Modelling and control strategies in path tracking control for autonomous tracked vehicles: A review of state of the art and challenges

Ruslan, Noor Amira Ilyanie; Amer, Noor Hafizah; Hudha, Khisbullah; Kadir, Zulkiffli Abdul; Ishak, Saiddi Ali Firdaus Mohamed; Dardin, S.

doi:10.1016/j.jterra.2022.10.003

Cited by 32 publications

(10 citation statements)

References 49 publications

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“…This makes path tracking also important for autonomous robot navigation which is inseparable from path planning. This is supported by research [5], mobile robot tracking control is another important field of study in recent years.…”

Section: Introductionmentioning

confidence: 86%

Problem solving path planning and path tracking in a 3 DOF hexapod robot using the RRT* algorithm with path optimization and Pose-to-Pose

Suwoyo,

Burhanudin,

Tian

et al. 2024

Sinergi

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Path planning is one of the most fundamental problems that must be solved before a robot can navigate and explore autonomously. Path planning needs to be integrated with path tracking to be applied to autonomous robots. This makes path tracking also important for autonomous robot navigation which cannot be separated from path planning. There are two path planning methods, the first is search-based method, the second is sampling-based method. Both have their own advantages, but the popular and commonly used sampling-based algorithm due to its fast convergence is preferred in path planning. The RRT* algorithm was developed. This improvement initiated a major civilization in sampling-based algorithms, namely parent node selection and rewiring in RRT. Although there has been an improvement in optimality, RRT* still doesn't provide the distance optimality value as expected, due to its character that is still adopted from RRT. The resulting path is still suboptimal and not smooth (jagged). On the other side, Path tracking has several methods, however, these path tracking methods are difficult to apply to autonomous robots and need to be adapted to the robot used. Based on the description above, there are still problems with path planning, namely paths that are still less than optimal and convergence that is still slow. This research will add a way to shorten the distance in the RRT* algorithm with the triangular inequality method. Meanwhile, for path tracking, we will apply the pose-to-pose method, which follows the waypoint that has been made by path planning.

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Section: Introductionmentioning

confidence: 86%

Problem solving path planning and path tracking in a 3 DOF hexapod robot using the RRT* algorithm with path optimization and Pose-to-Pose

Suwoyo,

Burhanudin,

Tian

et al. 2024

Sinergi

View full text Add to dashboard Cite

show abstract

“…In the last decade, the exponential growth of artificial intelligence, machine learning and other deep learning algorithms [12] have contributed for the development of many advanced UGVs with improved capabilities, e.g., sensing and control. Some relevant state of the art reviews in the civil and military sectors are available in the current literature [13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Conceptual Design of an Unmanned Electrical Amphibious Vehicle for Ocean and Land Surveillance

Policarpo,

Lourenço,

Anastácio

et al. 2024

Preprint

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Unmanned vehicles (UVs) have become increasingly important in various scenarios of civil and military operations. The present work aims at the conceptual design of a modular Amphibious Unmanned Ground Vehicle (A-UGV), that can be easily adapted for different types of land and/or water missions, with low monetary cost (&lt;5k€, without sensors). Basing the design on the needs highlighted in the 2021 review of the Strategic Directive of the Portuguese Navy, the necessary specifications and requirements are established for two mission scenarios. Then a market research analysis focused on vehicles currently available, and their technological advances, is conducted to identify existing UVs solutions and respective characteristics/capabilities of interest to the current work. To study and define the geometry of the hull and the configuration of the A-UGV itself, preliminary computational structural and fluid analyses are carried out to ensure it complies with the specifications initially established. As a result, one obtains a fully electric vehicle, with approximate dimensions of 1050×670×450mm (length-width-height), enabled with 6×6 traction capable of reaching 20km/h in land, possesses amphibious capabilities of independent propulsion in water up to 8kts, and with an estimated autonomy of over 60 minutes.

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“…In [16], the MPC‐HDDC hierarchical dynamic drifting controller is constructed, which enables integrated control of drifting and typical turning manoeuvres, dramatically improves the manoeuvring limits of automated vehicles, and has high tracking accuracy. Especially in recent years, with the continuous improvement of computer performance [17], MPC controllers has found extensive application in the realm of automated driving.…”

Section: Introductionmentioning

confidence: 99%

Path tracking control of automated vehicles based on adaptive MPC in variable scenarios

Liu,

Ou,

Yan

et al. 2024

IET Intelligent Trans Sys

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For complex and dynamic high‐speed driving scenarios, an adaptive model predictive control (MPC) controller is designed to ensure effective path tracking for automated vehicles. Firstly, in order to prevent model mismatch in the MPC controller, a tire cornering stiffness estimation algorithm is designed and a soft constraint on slip angle is added to further enhance the controller's precision in tracking trajectories and the vehicle's driving stability. Secondly, the improved particle swarm optimization (IPSO) method with dynamic weights and penalty functions is suggested to address the issue of insufficient accuracy in solving quadratic programming. Additionally, the standard particle swarm optimization (PSO) algorithm is used to seek the most suitable time horizon parameters offline to obtain the best time horizon data set under different vehicle speeds and adhesion coefficients, and then it is optimized online by an adaptive network‐based fuzzy inference system (ANFIS) to enhance the model predictive controller's adaptability in different operating conditions. Finally, simulation experiments are conducted under three different operating conditions: docked roads, split roads, and variable vehicle speeds. The results indicate that the designed adaptive MPC controller can accurately and stably track the reference trajectory in various scenarios.

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Modelling and control strategies in path tracking control for autonomous tracked vehicles: A review of state of the art and challenges

Cited by 32 publications

References 49 publications

Problem solving path planning and path tracking in a 3 DOF hexapod robot using the RRT* algorithm with path optimization and Pose-to-Pose

Problem solving path planning and path tracking in a 3 DOF hexapod robot using the RRT* algorithm with path optimization and Pose-to-Pose

Conceptual Design of an Unmanned Electrical Amphibious Vehicle for Ocean and Land Surveillance

Path tracking control of automated vehicles based on adaptive MPC in variable scenarios

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