In this paper, an obstacle avoidance and target tracking method for both indoor and outdoor mobile robots in dynamic environment is presented, and it aims to enhance autonomous navigation capability of such robots. In the proposed method, image processing and machine-learning approaches are considered. Since obstacles have differences in color and texture and in order to identify non-navigable areas, a monocular onboard camera is used to capture the road lanes by dealing with an image processing technique. The position of the robot with respect to road lane center during navigation is calculated on the basis of a proposed fuzzy logic rules set. In order to provide fast and robust computation, the Haar cascade classifier-based machine-learning technique has been exploited to detect the different sizes and shapes of the obstacles faced by the robot during its movement from source to destination. The dynamic model of a four-wheel mobile robot is initially developed using bond graph theory and then, the proposed obstacle-avoidance strategy is applied. The effectiveness and performance of the proposed method are tested under various simulation and experimentation scenarios. The behavior of the mobile robot for detecting and avoiding static obstacle for single and double road lane change environment is analyzed and discussed.
KumanoMOhyaAYutaS. Obstacle avoidance of autonomous mobile robot using stereo vision sensor. In: International symposium on robotics and automation, San Franciso, CA, USA, 24–28 April 2000, pp. 497–502, IEEE.
2.
AbdellatifMMontasserOA.Using ultrasonic range sensors to control a mobile robot in obstacle avoidance behavior. In: Proceeding of the world conference on systemics, cybernetics and informatics, Orlando, Florida, USA, 22–25 July 2001, pp. 78–83, WorldCat.
3.
KoNYSimmonsRGKimKS.A lane based obstacle avoidance method for mobile robot navigation. KSME Int J2003; 17: 1693–1703.
MoghadamPWijesomaWSFengDJ. Improving path planning and mapping based on stereo vision and lidar. In: IEEE 10th international conference on control, automation, robotics and vision, Hanoi, Vietnam, 17–20 December 2008, pp. 384–389. New York: IEEE.
7.
LinCHJiangSYPuYJ, et al. Robust ground plane detection for obstacle avoidance of mobile robots using a monocular camera. In: IEEE/RSJ international conference on intelligent robots and systems, Taipei, 18–22 October 2010, pp. 3706–3711. New York: IEEE.
8.
ChoiJMLeeSJWonM.Self-learning navigation algorithm for vision-based mobile robots using machine learning algorithms. J Mech Sci Technol2011; 25: 247–254.
9.
De CristóforisPNitscheMAKrajníkT, et al. Real-time monocular image-based path detection. J Real-Time Image Pr2013; 11: 335–348.
10.
ZiaeiZOftadehRMattilaJ. Global path planning with obstacle avoidance for omnidirectional mobile robot using overhead camera. In: IEEE international conference on mechatronics and automation, Tianjin, China, 3–6 August 2014, pp. 697–704. New York: IEEE.
11.
SimbaKRUchiyamaNAldibajaM, et al. Vision-based smooth obstacle avoidance motion trajectory generation for autonomous mobile robots using Bézier curves. Proc IMechE, Part C: J Mechanical Engineering Science2017; 231: 541–554.
12.
BallDUpcroftBWyethG, et al. Vision-based obstacle detection and navigation for an agricultural robot. J Field Robot2017; 33: 1107–1130.
13.
SinghRBeraTK.Fuzzy logic controller for obstacle avoidance of mobile robot. Int J Nonlin Sci Num2019; 20: 51–62.
14.
FerroMPaolilloACherubiniA, et al. Vision-based navigation of omnidirectional mobile robots. IEEE Robot Autom Lett2019; 4: 2691–2699.
15.
KalogeitonVSIoannidisKSirakoulisGC, et al. Real-time active SLAM and obstacle avoidance for an autonomous robot based on stereo vision. Cybernet Syst2019; 50: 239–260.
16.
Sánchez-FerreiraCMoriJYFariasMC, et al. A real-time stereo vision system for distance measurement and underwater image restoration. J Braz Soc Mech Sci2016; 38: 2039–2049.
17.
MaohaiLHanWLiningS, et al. Robust omnidirectional mobile robot topological navigation system using omnidirectional vision. Eng Appl Artif Intel2013; 26: 1942–1952.
18.
PradoÁJMichałekMMCheeinFA. Machine-learning based approaches for self-tuning trajectory tracking controllers under terrain changes in repetitive tasks. Eng Appl Artif Intel2018; 67: 63–80.
19.
UçarADemirYGüzelişC.Object recognition and detection with deep learning for autonomous driving applications. Simulation2017; 93: 759–769.
20.
WangYde SilvaCW.A machine-learning approach to multi-robot coordination. Eng Appl Artif Intel2008; 21: 470–484.
21.
ViolaPJonesM.Robust real-time object detection. Int J Comput Vision2001; 4: 34–47.
22.
SongWLiuHXiangB, et al. Application of the Haar classifier in obstacle target detection. In: WangSX (ed.) Current trends in computer science and mechanical automation, vol. 2. Berlin: De Gruyter Open, 2017, pp. 605–614.
MukherjeeAKarmakarRSamantarayAK.Bond graph in modelling, simulation and fault identification. Boca Raton, FL: CRC Press, 2006.
25.
SinghRBeraTK.Fault detection, isolation and reconfiguration of a bipedal-legged robot. Simulation2019; 95: 955–977.
26.
SinghRBeraTK.Bond graph approach for dynamic modeling of the biped robot and application to obstacle avoidance. J Braz Soc Mech Sci2019; 41: 444.
27.
ChattiNOuld-BouamamaBGehinAL, et al. Signed bond graph for multiple faults diagnosis. Eng Appl Artif Intel2014; 36: 134–147.
28.
ChattiNGehinALOuld-BouamamaB, et al. Functional and behavior models for the supervision of an intelligent and autonomous system. IEEE T Autom Sci Eng2013; 10: 431–445.
29.
JahanbinZGhafariASEbrahimiA, et al. Multi-body simulation of a flapping-wing robot using an efficient dynamical model. J Braz Soc Mech Sci2016; 38: 133–149.
LiuFWangJLiuB, et al. Low computation and high efficiency Sobel edge detector for robot vision. In: 2021 IEEE international conference on real-time computing and robotics (RCAR), Xining, China, 15–19 July 2021, pp. 684–689. New York: IEEE.
32.
PacejkaHB.Modelling complex vehicle systems using bond graphs. J Frankl Inst1985; 319: 67–81.
