5/29/2023 0 Comments Freespace blueThe objective is to provide good localization to the rover by selecting an optimal path that minimizes the localization uncertainty accumulation during the rover's traverse. In this paper, we consider a three-agent system composed of a Mars rover, copter, and orbiter. The copter's highresolution data helps the rover to identify small hazards such as steps and pointy rocks, as well as providing rich textual information useful to predict perception performance. In addition to conventional ground rovers, the Mars 2020 mission will send a helicopter to Mars. We conducted numerical simulations using the map of Mars 2020 landing site to demonstrate the effectiveness of the proposed planner. We jointly address where to map by the copter and where to drive by the rover using the proposed iterative copter-rover path planner. ![]() To achieve this goal, we quantify the localizability as a goodness measure associated with the map, and conduct a joint-space search over rover's path and copter's perceptual actions given prior information from the orbiter. The copter's high-resolution data helps the rover to identify small hazards such as steps and pointy rocks, as well as providing rich textual information useful to predict perception performance. Furthermore, (iii) in order to verify safety and performance requirements of a given POMDP, we formulate a barrier certificate theorem. Then, (ii) in order to estimate the reachable belief space of a POMDP, i.e., the set of all possible evolutions given an initial belief distribution over the states and a set of actions and observations, we find over-approximations in terms of sub-level sets of Lyapunov-like functions. Our contributions are fourfold: (i) We begin by casting the problem of analyzing a POMDP into analyzing the behavior of a discrete-time switched system. ![]() To overcome the complexity challenge of POMDPs, we apply techniques from control theory. ![]() Since the states are not directly observable ina POMDP, decision making has to be performed based on the output of a Bayesian filter (continuous beliefs) hence, making POMDPs intractable to solve and analyze. Partially observable Markov decision processes(POMDPs) provide a modeling framework for a variety of sequential decision making under uncertainty scenarios in artificial intelligence (AI).
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