A Safe-Distance Based Threat Assessment with Geometrical Based Steering Control for Vehicle Collision Avoidance

This work proposes a vehicle collision avoidance strategy based on the usage of Geometrical Based Steering Controller. The algorithm is composed of these features : 1) Collision Detection strategy using safe distance threshold, 2) predicts the future trajectory of the vehicle in the occurrence of obstacle, 3) decision making prior to avoiding collision, 4) avoiding obstacles while ensuring the vehicle to return to its original path. The strategy used a nonlinear vehicle model with steering and braking input as the actuators that will react and avoid collisions. Simulation results depict the ability of the methods to avoid the potential collision while returning to its original path. The inclusion of the Threat Assessment Strategy ensures the hindrance of the vehicle from colliding with the obstacle's edg

Asymptoticcaly-Optimal Path Planning Using the Improved Probabilistic Road Map Algorithm

A path planning algorithm is asymptotically optimal if it can guarantee that it will produce an optimal solution if given sufficient time. Path-planning algorithms that can provide optimal solutions are essential in many robotic applications. The objective of this study is to propose a new asymptotic optimal path planning algorithm. The method is to improve the probabilistic road map (PRM) algorithm through three strategies. The first strategy is to use an information-based sampling technique. The second strategy is that the search area starts from a small ellipsoid subset first. The third strategy is to improve the path using the wrapping process. We call this proposed algorithm the wrapping-based informed PRM (WIPRM) algorithm. Furthermore, the performance of the WIPRM algorithm was compared with the PRM algorithm, informed rapidly-exploring random tree* (RRT*), and informed PRM. The test results show that the WIPRM algorithm can build optimal paths for all test scenarios. The computational time needed by the WIPRM algorithm to build the optimal path is better than the informed RRT* and informed PRM algorithms. These results indicate that the WIPRM algorithm could be used in various robotic systems requiring optimal path planning algorithms, such as autonomous cars, unmanned aerial vehicles (UAV), and autonomous undersea vehicles (AUV).Suatu algoritma perencanaan jalur bersifat asimptotik optimal jika dapat menjamin bahwa akan menghasilkan solusi optimal jika diberikan waktu yang memadai. Algoritma perencanaan jalur yang dapat memberikan solusi optimal sangat penting dalam banyak aplikasi robotik. Penelitian ini bertujuan untuk mengusulkan algoritma perencanaan jalur baru yang bersifat asimptotik optimal. Metode yang digunakan adalah dengan meningkatkan algoritma probabilistic road map (PRM) melalui tiga strategi. Strategi pertama adalah menggunakan teknik pembangkitan sampel berbasis informasi. Strategi kedua adalah area pencarian dimulai dari subset ellipsoid yang berukuran kecil dahulu. Strategi ketiga adalah melakukan perbaikan jalur menggunakan proses wrapping. Algoritma yang diusulkan ini dinamakan algoritma wrapping-based informed PRM (WIPRM). Selanjutnya, performansi algoritma WIPRM dibandingkan dengan algoritma PRM, informed rapidly-exploring random tree* (RRT*), dan informed PRM. Hasil pengujian menunjukkan bahwa algoritma WIPRM dapat membangun jalur yang optimal untuk semua scenario pengujian. Waktu komputasi yang dibutuhkan algoritma WIPRM untuk membangun jalur optimal lebih baik daripada algoritma informed RRT* dan informed PRM. Dengan demikian, algoritma WIPRM berpotensi untuk diimplementasikan dalam berbagai sistem robotik yang membutuhkan algoritma perencanaan jalur yang optimal seperti mobil otonom, unmanned aerial vehicles (UAV), maupun autonomous underwater vehicles (AUV)