Home // International Journal On Advances in Systems and Measurements, volume 13, numbers 1 and 2, 2020 // View article
Authors:
Oren Gal
Yerach Doytsher
Keywords: Visibility; 3D; Spatial analysis; Motion Planning.
Abstract:
In this paper, we present a conceptual Spatial Trajectory Planning (STP) method using Rapid Random Trees (RRT) planner, generating visibility motion primitives in urban environments using Inverse Reinforcement Learning (IRL) approach. Visibility motion primitives are set by using Spatial Visibility Clustering (SVC) analysis. Based on the STP planning method, we introduce IRL formulation and analysis which learns the value function of the planner from demonstrated trajectories and generating spatial visibility trajectory planning. Additionally, we study the visible trajectories planning for patrolling application using heterogeneous multi agents in 3D urban environments. Our concept is based on spatial clustering method using visibility analysis of the 3D visibility problem from a viewpoints in 3D urban environments, defined as locations. We consider two kinds of agents, with different kinematic and perception capabilities. Using simplified version of Traveling Salesman Problem (TSP), we formulate the problem as patrolling strategy one, with upper bound optimal performances. We present a combination of relative deadline UniPartition approaches based on visibility clusters. These key features allow new planning optimal patrolling strategy for heterogeneous agents in urban environment. We demonstrate our patrolling strategy method in simulations using Autonomous Navigation and Virtual Environment Laboratory (ANVEL) test bed environment.
Pages: 107 to 118
Copyright: Copyright (c) to authors, 2020. Used with permission.
Publication date: June 30, 2020
Published in: journal
ISSN: 1942-261x