Real Time Underwater Obstacle Avoidance and Path Re-planning Using Simulated Multi-beam Forward Looking Sonar Images for Autonomous Surface Vehicle

This paper describes underwater obstacle avoidance and path re-planning techniques for autonomous surface vehicle (ASV) based on simulated multi-beam forward looking sonar images. The sonar image is first simulated and then a circular obstacle is defined and created in the field of view of the sonar. In this study, the robust real-time path re-planning algorithm based on an A* algorithm is developed. Our real-time path re-planning algorithm has been tested to regenerate the optimal path for several updated frames with a proper update frequency between the start point and the goal point both in static and dynamical environments. The performance of proposed method is verified through simulations, and tank experiments using an actual ASV. While the simulation results are successful, the vehicle model can avoid both single obstacle, multiple obstacles and moving obstacle with the optimal trajectory. For tank experiments, the proposed method for underwater obstacle avoidance system is implemented with the ASV test platform. The vehicle is controlled in real-time and moderately succeeds in its avoidance against the obstacle simulated in the field of view of the sonar together with the proposed position stochastic estimation of the vehicle

Desain dan analisa sistem gerak autonomous underwater vehicle

Autonomous Underwater Vehicle (AUV) merupakan salah satu jenis robot bawah air yang relatif flexibel untuk eksplorasi bawah laut dan peralatan sistem pertahanan bawah laut. AUV dikendalikan dan dikemudikan oleh komputer di atas kapal pendukung untuk melaju dan bergerak dengan enam derajat kebebasan (6-DOF). Untuk mengendalikan dan mengemudikan AUV dibutuhkan sistem navigasi, panduan dan kendali gerak. Pada penelitian ini dikembangkan sistem navigasi, panduan dan kendali gerak menggunakan model linier Segorogeni AUV. Pemodelan diawali dengan penyusunan formulasi matematika gerakan AUV, berupa model non-linier 6-DOF yang selanjutnya dilakukan linierisasi model non-linier 6-DOF dengan matriks Jacobi. Model linier ini digunakan sebagai platform untuk mengembangkan sistem navigasi, panduan dan kendali gerak. Untuk menjaga akurasi posisi secara terus menerus diterapkan estimasi trajektori pada navigasi dan panduan AUV dengan metode estimasi, dengan metode estimasi Ensemble Kalman Filter (EnKF) dan Ensemble Kalman Filter Square Root (EnKF-SR). Untuk menjaga kestabilan AUV diterapkan sistem kendali gerak dengan metode Proportional Integral Derivative (PID), Sliding Mode Control (SMC) dan Sliding PID (SPID). Pada penelitian ini dibuat sepuluh kasus lintasan yang harus dilalui AUV. Lintasan pertama berupa gerakan maju lurus tanpa belok dan menyelam (diving), lintasan kedua adalah gerakan belok tanpa diving, lintasan ketiga yaitu gerakan diving tanpa belok, lintasan keempat merupakan gerakan belok dengan diving, lintasan kelima adalah gerakan memutar tanpa diving, lintasan keenam berupa gerakan memutar dengan diving, lintasan ketujuh yakni gerakan memutar ellips tanpa diving, lintasan kedelapan yaitu gerakan memutar ellips dengan diving, lintasan kesembilan adalah gerakan diving dan gerakan naik (emerging) tanpa belok, sedangkan lintasan kesepuluh merupakan gerakan diving dan emerging dengan belok. Berdasarkan metode yang dikembangkan, hasil simulasi menunjukkan bahwa metode navigasi dan panduan EnKF, EnKF-SR dan KF untuk model linier memiliki tingkat akurasi yang tinggi dalam memodelkan kesepuluh lintasan, dan tidak melebihi maksimal error posisi dari desain kriteria, yaitu 3.0%. Rata-rata error posisi untuk kesepuluh lintasan berkisar 0.1%- 0.99%. Hasil simulasi menunjukkan tingkat akurasi yang tinggi, sehingga dapat disimpulkan bahwa implementasi dari metode-metode tersebut pada platform Segorogeni AUV telah dilakukan secara tepat. Berdasarkan pembangkitan ensemble, hasil simulasi sistem navigasi dan panduan dengan metode EnKF-SR memiliki tingkat akurasi yang lebih baik daripada EnKF dan KF untuk kesepuluh lintasan. Simulasi menggunakan metode EnKF-SR dengan membangkitkan 300 ensemble memiliki rata-rata akurasi yang lebih baik daripada dengan membangkitkan 100, 200 atau 400 ensemble. Sedangkan berdasarkan lintasan kasus yang dibuat, hasil simulasi mendapatkan hasil yang baik dengan metode EnKF-SR. Hasil simulasi sistem kendali dengan metode SMC dan SPID menunjukkan adanya error yang tidak jauh berbeda, yakni kurang dari 5%, serta memiliki settling time yang cukup cepat, yaitu sekitar 1.0 detik. Sedangkan metode PID mempunyai error yang lebih besar dari 5% dan settling time yang sangat lama, sekitar 60 – 80 detik. Hal ini berarti bahwa hasil kendali gerak dengan metode SMC dan SPID jauh lebih stabil daripada PID. Selanjutnya, analisa kestabilan yang dilakukan dengan metode Lyapunov terhadap keseluruhan metode kendali menunjukkan kondisi stabil asimtotik global. ====================================================================================================== Autonomous Underwater Vehicle (AUV) is one type of underwater robot which is relatively flexible for undersea exploration and underwater defense systems. AUV is controlled and driven by computer on the support vessel to drive and move with six degrees of freedom (6-DOF). To control and navigate AUV, systems of guidance and motion control are required. In this research, the systems of navigation, guidance and motion control were developed using the linear model of Segorogeni AUV. The modeling began with mathematical formulation of AUV movement, in the form of non-linear models of 6-DOF, then the non-linear 6-DOF was linearized with Jacobi matrix. This linear model was used as a platform for developing navigation, guidance and motion control systems. To maintain the continuous accuracy of the position, estimation of trajectory was applied to the AUV navigation and guidance with the estimation method, Ensemble Kalman Filter (EnKF) and Ensemble Kalman Filter Square Root (EnKF-SR). To maintain the stability of AUV, the motion control systems by the methods of Proportional Integral Derivative (PID), Sliding Mode Control (SMC) and Sliding PID (SPID) were applied. In this research, ten trajectory cases to be passed by AUV were prepared.\ud The first trajectory was forward straight movement without turning and diving. The second trajectory was the movement of turning without diving. The third trajectory was diving movement without turning. The fourth trajectory was turning movement without diving. The fifth trajectory was twisting movement without diving. The sixth trajectory was twisting movement with diving. The seventh trajectory was ellipse like twisting without diving. The eighth trajectory was ellipse-like twisting movement with diving. The ninth was diving and remerging movement without turning whereas the tenth trajectory was diving and emerging movement with turning. Based on the methods developed, the simulation results showed that the methods of navigation and guidance of EnKF, EnKF-SR and KF for the linear models had a high degree of accuracy in modeling the tent trajectories and did not exceed the position maximum error against the design criteria, which is 3.0%. The average error for the ten trajectories ranged from 0.1% - 0.99%. The simulation results showed a high degree of accuracy, so it could be concluded that the implementation of these methods on the platform of Segorogeni AUV was appropriately carried out. Based ensemble generation, the simulation results showed that the navigation and guidance systems by EnKF-SR method demonstrated a higher accuracy than those by EnKF and KF for those ten trajectories. The simulations using EnKFSR to generate 300 ensembles had an average accuracy, better than those by generating a 100, 200 or 400 ensembles. Whereas, based on the trajectory cases made, the simulation results showed that the good results were obtained by using EnKF-SR method. The simulation results of the control system by applying the SMC and SPID methods demonstrated more or less similar error, for instance less than 5%, and a fairly fast settling time, which is about 1.0 seconds. While the application of PID method resulted in higher error, greater than 5% and the very long settling time, about 60-80 seconds. This means that the results of motion control by SMC and SPID methods were much more stable than those by PID. Furthermore, the stability analysis conducted by Lyapunov method to the overall control methods showed the global asymptotically stable condition. Keywords: AUV, the linear model, Ensemble Kalman Filter (EnKF), Ensemble Kalman Filter Square Root (EnKF), Kalman Filter (KF), Proportional Integral Derivative (PID), Sliding Mode Control (SMC), Sliding PID (SPID), Lyapunov

Robotics 2010

Without a doubt, robotics has made an incredible progress over the last decades. The vision of developing, designing and creating technical systems that help humans to achieve hard and complex tasks, has intelligently led to an incredible variety of solutions. There are barely technical fields that could exhibit more interdisciplinary interconnections like robotics. This fact is generated by highly complex challenges imposed by robotic systems, especially the requirement on intelligent and autonomous operation. This book tries to give an insight into the evolutionary process that takes place in robotics. It provides articles covering a wide range of this exciting area. The progress of technical challenges and concepts may illuminate the relationship between developments that seem to be completely different at first sight. The robotics remains an exciting scientific and engineering field. The community looks optimistically ahead and also looks forward for the future challenges and new development