Reconfigurable unmanned vehicle unit (RUVU) exhibits three distinct steering modes: Ackermann steering, Diagonal steering, and In-Situ steering. This arises from its capacity for All-Wheel-Independent-Steering (AWIS) and All-Wheel-Independent-Driving (AWID). Given its unique motion characteristics, there is currently a lack of established methods to assist a RUVU in autonomously planning trajectories. In response to this deficiency, this paper proposes the Multi-Steering Modes Trajectory Planning algorithm (MSMTP) based on the framework of optimal control problem (OCP). The approach innovatively formulates the kinematics and Steering-Mode-Switch theory for vehicles with multi-steering modes. Additionally, a safe traveling corridor (STC) is implemented to guarantee non-conflicting interaction with the surrounding environment. Ultimately, the Bonmin solver is utilized to derive optimal results. Our findings demonstrate the effectiveness of the proposed algorithm in RUVU’s trajectory planning. When comparing the trajectory of RUVU to that of a vehicle only with Ackermann steering, the results also reveal that RUVU has the advantage of reaching its destination in a shorter time, especially in narrow environments.
GabrichBSaldanaDKumarV, et al. A flying gripper based on cuboid modular robots. In: 2018 IEEE international conference on robotics and automation (ICRA), Brisbane, QLD, Australia, pp.7024–7030. IEEE.
2.
ZhaoMShiFAnzaiT, et al. Online motion planning for deforming maneuvering and manipulation by multilinked aerial robot based on differential kinematics. IEEE Robot Autom Lett2020; 5(2): 1602–1609.
3.
BouhabzaKGuiatniMBouzidY, et al. Aco-based optimal mimo sliding mode controller design for a new reconfigurable unmanned aerial vehicle. Eng Proc2023; 29(1): 9.
4.
XiaoNXiaoYYeD, et al. Adaptive differential game for modular reconfigurable satellites based on neural network observer. Aerosp Sci Technol2022; 128: 107759.
5.
AnJLiXZhangZ, et al. Path planning for self-collision avoidance of space modular self-reconfigurable satellites. Aerospace2022; 9(3): 141.
6.
HuGLiYLiX, et al. Modular self-reconfigurable spacecraft: development status, key technologies, and application prospect. Acta Astronaut2023; 207: 240–256.
7.
MaBJiangZLiuY, et al. Advances in space robots for on-orbit servicing: a comprehensive review. Adv Intell Syst2023; 5(8): 2200397.
8.
KyawPTLeAVVeerajagadheswarP, et al. Energy-efficient path planning of reconfigurable robots in complex environments. IEEE Trans Robot2022; 38(4): 2481–2494.
9.
SongRLiuLXuZ.Adaptive dynamic event-triggered control for constrained modular reconfigurable robot. Knowl Based Syst2022; 254: 109624.
10.
KalimuthuMPathmakumarTHayatAA, et al. Optimal morphologies of n-omino-based reconfigurable robot for area coverage task using metaheuristic optimization. Mathematics2023; 11(4): 948.
11.
WadaMMoriS. Holonomic and omnidirectional vehicle with conventional tires. In: Proceedings of IEEE international conference on robotics and automation, Minneapolis, MN, USA, vol. 4, pp.3671–3676. IEEE.
12.
TaheriHZhaoCX.Omnidirectional mobile robots, mechanisms and navigation approaches. Mech Mach Theory2020; 153: 103958.
13.
NiJHuJXiangC.Robust control in diagonal move steer mode and experiment on an X-by-wire UGV. IEEE/ASME Trans Mechatron2019; 24(2): 572–584.
14.
DijkstraEW. A note on two problems in connexion with graphs. Numer. Math1959; 1: 269–271.
15.
HartPENilssonNJRaphaelB.A formal basis for the heuristic determination of minimum cost paths. IEEE Trans Syst Sci Cybern1968; 4(2): 100–107.
16.
StentzA. Optimal and efficient path planning for partially-known environments. In: Proceedings of the 1994 IEEE international conference on robotics and automation, San Diego, CA, 1994, pp.3310–3317. IEEE.
17.
LaValleSM. Rapidly-exploring random trees: A new tool for path planning. Technical Report no. TR 98–11, Department of Computer Science, Iowa State University, Ames, Iowa, USA, 1998.
18.
UrmsonCSimmonsR. Approaches for heuristically biasing RRT growth. In: Proceedings 2003 IEEE/RSJ international conference on intelligent robots and systems (IROS 2003) Las Vegas, NV, USA, (Cat. No. 03CH37453), vol. 2, pp. 1178–1183. IEEE.
19.
KuffnerJJLaValleSM. RRT-connect: an efficient approach to single-query path planning. In: Proceedings 2000 ICRA. Millennium conference. IEEE international conference on robotics and automation. Symposia proceedings (Cat. No. 00CH37065), San Francisco, CA, 2000, vol. 2, pp.995–1001. IEEE.
20.
KaramanSWalterMRPerezA, et al. Anytime motion planning using the RRT. In: 2011 IEEE international conference on robotics and automation, Shanghai, China, pp.1478–1483. IEEE.
21.
ChengPLaValleSM. Reducing metric sensitivity in randomized trajectory design. In: Proceedings 2001 IEEE/RSJ international conference on intelligent robots and systems. Expanding the societal role of robotics in the the next Millennium (Cat. No. 01CH37180), Maui, HI, USA, vol. 1, pp. 43–48. IEEE.
22.
JailletLYershovaALaValleSM, et al. Adaptive tuning of the sampling domain for dynamic-domain RRTs. In: 2005 IEEE/RSJ international conference on intelligent robots and systems, Edmonton, AB, Canada, 2005, pp.2851–2856.
23.
GuoXCaoYZhouJ, et al. HDM-RRT: a fast hd-map-guided motion planning algorithm for autonomous driving in the campus environment. Remote Sens2023; 15(2): 487.
24.
DolgovDThrunSMontemerloM, et al. Practical search techniques in path planning for autonomous driving. Ann Arbor2008; 1001(48105): 18–80.
25.
JaradatMAKAl-RousanMQuadanL. Reinforcement based mobile robot navigation in dynamic environment. Robot Comput Integr Manuf2011; 27(1): 135–149.
26.
MnihVKavukcuogluKSilverD, et al. Playing atari with deep reinforcement learning. arXiv Preprint arXiv:1312.5602, 2013.
27.
MaBXieZYangX, et al. Neurodynamics-based configuration transformation with engineering application to robot manipulators using two intelligent approaches. Eng Appl Artif Intell2023; 120: 105861.
28.
ZhangEZhangRMasoudN.Predictive trajectory planning for autonomous vehicles at intersections using reinforcement learning. Transp Res Part C Emerg Technol2023; 149: 104063.
29.
ChengYHuXChenK, et al. Online longitudinal trajectory planning for connected and autonomous vehicles in mixed traffic flow with deep reinforcement learning approach. J Intell Transp Syst2023; 27(3): 396–410.
30.
WangJZhangQZhaoD, et al. Lane change decision-making through deep reinforcement learning with rule-based constraints. In: 2019 international joint conference on neural networks (IJCNN), Budapest, Hungary, pp.1–6. IEEE.
MaBXieZZhanB, et al. Actual shape-based obstacle avoidance synthesized by velocity–acceleration minimization for redundant manipulators: an optimization perspective. IEEE Trans Syst Man Cybern Syst2023; 53(10): 6460–6474.
33.
LiBAcarmanTZhangY, et al. Optimization-based trajectory planning for autonomous parking with irregularly placed obstacles: a lightweight iterative framework. IEEE Trans Intell Transp Syst2021; 23(8): 11970–11981.
34.
LiBFanLOuyangY, et al. Online competition of trajectory planning for automated parking: benchmarks, achievements, learned lessons, and future perspectives. IEEE Trans Intell Veh2022; 8(1): 16–21.
35.
LiBOuyangYLiL, et al. Autonomous driving on curvy roads without reliance on frenet frame: a cartesian-based trajectory planning method. IEEE Trans Intell Transp Syst2022; 23(9): 15729–15741.
36.
LiBWangYMaS, et al. Adaptive pure pursuit: a real-time path planner using tracking controllers to plan safe and kinematically feasible paths. IEEE Trans Intell Veh2023; 8(9): 4155–4168.
37.
CenHLiBAcarmanT, et al. Optimization-based maneuver planning for a tractor-trailer vehicle in complex environments using safe travel corridors. In: 2021 IEEE intelligent vehicles symposium (IV), Nagoya, Japan, pp.974–979. IEEE.
38.
LiBLiLAcarmanT, et al. Optimization-based maneuver planning for a tractor-trailer vehicle in a curvy tunnel: a weak reliance on sampling and search. IEEE Robot Autom Lett2021; 7(2): 706–713.
39.
LiBOuyangYLiX, et al. Mixed-integer and conditional trajectory planning for an autonomous mining truck in loading/dumping scenarios: a global optimization approach. IEEE Trans Intell Veh2022; 8(2): 1512–1522.
