Informed sampling space driven robot informative path planning

Path planning is an important primitive in robotics. In this paper, a new Informed Sampling Space (ISS) driven Informative Path Planning (IPP) approach is developed to facilitate autonomous robots to navigate and explore unknown and hazardous environments for in -situ resource utilization efficiently. The developed ISS-driven IPP approach is targeted on multi -objective optimization enabling the robot to plan its path from start to target locations in the environment and simultaneously explore multiple high -interest areas efficiently. The high -interest areas could be locations advised by a human supervisor or from the robot's prior knowledge of the environment. Typically, a cost function (time, distance, etc.) is used in sampling -based path planners. A new cost function is also developed to incorporate the high -interest spots in this paper, which is based on Multivariate normal (MVN) probability density function (PDF) and a normalization function. Two different IPP models are developed using the new cost function to assist robot navigation. IPP with RRT* is used in the first model with no heuristics, while IPP with RRT* and heuristic ISS is used in the second model. Simulation and comparative analysis substantiate the efficacy and robustness of our approach. The simulation results corroborate that our proposed ISS-driven IPP with RRT* converges rapidly towards the near -optimal solution with respect to both navigation time and environment exploration.