Obstacle avoidance strategy is important to ensure the driving safety of unmanned ground vehicles. In the presence of static and moving obstacles, it is challenging for the unmanned ground vehicles to plan and track the collision-free paths. This paper proposes an obstacle avoidance strategy consists of the path planning and the robust fuzzy output-feedback control. A path planner is formed to generate the collision-free paths that avoid static and moving obstacles. The quintic polynomial curves are employed for path generation considering computational efficiency and ride comfort. Then, a robust fuzzy output-feedback controller is designed to track the planned paths. The Takagi–Sugeno (T–S) fuzzy modeling technique is utilized to handle the system variables when forming the vehicle dynamic model. The robust output-feedback control approach is used to track the planned paths without using the lateral velocity signal. The proposed obstacle avoidance strategy is validated in CarSim® simulations. The simulation results show the unmanned ground vehicle can avoid the static and moving obstacles by applying the designed path planning and robust fuzzy output-feedback control approaches.
NiJHuJXiangC. Robust path following control at driving/handling limits of an autonomous electric racecar. IEEE Trans Veh Technol2019; 68(6): 5518–5526.
2.
SahooSSubramanianSCSrivastavaS. Evaluation of the transient response and implementation of a heading-angle controller for an autonomous ground vehicle. Proc IMechE, Part D: J Automobile Engineering2016; 230(8): 1040–1056.
3.
GhaffariAKhodayariAFarajiM. Optimal fuzzy control system design for car-following behaviour based on the driver–vehicle unit online delays in a real traffic flow. Proc IMechE, Part D: J Automobile Engineering2014; 228(12): 1440–1451.
4.
GuoJHuPWangR. Nonlinear coordinated steering and braking control of vision-based autonomous vehicles in emergency obstacle avoidance. IEEE Trans Intell Transp Syst2016; 17(11): 3230–3240.
5.
NiJHuJXiangC. A review for design and dynamics control of unmanned ground vehicle. Proc ImechE, Part D: J Automobile Engineering. Epub ahead of print 24 April 2020. DOI: 10.1177/0954407020912097.
6.
GonzálezDPérezJMilanésV, et al. A review of motion planning techniques for automated vehicles. IEEE Trans Intell Transp Syst. 2016; 17(4): 1135–1145.
7.
LiGYamashitaAAsamaH, et al. An efficient improved artificial potential field based regression search method for robot path planning. In: 2012 IEEE international conference on mechatronics and automation, Chengdu, China, 5–8 August 2012, pp.1227–1232. New York: IEEE.
8.
DolgovDThrunSMontemerloM, et al. Path planning for autonomous vehicles in unknown semi-structured environments. Int J Robot Res2010; 29(5): 485–501.
9.
HowardTMGreenCJKellyA, et al. State space sampling of feasible motions for high-performance mobile robot navigation in complex environments. J Field Robot2008; 25(6–7): 325–345.
10.
KavrakiLSvestkaPOvermarsMH. Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Trans Rob Autom1994; 12(4): 566–580.
11.
KuwataYTeoJFioreG, et al. Real-time motion planning with applications to autonomous urban driving. IEEE Trans Control Syst Technol2009; 17(5): 1105–1118.
12.
VillagraJMilanésVPérezJ, et al. Smooth path and speed planning for an automated public transport vehicle. Rob Auton Syst2012; 60(2): 252–265.
13.
KoganDMurrayR. Optimization-based navigation for the DARPA Grand Challenge. In: Conference on decision and control (CDC), San Diego, CA, December2006.
14.
QinYWangZXiangCHashemiEKhajepourAHuangY. Speed independent road classification strategy based on vehicle response: Theory and experimental validation. Mech Syst Signal Process. 2019Feb15; 117: 653–66.
15.
ChenYChaoLWenboC. A cooperative driving strategy based on velocity prediction for connected vehicles with robust path-following control. IEEE Internet Things J2020; 7(5): 3822–3832.
16.
ChenYZhaJWangJ. An autonomous T-intersection driving strategy considering oncoming vehicles based on connected vehicle technology. IEEE ASME Trans Mechatron2019; 25(6): 2779–2790.
17.
HuCJingHWangR, et al. Robust H∞ output-feedback control for path following of autonomous ground vehicles. Mech Syst Signal Process2016; 70: 414–427.
18.
GuoJWangJLuoY, et al. Robust lateral control of autonomous four-wheel independent drive electric vehicles considering the roll effects and actuator faults. Mech Syst Signal Process2020; 143: 106773.
19.
ChenYStoutCJoshiA, et al. Driver-assistance lateral motion control for in-wheel-motor-driven electric ground vehicles subject to small torque variation. IEEE Trans Veh Technol2018; 67(8): 6838–6850.
20.
NiJHuJXiangC. Robust control in diagonal move steer mode and experiment on an X-by-wire UGV. IEEE ASME Trans Mechatron2019; 29(2):572–584.
21.
NiJWangWHuJ, et al. Relaxed static stability for four-wheel independently actuated ground vehicle. Mech Syst Signal Process2019; 127: 654–666.
22.
GaoYGrayATsengHE, et al. A tube-based robust nonlinear predictive control approach to semiautonomous ground vehicles. Veh Syst Dyn2014; 52(6): 802–823.
23.
GuoJLuoYLiK. An adaptive hierarchical trajectory following control approach of autonomous four-wheel independent drive electric vehicles. IEEE Trans Intell Transp Syst2017; 19(8): 2482–2492.
24.
KapaniaNRGerdesJC. Path tracking of highly dynamic autonomous vehicle trajectories via iterative learning control. In: 2015 American control conference (ACC), Chicago, IL, 1–3 July 2015, pp.2753–2758. New York: IEEE.
25.
HuCWangZTaghavifarH, et al. MME-EKF-based path-tracking control of autonomous vehicles considering input saturation. IEEE Trans Veh Technol. 2019; 68(6): 5246–5259.
26.
GuoJLuoYLiK. Robust gain-scheduling automatic steering control of unmanned ground vehicles under velocity-varying motion. Veh Syst Dyn2019; 57(4): 595–616.
27.
WerlingMZieglerJKammelS, et al. Optimal trajectory generation for dynamic street scenarios in a Frenét Frame. In: 2010 IEEE international conference on robotics and automation, Anchorage, AK, 3–7 May 2010, pp.987–993. New York: IEEE.
28.
QinYWangZXiangC, et al. A novel global sensitivity analysis on the observation accuracy of the coupled vehicle model. Veh Syst Dyn. 2019; 57(10): 1445–1466.
29.
HuCWangRYanF, et al. Should the desired heading in path following of autonomous vehicles be the tangent direction of the desired path?IEEE Trans Intell Transp Syst2015; 16(6): 3084–3094.
30.
NiJHuJXiangC. Envelope control for four-wheel independently actuated autonomous ground vehicle through AFS/DYC integrated control. IEEE Trans Veh Technol2017; 66(11): 9712–9726.
31.
JinXYuZYinG, et al. Improving vehicle handling stability based on combined AFS and DYC system via robust Takagi-Sugeno fuzzy control. IEEE Trans Intell Transp Syst2018; 19(8): 2696–2707.
32.
ChenYHuCWangJ. Human-centered tracking control for autonomous vehicle with driver cut-in behavior prediction. IEEE Trans Veh Technol2019; 68(9): 8461–8471.
33.
ChenYWangJ. In-wheel-motor-driven electric vehicles motion control methods considering motor thermal protection. J Dyn Syst Meas Control2018; 141(1): 011015.
