Presentation

2. Relevant Studies : Numerical/Deterministic Approach
The numerical analysis involves the implementation of algorithms for
obtaining numerical solutions. It engages the theoretical mathematical
analysis. The concept started with the DARPA Urban Challenge.
Vehicle trajectory optimization is treated as an optimal control problem [1] .
optimal control problems must be solved numerically
Methods involved : Direct global collocation Pseudo spectral [2],Variable order
orthogonal collocation method [3], Parametric characterization [4], Runge–
Kutta method [5], Iterative method[6]
Different controllers has been designed for autonomous guided vehicles, e.g., PID controller [7],
sliding mode controller [8], linear quadratic regulator [9], fuzzy logic controller [10], backstepping
controller [11], adaptive control [12], and pure pursuit controller [13]
[7] Li, X., Luo, C., Xu, Y. and Li, P., 2016, August. A Fuzzy PID controller applied in AGV control system. In 2016 International Conference on Advanced Robotics and Mechatronics (ICARM) (pp. 555-
560). IEEE.
[8] Matveev, A.S., Wang, C. and Savkin, A.V., 2012. Real-time navigation of mobile robots in problems of border patrolling and avoiding collisions with moving and deforming obstacles. Robotics and
Autonomous systems, 60(6), pp.769-788.
[9] Li, Y., Wang, X.N., Li, S.J. and Zhu, J., 2014. Lqr based trajectory tracking control for forked agv. In Applied Mechanics and Materials (Vol. 577, pp. 447-451). Trans Tech Publications Ltd.
[10] Castillo, O., Aguilar, L.T. and Cárdenas, S., 2006. Fuzzy Logic Tracking Control for Unicycle Mobile Robots. Engineering Letters, 13(3).
[11] Dumitrascu, B., Filipescu, A. and Minzu, V., 2011, October. Backstepping control of wheeled mobile robots. In 15th International Conference on System Theory, Control and Computing (pp. 1-6).
IEEE.
[12] Narendra, K.S. and Valavani, L.S., 1978. Stable adaptive controller design--Direct control. IEEE Transactions on Automatic Control, 23(4), pp.570-583.
[13] Liu, J., Yang, Z., Huang, Z., Li, W., Dang, S. and Li, H., 2021, July. Simulation Performance Evaluation of Pure Pursuit, Stanley, LQR, MPC Controller for Autonomous Vehicles. In 2021 IEEE
International Conference on Real-time Computing and Robotics (RCAR) (pp. 1444-1449). IEEE.
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For
autonomous
vehicles,
these
methods
are
employed
to
solve
path
planning,
trajectory
optimization,
and
numerous
other
vehicle
variants
[1] Jung, I. K., Hong, K. B., Hong, S. K., & Hong, S. C. (1999, July). Path planning of mobile robot using neural network. In ISIE'99. Proceedings of the IEEE
International Symposium on Industrial Electronics (Cat. No. 99TH8465) (Vol. 3, pp. 979-983). IEEE.
[2] Gao, X.Z., Hou, Z.X., Guo, Z., Fan, R.F. and Chen, X.Q., 2014. Analysis and design of guidance-strategy for dynamic soaring with UAVs. Control Engineering Practice, 32, pp.218-226.
[3] Zollars, M.D. and Cobb, R.G., 2017, October. Simplex Methods for Optimal Control of Unmanned Aircraft Flight Trajectories. In Dynamic Systems and Control Conference (Vol.
58295, p. V003T39A001). American Society of Mechanical Engineers.
[4] Mammarella, M. and Capello, E., 2018, June. A robust MPC-based autopilot for mini UAVs. In 2018 International Conference on Unmanned Aircraft Systems (ICUAS) (pp. 1227-
1235). IEEE.
[5 ] Alsultan, T., M. Ali, H. and Y. Hamid, Q., 2018. A numerical approach for solving problems in robotic arm movement. Production & Manufacturing Research, 6(1), pp.385-395.
[6] Saudi, A. and Sulaiman, J., 2010, June. Numerical technique for robot path planning using four Point-EG iterative method. In 2010 International Symposium on Information
Technology (Vol. 2, pp. 831-836). IEEE.

3. Relevant Studies : Bio-Inspired Approach
The bioinspired methods are derived from the social hierarchy of birds and
animals, which include ants, bees, birds, and genetic algorithms [1-4]
Development of bio-inspired algorithms for optimizing path length [6]
Used in path planning and motion control of the vehicle[7]
[7] Li, L., Wang, F.Y. and Zhou, Q., 2006. Integrated longitudinal and lateral tire/road friction modeling and monitoring for vehicle motion control. IEEE Transactions on intelligent
transportation systems, 7(1), pp.1-19.
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Techniques were utilized on optimizing the path-related issues [5]
They are designed based on how they behave in nature. Their natural traits
are modeled into the form of an algorithm
[1] Karaboga, D., 2010. Artificial bee colony algorithm. scholarpedia, 5(3), p.6915.
[2] Poli, R., Kennedy, J. and Blackwell, T., 2007. Particle swarm optimization. Swarm intelligence, 1(1), pp.33-57.
[3] Mirjalili, S., 2019. Genetic algorithm. In Evolutionary algorithms and neural networks (pp. 43-55). Springer, Cham.
[4] Blum, C., 2005. Ant colony optimization: Introduction and recent trends. Physics of Life reviews, 2(4), pp.353-373.
[5] Campos-Macías, L., Gómez-Gutiérrez, D., Aldana-López, R., de la Guardia, R. and Parra-Vilchis, J.I., 2017. A hybrid method for online trajectory planning of mobile robots in
cluttered environments. IEEE Robotics and Automation Letters, 2(2), pp.935-942.
[6] Lee, S.G. and Lee, S.W., 2013. Bio-Inspired Algorithm for the Shortest Path According to the Maximum Time for Each Trial. In Advanced Materials Research (Vol. 717, pp. 455-459).
Trans Tech Publications Ltd.

4. Relevant Studies : Hybrid Approach
Hybridization :To integrate techniques for achieving higher accuracy
and designing the system more efficiently Algorithms/Techniques [1]
Exploratory studies on blending path planning capabilities in
autonomous vehicles [2-3]
Multi-objective optimization: path planning for autonomous vehicles is
a MOP problem; requiring more than one objective [4]
Aggravating the merits by utilizing the strengths and mitigating the
disadvantages and drawbacks of each technique.
E.g: proper integration of different methods can improve oscillations
and reduce noise and data uncertainties due to the local minima
problem associated with the APF method [5]
[5]Al-Mutib, K., Abdessemed, F., Faisal, M., Ramdane, H., Alsulaiman, M. and Bencherif, M., 2016, September. Obstacle avoidance using wall-following strategy for indoor mobile robots. In 2016 2nd
IEEE International Symposium on Robotics and Manufacturing Automation (ROMA) (pp. 1-6). IEEE.
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[1] Ju, M.Y., Wang, S.E. and Guo, J.H., 2014. Path planning using a hybrid evolutionary algorithm based on tree structure encoding. The Scientific World Journal, 2014.
[2] Li, J.; Deng, G.; Luo, C.; Lin, Q.; Yan, Q.; Ming, Z. A hybrid path planning method in unmanned air/ground vehicle (UAV/UGV) cooperative systems. IEEE Trans. Veh. Technol. 2016, 65,
9585–9596.
[3] Châari, I.; Koubaa, A.; Bennaceur, H.; Trigui, S.; Al-Shalfan, K. SmartPATH: A hybrid ACO-GA algorithm for robot path planning. In Proceedings of the 2012 IEEE congress on evolutionary
computation, Brisbane, QLD, Australia, 10–15 June 2012; pp. 1–8
[4] Qu, C.; Gai, W.; Zhang, J.; Zhong, M. A novel hybrid grey wolf optimizer algorithm for unmanned aerial vehicle (UAV) path planning. Knowl.-Based Syst. 2020, 194, 105530