using Bézier curves. In: IEEE/SICE international symposium on system integration (SII), 2013, pp.37–41.
22.
FidalgoEGRodriguezAO. Vision-based topological mapping and localization methods: A survey. Robot Autonom Syst2015; 64: 1–20.
23.
Petillot Y, Ruiz IT, Lane DM, et al. Underwater vehicle path planning using a multi-beam forward looking sonar. In: OCEANS ’98 conference proceedings, vol. 2, Nice, France, 1998, pp.1194–1199.
24.
KimJDoY. Moving obstacle avoidance of a mobile robot using a single camera. Procedia Eng2012; 41: 911–916.
25.
Kumler JJ and Bauer ML. Fish-eye lens designs and their relative performance. Proceedings of the SPIE, vol. 4093, 2000, pp.360–369.
26.
FryerJGBrownDC. Lens distortion for close-range photogrammetry. Photogram Eng Remote Sens1986; 52: 51–58.
27.
FaugerasO. Three-dimensional computer vision: A geometric viewpoint, Cambridge, MA: MIT Press, 1993.
28.
Derpanis KG. The Harris Corner Detector, 2004.
29.
Araujo H and Dias JM. An introduction to the log-polar mapping [image sampling]. In: Proceedings of second workshop on cybernetic vision, Sao Carlos, Brazil, 1996, pp.139–144.
30.
PanWQinKChenY. An adaptable-multilayer fractional fourier transform approach for image registration. In: IEEE Trans Pattern Anal Mach Intell2009; 31: 400–414.
31.
Simba KR, Uchiyama N and Sano S. Real-time obstacle-avoidance motion planning for autonomous mobile robots. In: AUCC Australian control conference, Canberra, Australia, 2014, pp.267–272.
32.
SiegwartRNourbakhshIRScaramuzzaD. Introduction to autonomous mobile robots, 2nd ed. Cambridge, MA: MIT Press, London, England.
33.
TheoharisTPapaioannouGPlatisN. Graphics and visualization: Principles & algorithms, Natick, MA: A. K. Peters, Ltd., 2007.
