Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-23T15:53:21.204Z Has data issue: false hasContentIssue false
  • English
  • Français

Hybrid Methodology for Path Planning and Computational Vision Applied to Autonomous Mission: A New Approach

Published online by Cambridge University Press:  25 July 2019

Fabrício O. Coelho ,
Milena F. Pinto ,
João Pedro C. Souza  and
André L. M. Marcato Open the ORCID record for André L. M. Marcato [Opens in a new window]
Fabrício O. Coelho*
Affiliation:
Electrical Engineering Department, Federal University of Juiz de Fora, Juiz de Fora, Brazil E-mails: [email protected], [email protected]
Milena F. Pinto
Affiliation:
Electrical Engineering Department, Federal University of Juiz de Fora, Juiz de Fora, Brazil E-mails: [email protected], [email protected]
João Pedro C. Souza
Affiliation:
Faculty of Engineering, Faculdade de Engenharia da Universidade do PortoPorto, Portugal. E-mail: [email protected]
André L. M. Marcato
Affiliation:
Electrical Engineering Department, Federal University of Juiz de Fora, Juiz de Fora, Brazil E-mails: [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

In recent years, mobile robots have become increasingly frequent in daily life applications, such as cleaning, surveillance, support for the elderly and people with disabilities, as well as hazardous activities. However, a big challenge arises when the robotic system must perform a fully autonomous mission. The main problems of autonomous missions include path planning, localisation, and mapping. Thus, this research proposes a hybrid methodology for mobile robots on an autonomous mission involving an offline approach that uses the Direct-DRRT* algorithm and the artificial potential fields algorithm as the online planner. The experimental design covers three scenarios with an increasing degree of accuracy in respect of the real world. Additionally, an extensive evaluation of the proposed methodology is reported.

Keywords

Autonomous missionsArtificial intelligenceLocalisationMobile robotsPath planning
Type
Articles
Information
Robotica , Volume 38 , Issue 6 , June 2020 , pp. 1000 - 1018
Copyright
© Cambridge University Press 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Lattanzi, D. and Miller, G., “Review of robotic infrastructure inspection systems,” J. Infrastruct. Syst., 04017004 (2017).CrossRefGoogle Scholar
Panchpor, A. A., Shue, S. and Conrad, J. M., “A Survey of Methods for Mobile Robot Localization and Mapping in Dynamic Indoor Environments,” 2018 Conference on Signal Processing And Communication Engineering Systems (SPACES), Vijayawada, India (2018) pp. 138–144.Google Scholar
Nosrati, M., Karimi, R. and Hasanvand, H. A., “Investigation of the*(star) search algorithms: Characteristics, methods and approaches,” World Appl. Program . 2(4), 251–256 (2012).Google Scholar
Tao, S. and Yang, Y., “Collision-free motion planning of a virtual arm based on the fabrik algorithm,” Robotica 35(6), 1431–1450 (2017).CrossRefGoogle Scholar
Khan, F., Alakberi, A., Almaamari, S. and Beig, A. R., “Navigation Algorithm for Autonomous Mobile Robots in Indoor Environments,” Advances in Science and Engineering Technology International Conferences (ASET), Dubai (2018) pp. 1–6.Google Scholar
Coelho, F. O., Carvalho, J. P., Pinto, M. F. and Marcato, A. L., “Direct-DRRT*: A RRT Improvement Proposal,” 2018 13th APCA International Conference on Control and Soft Computing (CONTROLO), Ponta Delgada, Azores (2018) pp. 154–158.Google Scholar
LaValle, S. M., “Rapidly-exploring random trees: A new tool for path planning,” http://msl.cs.illinois.edu/∼lavalle/papers/Lav98c.pdf (1998). Accessed 1st August 2017.Google Scholar
Tomasello, P., Sidhu, S., Shen, A., Moskewicz, M.W., Redmon, N., Joshi, G., Phadte, R., Jain, P. and Iandola, F., “Dscnet: Replicating lidar point clouds with deep sensor cloning,” arXiv preprint arXiv:1811.07070 (2018).CrossRefGoogle Scholar
Coelho, F. O., Carvalho, J. P., Pinto, M. F. and Marcato, A. L., “Ekf and Computer Vision for Mobile Robot Localization,” 2018 13th APCA International Conference on Control and Soft Computing (CONTROLO), Ponta Delgada, Azores (2018) pp. 148–153.Google Scholar
Shiller, Z., “Off-line and On-line Trajectory Planning,” In: Motion and Operation Planning of Robotic Systems (Carbone, G., Gomez-Bravo, F. (eds)) (Springer, Cham, 2015) pp. 29–62.CrossRefGoogle Scholar
Connell, D. and La, H. M., “Dynamic path planning and replanning for mobile robots using RRT,” arXiv preprint arXiv:1704.04585 (2017).CrossRefGoogle Scholar
Connell, D. and Manh La, H., “Extended rapidly exploring random tree–based dynamic path planning and replanning for mobile robots,” Int. J. Adv. Robot. Syst. 15(3), 1729881418773874 (2018).CrossRefGoogle Scholar
Du, Z. and Liu, S., “Asymptotical RRT-Based Path Planning for Mobile Robots in Dynamic Environments ” 2018 37th Chinese Control Conference (CCC), Wuhan, China (2018) pp. 5281–5286.Google Scholar
Breuer, T., Macedo, G. R. G., Hartanto, R., Hochgeschwender, N., Holz, D., Hegger, F., Jin, Z., Müller, C., Paulus, J., Reckhaus, M. and Ruiz, J. A. A., “Johnny: An autonomous service robot for domestic environments,” J. Intell. Robot. Syst. 66(1–2), 245–272 (2012).CrossRefGoogle Scholar
Wisspeintner, T., Nowak, W. and Bredenfeld, A., “Volksbot–a Flexible Component-Based Mobile Robot System,” In: Robot Soccer World Cup (Bredenfeld, A., Jacoff, A., Noda, I., Takahashi, Y. (eds)) (Springer, Berlin, Heidelberg, 2005) pp. 716–723.Google Scholar
Qureshi, A. H., Iqbal, K. F., Qamar, S. M., Islam, F., Ayaz, Y. and Muhammad, N., “Potential Guided Directional-RRT* for Accelerated Motion Planning in Cluttered Environments,” 2013 IEEE International Conference on Mechatronics and Automation, Karlsruhe, Germany (2013) pp. 519–524.Google Scholar
Khatib, O., “Real-time obstacle avoidance for manipulators and mobile robots,” Int. J. Robot. Res. 5(1), 90–98 (1986).CrossRefGoogle Scholar
Xu, X., Yang, Y. and Pan, S., “Motion Planning for Mobile Robots,” In: Advanced Path Planning for Mobile Entities (IntechOpen, 2018).CrossRefGoogle Scholar
Feirstein, D. S., Koryakovskiy, I., Kober, J. and Vallery, H., “Reinforcement learning of potential fields to achieve limit-cycle walking,” IFAC-PapersOnLine 49(14), 113–118 (2016).CrossRefGoogle Scholar
Faria, M. P., Mendonça, T., Olivi, L. and Marcato, A., “A modified approach of potential field method for control of trajectory tracking and obstacle avoidance,” IEEE/IAS International Conference on Industry Applications (INDUSCON), Juiz de Fora, Brazil (2014).Google Scholar
Cui, P.,Yan, W. and Guo, X., “Path Planning for Underwater Docking Based on Modified Artificial Potential Field,” International Conference on Advanced Robotics and Mechatronics (ICARM), Osaka, Japan (2016) pp. 376–381.Google Scholar
Leite, D., Figueiredo, K. and Vellasco, M., “Localização por kalman estendido aplicado a mapas baseados em marcos,” In: Simpósio Brasileiro de Automação Inteligente, vol. 12 (2015).Google Scholar
Marin-Plaza, P., Hussein, A., Martin, D. and Escalera, A. D. l., “Global and local path planning study in a ros-based research platform for autonomous vehicles,” J. Adv. Trans. 2018, 1–10 (2018).CrossRefGoogle Scholar
Dayoub, F., Morris, T., Upcroft, B. and Corke, P., “Vision-Only Autonomous Navigation Using Topometric Maps,” 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Tokyo, Japan (2013) pp. 1923–1929.Google Scholar
Fung, M. L., Chen, M. Z. and Chen, Y. H., “Sensor Fusion: A Review of Methods and Applications,” 2017 29th Chinese Control And Decision Conference (CCDC), Chongqing (2017) pp. 3853–3860.Google Scholar
La, H. M., Lim, R. S., Basily, B. B., Gucunski, N., Yi, J., Maher, A., Romero, F. A. and Parvardeh, H., “Mechatronic systems design for an autonomous robotic system for high-efficiency bridge deck inspection and evaluation,” IEEE/ASME Trans. Mech . 18(6), 1655–1664 (2013).CrossRefGoogle Scholar
La, H. M., Gucunski, N., Dana, K. and Kee, S.-H., “Development of an autonomous bridge deck inspection robotic system,” J. Field Robot. 34(8), 1489–1504 (2017).CrossRefGoogle Scholar
Sobreira, H., Moreira, A. P., Costa, P. and Lima, J., “Robust mobile robot localization based on a security laser: an industry case study,” Indus. Robot Int. J. 43(6), 596–606 (2016).CrossRefGoogle Scholar
Corke, P., Robotics, Vision and Control: Fundamental Algorithms in MATLAB, vol. 73 (Springer, Berlin, 2011).CrossRefGoogle Scholar
Okuyama, I. F., Maximo, M. R. O. A., Cavalcanti, A. L. O. and Afonso, R. J. M., “Nonlinear Grey-Box Identification of a Differential Drive Mobile Robot. XIII Simpósio Brasileiro de Automação Inteligente (SBAI),” Brazil (2017).Google Scholar
Thrun, S., Burgard, W. and Fox, D., Probabilistic robotics (intelligent robotics and autonomous agents) (MIT Press, Cambridge, MA, USA, 2005).Google Scholar
Carvalho, J. P., Jucá, M., Menezes, A., Marcato, A., Bessa, A. D. S. and Olivi, L., “Landing a UAV in a dynamical target using fuzzy control and computer vision,” CBA2016 (2016) pp. 2636–2641.Google Scholar
Cashbaugh, J. and Kitts, C., “Automatic calculation of a transformation matrix between two frames,” IEEE Access (2018).CrossRefGoogle Scholar
Rudy, N., Robot Localization and Kalman Filters on Finnding Your Position in a Noisy World Ph.D. Dissertation (Utrecht University, 2003).Google Scholar
Pinto, M. F., Mendonça, T. R., Olivi, L. R., Costa, E. B. and Marcato, A. L., “Modified Approach Using Variable Charges to Solve Inherent Limitations of Potential Fields Method,” 2014 11th IEEE/IAS International Conference on Industry Applications (INDUSCON), Juiz de Fora, Brazil (2014), pp. 1–6.Google Scholar
Woods, A. C. and La, H. M., “A novel potential field controller for use on aerial robots,” IEEE Transactions on Systems, Man, and Cybernetics: Systems 49(4), 665–676 (2017).CrossRefGoogle Scholar
Sharma, S., Sutton, R., Hatton, D. and Singh, Y., “Path planning of an autonomous surface vehicle based on artificial potential fields in a real time marine environment,” COMPIT’17: 16th International Conference on Computer and IT Applications in the Maritime Industries, Cardiff, UK (2017).Google Scholar
Lima, P.U., Ahmad, A., Dias, A.,Conceição, A.G., Moreira, A. P., Silva, E., Almeida, L., Oliveira, L. and Nascimento, T. P., “Formation control driven by cooperative object tracking,” Robot. Auto. Sys. 63(1), 68–79 (2015).CrossRefGoogle Scholar
Robots, A. M., “Pionner 3-dx datasheet,” http://www.mobilerobots.com/Libraries/Downloads/Pioneer3DXP3DX-RevA.sflb.ashx (2011). Accessed in 09 August 2017.Google Scholar