A simultaneous path planning and positioning based on artificial distribution of landmarks in a GNSS denied environment
Abstract
In recent years, exploration operations by autonomous robots are expanding into unknown environments on Earth, under the sea, or even on other planets. This paper proposes the idea of Concurrent Path Planning and Positioning (CPPAP) using artificially distributed landmarks, while no GNSS signal is available. The method encompasses an observability-based direction search algorithm for path planning in parallel with Simultaneous Localization and Mapping (SLAM) for localization. Most of the path planning methods utilize offline algorithms; however, the proposed method determines the robot’s direction of motion in real-time, concurrently with the positioning tasks by the inclusion of the system observability, related to the features’ distribution. Same as in all feature-based SLAMs, features play an important role in determination of the most observable direction, and hence the direction of the robot’s motion. Moreover, the effectiveness of the distribution of the features and their pattern in the proposed method is investigated. To evaluate the efficiency and accuracy of the CPPAP, outcomes are compared with an existing random SLAM.
Keyword : concurrent path planning and positioning (CPPAP), simultaneous localization and mapping (SLAM), Eigenvalue observability analysis, artificial landmarks, GNSS denied environments
How to Cite
Elahian, S., Amiri Atashgah, M.-A., & Tarverdizadeh, B. (2023). A simultaneous path planning and positioning based on artificial distribution of landmarks in a GNSS denied environment. Aviation, 27(1), 36–46. https://doi.org/10.3846/aviation.2023.18461
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Aguilar-López, R., Mata-Machuca, J. L., & Martinez-Guerra, R. (2010). On the observability for a class of nonlinear (bio)chemical systems. International Journal of Chemical Reactor Engineering, 8(1). https://doi.org/10.2202/1542-6580.2052
Aggarwal, S., & Kumar, N. (2020). Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges. Computer Communications, 149, 270–299. https://doi.org/10.1016/j.comcom.2019.10.014
Aminzadeh, A., & Amiri Atashgah, M. A. (2018). Feature article: Implementation and performance evaluation of optical flow navigation system under specific conditions for a flying robot. IEEE Aerospace and Electronic Systems Magazine, 33(11), 20–28. https://doi.org/10.1109/MAES.2018.170075
Amiri Atashgah, M. A., & Malaek, S. M. B. (2013). Prediction of aerial-image motion blurs due to the flying vehicle dynamics and camera characteristics in a virtual environment. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 227(7), 1055–1067. https://doi.org/10.1177/0954410012450107
Bahraini, M. S., Bozorg, M., & Rad, A. B. (2018). A new adaptive UKF algorithm to improve the accuracy of SLAM. International Journal of Robotics, 5(1), 35–46.
Bakdi, A., Hentout, A., Boutami, H., Maoudj, A., Hachour, O., & Bouzouia, B. (2017). Optimal path planning and execution for mobile robots using genetic algorithm and adaptive fuzzy-logic control. Robotics and Autonomous Systems, 89, 95–109. https://doi.org/10.1016/j.robot.2016.12.008
Barrau, A., & Bonnabel, S. (2015). An EKF-SLAM algorithm with consistency properties. Cornell University. http://arxiv.org/abs/1510.06263
Batista, P., Silvestre, C., & Oliveira, P. (2011). Single range aided navigation and source localization: Observability and filter design. Systems & Control Letters, 60(8), 665–673. https://doi.org/10.1016/j.sysconle.2011.05.004
Bryson, M., & Sukkarieh, S. (2008). Observability analysis and active control for airborne SLAM. IEEE Transactions on Aerospace and Electronic Systems, 44(1), 261–280. https://doi.org/10.1109/TAES.2008.4517003
Butcher, E. A., Wang, J., & Lovell, T. A. (2017). On Kalman filtering and observability in nonlinear sequential relative orbit estimation. Journal of Guidance, Control, and Dynamics, 40(9), 2167–2182. https://doi.org/10.2514/1.G002702
Carlone, L., Du, J., Kaouk Ng, M., Bona, B., & Indri, M. (2014). Active SLAM and exploration with particle filters using Kullback-Leibler divergence. Journal of Intelligent and Robotic Systems: Theory and Applications, 75(2), 291–311. https://doi.org/10.1007/s10846-013-9981-9
Chakraborty, A., Misra, S., Sharma, R., & Taylor, C. N. (2017). Observability conditions for switching sensing topology for cooperative localization. Unmanned Systems, 5(3), 141–157. https://doi.org/10.1142/S2301385017400039
Chen, C. (1999). Linear system theory and design. Oxford University Press.
Chen, Y. B., Luo, G. C., Mei, Y. S., Yu, J. Q., & Su, X. L. (2014). UAV path planning using artificial potential field method updated by optimal control theory. International Journal of Systems Science, 47(6), 1407–1420. https://doi.org/10.1080/00207721.2014.929191
Clemens, J., Reineking, T., & Kluth, T. (2016). An evidential approach to SLAM, path planning, and active exploration. International Journal of Approximate Reasoning, 73, 1–26. https://doi.org/10.1016/j.ijar.2016.02.003
Fakoor, M., Kosari, A., & Jafarzadeh, M. (2016). Humanoid robot path planning with fuzzy Markov decision processes. Journal of Applied Research and Technology, 14(5), 300–310. https://doi.org/10.1016/j.jart.2016.06.006
Fethi, D., Nemra, A., Louadj, K., & Hamerlain, M. (2018). Simultaneous localization, mapping, and path planning for unmanned vehicle using optimal control. Advances in Mechanical Engineering, 10(1). https://doi.org/10.1177/1687814017736653
Fraundorfer, F., & Scaramuzza, D. (2012). Visual odometry: Matching, robustness, optimization, and applications. IEEE Robotics & Automation Magazine, 19(2). https://doi.org/10.1109/MRA.2012.2182810
Fu, B., Chen, L., Zhou, Y., Zheng, D., Wei, Z., Dai, J., & Pan, H. (2018). An improved A* algorithm for the industrial robot path planning with high success rate and short length. Robotics and Autonomous Systems, 106, 26–37. https://doi.org/10.1016/j.robot.2018.04.007
González, D., Pérez, J., Milanés, V., & Nashashibi, F. (2016). A review of motion planning techniques for automated vehicles. IEEE Transactions on Intelligent Transportation Systems, 17(4), 1135–1145. https://doi.org/10.1109/TITS.2015.2498841
Hahn, J., Edgar, T. F., Marquardt, W. (2003). Controllability and observability covariance matrices for the analysis and order reduction of stable nonlinear systems. Journal of Process Control, 13(2), 115–127. https://doi.org/10.1016/S0959-1524(02)00024-0
Ham, F. M., & Grover Brown, R. (1983). Observability, eigenvalues, and Kalman filtering. IEEE Transactions on Aerospace and Electronic Systems, AES-19(2), 269–273. https://doi.org/10.1109/TAES.1983.309446
Hasegawa, Y., & Fujimoto, Y. (2016). Experimental verification of path planning with SLAM. IEEJ Journal of Industry Applications, 5(3), 253–260. https://doi.org/10.1541/ieejjia.5.253
Hermann, R., Krener, A. (1977). Nonlinear controllability and observability. IEEE Transactions on Automatic Control, AC-22(5), 728–740. https://doi.org/10.1109/TAC.1977.1101601
Hesch, J. A., Kottas, D. G., Bowman, S. L., & Roumeliotis, S. I. (2013). Camera-IMU-based localization: Observability analysis and consistency improvement. The International Journal of Robotics Research, 33(1), 182–201. https://doi.org/10.1177/0278364913509675
Huang, G. P., Mourikis, A. I., Roumeliotis, S. I. (2008). Analysis and improvement of the consistency of extended Kalman filter based SLAM. In IEEE International Conference on Robotics and Automation (ICRA). IEEE Xplore. https://doi.org/10.1109/ROBOT.2008.4543252
Huang, G. P., Mourikis, A. I., & Roumeliotis, S. I. (2009). On the complexity and consistency of UKF-based SLAM. In 2009 IEEE International Conference on Robotics and Automation. IEEE. https://doi.org/10.1109/ROBOT.2009.5152793
Huang, L., Song, J., Zhang, Ch. (2017). Observability analysis and filter design for a vision inertial absolute navigation system for UAV using landmarks. Optik, 149, 455–468. https://doi.org/10.1016/j.ijleo.2017.09.060
Kalogeiton, V. S., Ioannidis, K., Sirakoulis, G. C., & Kosmatopoulos, E. B. (2019). Real-time active SLAM and obstacle avoidance for an autonomous robot based on stereo vision. Cybernetics and Systems, 50(3), 239–260. https://doi.org/10.1080/01969722.2018.1541599
Kurt-Yavuz, Z., & Yavuz, S. (2012). A comparison of EKF, UKF, FastSLAM2.0, and UKF-based FastSLAM algorithms. In 2012 IEEE 16th International Conference on Intelligent Engineering Systems (INES) (pp. 37–43). IEEE. https://doi.org/10.1109/INES.2012.6249866
Lall, S., Marsden, J. E., & Glavaški, S. (2002). A subspace approach to balanced truncation for model reduction of nonlinear control systems. International Journal of Robust and Nonlinear Control, 12(6), 519–535. https://doi.org/10.1002/rnc.657
Lee, K. W., Wijesoma, W. S., Ibanez Guzman, J. (2006). On the observability and observability analysis of SLAM. In 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE. https://doi.org/10.1109/IROS.2006.281646
Leonard, M. R., & Zoubir, A. M. (2019). Multi-Target tracking in distributed sensor networks using particle PHD filters. Signal Processing, 159, 130–146. https://doi.org/10.1016/j.sigpro.2019.01.020
Leung, C., Huang, S., & Dissanayake, G. (2006). Active SLAM using model predictive control and attractor based exploration. In IEEE International Conference on Intelligent Robots and Systems (pp. 5026–5031). IEEE. https://doi.org/10.1109/IROS.2006.282530
Lystianingrum, V., Hredzak, B., Agelidis, V. G., & Djanali, V. S. (2014). Observability degree criteria evaluation for temperature observability in a battery string towards optimal thermal sensors placement. In 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). IEEE. https://doi.org/10.1109/ISSNIP.2014.6827641
Mac, T. T., Copot, C., Tran, D. T., & De Keyser, R. (2016). Heuristic approaches in robot path planning: A survey. Robotics and Autonomous Systems, 86, 13–28. https://doi.org/10.1016/j.robot.2016.08.001
Maurovic, I., Seder, M., Lenac, K., & Petrovic, I. (2018). Path planning for active SLAM based on the D*algorithm with negative edge weights. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(8), 1321–1331. https://doi.org/10.1109/TSMC.2017.2668603
Myhre, N. (2018). Vision-aided navigation using tracked lankmarks [Doctoral Dissertations and Master’s Theses]. Embry-Riddle Aeronautical University. https://commons.erau.edu/edt/390
Nijmeijer, H., & van der Schaft, A. (1990). Nonlinear dynamical control systems. Springer. https://doi.org/10.1007/978-1-4757-2101-0
Niu, H., Savvaris, A., Tsourdos, A., & Ji, Z. (2019). Voronoi-visibility roadmap-based path planning algorithm for unmanned surface vehicles. The Journal of Navigation, 72(4), 850–874. https://doi.org/10.1017/S0373463318001005
Patle, B. K., Babu L, G., Pandey, A., Parhi, D. R. K., & Jagadeesh, A. (2019). A review: On path planning strategies for navigation of mobile robot. Defence Technology, 15(4), 582–606. https://doi.org/10.1016/j.dt.2019.04.011
Perez, A., Platt, R., Konidaris, G., Kaelbling, L., & Lozano-Perez, T. (2012). LQR-RRT*: Optimal sampling-based motion planning with automatically derived extension heuristics. In IEEE International Conference on Robotics and Automation (pp. 2537–2542). IEEE. https://doi.org/10.1109/ICRA.2012.6225177
Qu, C., Gai, W., Zhong, M., & Zhang, J. (2020). A novel reinforcement learning based grey wolf optimizer algorithm for unmanned aerial vehicles (UAVs) path planning. Applied Soft Computing, 89, 106099. https://doi.org/10.1016/j.asoc.2020.106099
Rasekhipour, Y., Khajepour, A., Chen, S. K., & Litkouhi, B. (2017). A potential field-based model predictive path-planning controller for autonomous road vehicles. IEEE Transactions on Intelligent Transportation Systems, 18(5), 1255–1267. https://doi.org/10.1109/TITS.2016.2604240
Serpas, M., Hackebeil, G., Laird, C., & Hahn, J. (2013). Sensor location for nonlinear dynamic systems via observability analysis and MAX-DET optimization. Computers and Chemical Engineering, 48, 105–112. https://doi.org/10.1016/j.compchemeng.2012.07.014
Sharma, R. (2014). Observability based control for cooperative localization. In 2014 International Conference on Unmanned Aircraft Systems (ICUAS) (pp. 134–139). IEEE. https://doi.org/10.1109/ICUAS.2014.6842248
Sharma, R., Beard, R. W., Taylor, C. N., & Quebe, S. (2012). Graph-based observability analysis of bearing-only cooperative localization. IEEE Transactions on Robotics, 28(2), 522–529. https://doi.org/10.1109/TRO.2011.2172699
Tao, T., Huang, Y., Sun, F., Wang, T. (2007). Motion planning for SLAM based on frontier exploration. In 2007 International Conference on Mechatronics and Automation. IEEE. https://doi.org/10.1109/ICMA.2007.4303879
Van Den Berg, F. W. J., Hoefsloot, H. C. J., Boelens, H. F. M., & Smilde, A. K. (2000). Selection of optimal sensor position in a tubular reactor using robust degree of observability criteria. Chemical Engineering Science, 55(4), 827–837. https://doi.org/10.1016/S0009-2509(99)00360-7
Wang, Z., & Cai, J. (2018). Probabilistic roadmap method for path-planning in radioactive environment of nuclear facilities. Progress in Nuclear Energy, 109, 113–120. https://doi.org/10.1016/j.pnucene.2018.08.006
Xu, W., He, D., Cai, Y., & Zhang, F. (2022). Robots’ state estimation and observability analysis based on statistical motion models. IEEE Transactions on Control Systems Technology, 30(5), 2030–2045. https://doi.org/10.1109/TCST.2021.3133080
Yousif, K., Bab-Hadiashar, A., & Hoseinnezhad, R. (2015). An overview to visual odometry and visual SLAM: Applications to mobile robotics. Intelligent Industrial Systems, 1(4), 289–311. https://doi.org/10.1007/s40903-015-0032-7
Zammit, C., & van Kampen, E. J. (2018). Comparison between A* and RRT algorithms for UAV path planning. In 2018 AIAA Guidance, Navigation, and Control Conference, 2018, 210039. Aerospace Research Central. https://doi.org/10.2514/6.2018-1846
Zhang, F., Li, S., Yuan, S., Sun, E., & Zhao, L. (2018). Algorithms analysis of mobile robot SLAM based on Kalman and particle filter. In 2017 9th International Conference On Modelling, Identification and Control (ICMIC) (pp. 1050–1055). IEEE. https://doi.org/10.1109/ICMIC.2017.8321612
Zhang, G., & Vela, P. A. (2015). Good features to track for visual SLAM. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 1373–1382). IEEE. https://doi.org/10.1109/CVPR.2015.7298743
Zhang, Y., Zhang, T., & Huang, S. (2018). Comparison of EKF based SLAM and optimization based SLAM algorithms. In 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA) (pp. 1308–1313). https://doi.org/10.1109/ICIEA.2018.8397911
Aggarwal, S., & Kumar, N. (2020). Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges. Computer Communications, 149, 270–299. https://doi.org/10.1016/j.comcom.2019.10.014
Aminzadeh, A., & Amiri Atashgah, M. A. (2018). Feature article: Implementation and performance evaluation of optical flow navigation system under specific conditions for a flying robot. IEEE Aerospace and Electronic Systems Magazine, 33(11), 20–28. https://doi.org/10.1109/MAES.2018.170075
Amiri Atashgah, M. A., & Malaek, S. M. B. (2013). Prediction of aerial-image motion blurs due to the flying vehicle dynamics and camera characteristics in a virtual environment. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 227(7), 1055–1067. https://doi.org/10.1177/0954410012450107
Bahraini, M. S., Bozorg, M., & Rad, A. B. (2018). A new adaptive UKF algorithm to improve the accuracy of SLAM. International Journal of Robotics, 5(1), 35–46.
Bakdi, A., Hentout, A., Boutami, H., Maoudj, A., Hachour, O., & Bouzouia, B. (2017). Optimal path planning and execution for mobile robots using genetic algorithm and adaptive fuzzy-logic control. Robotics and Autonomous Systems, 89, 95–109. https://doi.org/10.1016/j.robot.2016.12.008
Barrau, A., & Bonnabel, S. (2015). An EKF-SLAM algorithm with consistency properties. Cornell University. http://arxiv.org/abs/1510.06263
Batista, P., Silvestre, C., & Oliveira, P. (2011). Single range aided navigation and source localization: Observability and filter design. Systems & Control Letters, 60(8), 665–673. https://doi.org/10.1016/j.sysconle.2011.05.004
Bryson, M., & Sukkarieh, S. (2008). Observability analysis and active control for airborne SLAM. IEEE Transactions on Aerospace and Electronic Systems, 44(1), 261–280. https://doi.org/10.1109/TAES.2008.4517003
Butcher, E. A., Wang, J., & Lovell, T. A. (2017). On Kalman filtering and observability in nonlinear sequential relative orbit estimation. Journal of Guidance, Control, and Dynamics, 40(9), 2167–2182. https://doi.org/10.2514/1.G002702
Carlone, L., Du, J., Kaouk Ng, M., Bona, B., & Indri, M. (2014). Active SLAM and exploration with particle filters using Kullback-Leibler divergence. Journal of Intelligent and Robotic Systems: Theory and Applications, 75(2), 291–311. https://doi.org/10.1007/s10846-013-9981-9
Chakraborty, A., Misra, S., Sharma, R., & Taylor, C. N. (2017). Observability conditions for switching sensing topology for cooperative localization. Unmanned Systems, 5(3), 141–157. https://doi.org/10.1142/S2301385017400039
Chen, C. (1999). Linear system theory and design. Oxford University Press.
Chen, Y. B., Luo, G. C., Mei, Y. S., Yu, J. Q., & Su, X. L. (2014). UAV path planning using artificial potential field method updated by optimal control theory. International Journal of Systems Science, 47(6), 1407–1420. https://doi.org/10.1080/00207721.2014.929191
Clemens, J., Reineking, T., & Kluth, T. (2016). An evidential approach to SLAM, path planning, and active exploration. International Journal of Approximate Reasoning, 73, 1–26. https://doi.org/10.1016/j.ijar.2016.02.003
Fakoor, M., Kosari, A., & Jafarzadeh, M. (2016). Humanoid robot path planning with fuzzy Markov decision processes. Journal of Applied Research and Technology, 14(5), 300–310. https://doi.org/10.1016/j.jart.2016.06.006
Fethi, D., Nemra, A., Louadj, K., & Hamerlain, M. (2018). Simultaneous localization, mapping, and path planning for unmanned vehicle using optimal control. Advances in Mechanical Engineering, 10(1). https://doi.org/10.1177/1687814017736653
Fraundorfer, F., & Scaramuzza, D. (2012). Visual odometry: Matching, robustness, optimization, and applications. IEEE Robotics & Automation Magazine, 19(2). https://doi.org/10.1109/MRA.2012.2182810
Fu, B., Chen, L., Zhou, Y., Zheng, D., Wei, Z., Dai, J., & Pan, H. (2018). An improved A* algorithm for the industrial robot path planning with high success rate and short length. Robotics and Autonomous Systems, 106, 26–37. https://doi.org/10.1016/j.robot.2018.04.007
González, D., Pérez, J., Milanés, V., & Nashashibi, F. (2016). A review of motion planning techniques for automated vehicles. IEEE Transactions on Intelligent Transportation Systems, 17(4), 1135–1145. https://doi.org/10.1109/TITS.2015.2498841
Hahn, J., Edgar, T. F., Marquardt, W. (2003). Controllability and observability covariance matrices for the analysis and order reduction of stable nonlinear systems. Journal of Process Control, 13(2), 115–127. https://doi.org/10.1016/S0959-1524(02)00024-0
Ham, F. M., & Grover Brown, R. (1983). Observability, eigenvalues, and Kalman filtering. IEEE Transactions on Aerospace and Electronic Systems, AES-19(2), 269–273. https://doi.org/10.1109/TAES.1983.309446
Hasegawa, Y., & Fujimoto, Y. (2016). Experimental verification of path planning with SLAM. IEEJ Journal of Industry Applications, 5(3), 253–260. https://doi.org/10.1541/ieejjia.5.253
Hermann, R., Krener, A. (1977). Nonlinear controllability and observability. IEEE Transactions on Automatic Control, AC-22(5), 728–740. https://doi.org/10.1109/TAC.1977.1101601
Hesch, J. A., Kottas, D. G., Bowman, S. L., & Roumeliotis, S. I. (2013). Camera-IMU-based localization: Observability analysis and consistency improvement. The International Journal of Robotics Research, 33(1), 182–201. https://doi.org/10.1177/0278364913509675
Huang, G. P., Mourikis, A. I., Roumeliotis, S. I. (2008). Analysis and improvement of the consistency of extended Kalman filter based SLAM. In IEEE International Conference on Robotics and Automation (ICRA). IEEE Xplore. https://doi.org/10.1109/ROBOT.2008.4543252
Huang, G. P., Mourikis, A. I., & Roumeliotis, S. I. (2009). On the complexity and consistency of UKF-based SLAM. In 2009 IEEE International Conference on Robotics and Automation. IEEE. https://doi.org/10.1109/ROBOT.2009.5152793
Huang, L., Song, J., Zhang, Ch. (2017). Observability analysis and filter design for a vision inertial absolute navigation system for UAV using landmarks. Optik, 149, 455–468. https://doi.org/10.1016/j.ijleo.2017.09.060
Kalogeiton, V. S., Ioannidis, K., Sirakoulis, G. C., & Kosmatopoulos, E. B. (2019). Real-time active SLAM and obstacle avoidance for an autonomous robot based on stereo vision. Cybernetics and Systems, 50(3), 239–260. https://doi.org/10.1080/01969722.2018.1541599
Kurt-Yavuz, Z., & Yavuz, S. (2012). A comparison of EKF, UKF, FastSLAM2.0, and UKF-based FastSLAM algorithms. In 2012 IEEE 16th International Conference on Intelligent Engineering Systems (INES) (pp. 37–43). IEEE. https://doi.org/10.1109/INES.2012.6249866
Lall, S., Marsden, J. E., & Glavaški, S. (2002). A subspace approach to balanced truncation for model reduction of nonlinear control systems. International Journal of Robust and Nonlinear Control, 12(6), 519–535. https://doi.org/10.1002/rnc.657
Lee, K. W., Wijesoma, W. S., Ibanez Guzman, J. (2006). On the observability and observability analysis of SLAM. In 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE. https://doi.org/10.1109/IROS.2006.281646
Leonard, M. R., & Zoubir, A. M. (2019). Multi-Target tracking in distributed sensor networks using particle PHD filters. Signal Processing, 159, 130–146. https://doi.org/10.1016/j.sigpro.2019.01.020
Leung, C., Huang, S., & Dissanayake, G. (2006). Active SLAM using model predictive control and attractor based exploration. In IEEE International Conference on Intelligent Robots and Systems (pp. 5026–5031). IEEE. https://doi.org/10.1109/IROS.2006.282530
Lystianingrum, V., Hredzak, B., Agelidis, V. G., & Djanali, V. S. (2014). Observability degree criteria evaluation for temperature observability in a battery string towards optimal thermal sensors placement. In 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). IEEE. https://doi.org/10.1109/ISSNIP.2014.6827641
Mac, T. T., Copot, C., Tran, D. T., & De Keyser, R. (2016). Heuristic approaches in robot path planning: A survey. Robotics and Autonomous Systems, 86, 13–28. https://doi.org/10.1016/j.robot.2016.08.001
Maurovic, I., Seder, M., Lenac, K., & Petrovic, I. (2018). Path planning for active SLAM based on the D*algorithm with negative edge weights. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(8), 1321–1331. https://doi.org/10.1109/TSMC.2017.2668603
Myhre, N. (2018). Vision-aided navigation using tracked lankmarks [Doctoral Dissertations and Master’s Theses]. Embry-Riddle Aeronautical University. https://commons.erau.edu/edt/390
Nijmeijer, H., & van der Schaft, A. (1990). Nonlinear dynamical control systems. Springer. https://doi.org/10.1007/978-1-4757-2101-0
Niu, H., Savvaris, A., Tsourdos, A., & Ji, Z. (2019). Voronoi-visibility roadmap-based path planning algorithm for unmanned surface vehicles. The Journal of Navigation, 72(4), 850–874. https://doi.org/10.1017/S0373463318001005
Patle, B. K., Babu L, G., Pandey, A., Parhi, D. R. K., & Jagadeesh, A. (2019). A review: On path planning strategies for navigation of mobile robot. Defence Technology, 15(4), 582–606. https://doi.org/10.1016/j.dt.2019.04.011
Perez, A., Platt, R., Konidaris, G., Kaelbling, L., & Lozano-Perez, T. (2012). LQR-RRT*: Optimal sampling-based motion planning with automatically derived extension heuristics. In IEEE International Conference on Robotics and Automation (pp. 2537–2542). IEEE. https://doi.org/10.1109/ICRA.2012.6225177
Qu, C., Gai, W., Zhong, M., & Zhang, J. (2020). A novel reinforcement learning based grey wolf optimizer algorithm for unmanned aerial vehicles (UAVs) path planning. Applied Soft Computing, 89, 106099. https://doi.org/10.1016/j.asoc.2020.106099
Rasekhipour, Y., Khajepour, A., Chen, S. K., & Litkouhi, B. (2017). A potential field-based model predictive path-planning controller for autonomous road vehicles. IEEE Transactions on Intelligent Transportation Systems, 18(5), 1255–1267. https://doi.org/10.1109/TITS.2016.2604240
Serpas, M., Hackebeil, G., Laird, C., & Hahn, J. (2013). Sensor location for nonlinear dynamic systems via observability analysis and MAX-DET optimization. Computers and Chemical Engineering, 48, 105–112. https://doi.org/10.1016/j.compchemeng.2012.07.014
Sharma, R. (2014). Observability based control for cooperative localization. In 2014 International Conference on Unmanned Aircraft Systems (ICUAS) (pp. 134–139). IEEE. https://doi.org/10.1109/ICUAS.2014.6842248
Sharma, R., Beard, R. W., Taylor, C. N., & Quebe, S. (2012). Graph-based observability analysis of bearing-only cooperative localization. IEEE Transactions on Robotics, 28(2), 522–529. https://doi.org/10.1109/TRO.2011.2172699
Tao, T., Huang, Y., Sun, F., Wang, T. (2007). Motion planning for SLAM based on frontier exploration. In 2007 International Conference on Mechatronics and Automation. IEEE. https://doi.org/10.1109/ICMA.2007.4303879
Van Den Berg, F. W. J., Hoefsloot, H. C. J., Boelens, H. F. M., & Smilde, A. K. (2000). Selection of optimal sensor position in a tubular reactor using robust degree of observability criteria. Chemical Engineering Science, 55(4), 827–837. https://doi.org/10.1016/S0009-2509(99)00360-7
Wang, Z., & Cai, J. (2018). Probabilistic roadmap method for path-planning in radioactive environment of nuclear facilities. Progress in Nuclear Energy, 109, 113–120. https://doi.org/10.1016/j.pnucene.2018.08.006
Xu, W., He, D., Cai, Y., & Zhang, F. (2022). Robots’ state estimation and observability analysis based on statistical motion models. IEEE Transactions on Control Systems Technology, 30(5), 2030–2045. https://doi.org/10.1109/TCST.2021.3133080
Yousif, K., Bab-Hadiashar, A., & Hoseinnezhad, R. (2015). An overview to visual odometry and visual SLAM: Applications to mobile robotics. Intelligent Industrial Systems, 1(4), 289–311. https://doi.org/10.1007/s40903-015-0032-7
Zammit, C., & van Kampen, E. J. (2018). Comparison between A* and RRT algorithms for UAV path planning. In 2018 AIAA Guidance, Navigation, and Control Conference, 2018, 210039. Aerospace Research Central. https://doi.org/10.2514/6.2018-1846
Zhang, F., Li, S., Yuan, S., Sun, E., & Zhao, L. (2018). Algorithms analysis of mobile robot SLAM based on Kalman and particle filter. In 2017 9th International Conference On Modelling, Identification and Control (ICMIC) (pp. 1050–1055). IEEE. https://doi.org/10.1109/ICMIC.2017.8321612
Zhang, G., & Vela, P. A. (2015). Good features to track for visual SLAM. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 1373–1382). IEEE. https://doi.org/10.1109/CVPR.2015.7298743
Zhang, Y., Zhang, T., & Huang, S. (2018). Comparison of EKF based SLAM and optimization based SLAM algorithms. In 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA) (pp. 1308–1313). https://doi.org/10.1109/ICIEA.2018.8397911