TY - GEN
T1 - Collision Avoidance Simulation Using Voronoi Diagrams in a Centralized System of Holonomic Multi-agents
AU - Cuenca Macas, Leduin José
AU - Pineda, Israel
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - This work solves the Collision Avoidance problem in a simulation of a centralized system of holonomic multi-agents in a two dimensional space free of static obstacles. For this, we propose an implementation of three modules in an architecture: Threat Assessment Strategy (TAS), Path Planning Strategy (PPS), and Path Tracking Strategy (PTS). The Buffered Voronoi Cells represent the TAS. The PPS modules use two algorithms: the Analytical Geometric Algorithm (AGA) and the Receding Horizons Control (RHC) based on Quadratic Programming (QP) Algorithm. Finally, PTS controls the tracking according to fixed distance magnitudes in each iteration. The analysis of the results considers the computational execution time, the number of steps until convergence, and the calculation of optimal values. Also, these results are compared with the Optimal Reciprocal Collision Avoidance (ORCA) algorithm. In this way, our proposal successfully addresses and solves the collision avoidance problem but takes more execution time and number of steps compared with the ORCA algorithm. Besides, the number of steps of AGA is closer to ORCA, producing promising results with an accuracy of 95%.
AB - This work solves the Collision Avoidance problem in a simulation of a centralized system of holonomic multi-agents in a two dimensional space free of static obstacles. For this, we propose an implementation of three modules in an architecture: Threat Assessment Strategy (TAS), Path Planning Strategy (PPS), and Path Tracking Strategy (PTS). The Buffered Voronoi Cells represent the TAS. The PPS modules use two algorithms: the Analytical Geometric Algorithm (AGA) and the Receding Horizons Control (RHC) based on Quadratic Programming (QP) Algorithm. Finally, PTS controls the tracking according to fixed distance magnitudes in each iteration. The analysis of the results considers the computational execution time, the number of steps until convergence, and the calculation of optimal values. Also, these results are compared with the Optimal Reciprocal Collision Avoidance (ORCA) algorithm. In this way, our proposal successfully addresses and solves the collision avoidance problem but takes more execution time and number of steps compared with the ORCA algorithm. Besides, the number of steps of AGA is closer to ORCA, producing promising results with an accuracy of 95%.
KW - Collision avoidance
KW - Convex optimization
KW - Path planning
KW - Quadratic programming
KW - Simulation
KW - Voronoi diagrams
UR - http://www.scopus.com/inward/record.url?scp=85140758765&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-18272-3_2
DO - 10.1007/978-3-031-18272-3_2
M3 - Contribución a la conferencia
AN - SCOPUS:85140758765
SN - 9783031182716
T3 - Communications in Computer and Information Science
SP - 18
EP - 31
BT - Information and Communication Technologies - 10th Ecuadorian Conference, TICEC 2022, Proceedings
A2 - Herrera-Tapia, Jorge
A2 - Rodriguez-Morales, Germania
A2 - Fonseca C., Efraín R.
A2 - Berrezueta-Guzman, Santiago
PB - Springer Science and Business Media Deutschland GmbH
T2 - 10th Ecuadorian Congress of Information and Communication Technologies, TICEC 2022
Y2 - 12 October 2022 through 14 October 2022
ER -