Path planning for information acquisition and evasion using marsupial vehicles

Abstract

This paper considers a path planning problem with two marsupial vehicles (one carrier vehicle and one passenger vehicle that is deployed by the carrier vehicle) exploring a planar area. This work is motivated by multiagent intelligence, surveillance, and reconnaissance missions in contested environments. The vehicles are heterogeneous, e.g., the carrier vehicle is faster than the passenger vehicle or the passenger vehicle possesses better sensors than the carrier vehicle. The vehicles are to gather a finite amount of information about an object of interest using their sensors while minimizing the likelihood of their detection by an opponent. Necessary conditions for optimal solutions are given and straight line travel for both vehicles is shown to be optimal. The results are illustrated in several simulation examples.

Marsupial Walking-and-Flying Robotic Deployment for Collaborative Exploration of Unknown Environments

“…The work in [16] considers the problem of deploying two flying robots from an unmanned ground vehicle and solves an optimization problem to minimize the time for reaching multiple target points. A different objective is proposed in [17], where a marsupial system consisting of two aircrafts is tasked to gather information about an object of interest, while minimizing the likelihood of their detection by their opponent. In the context of autonomous exploration, the authors in [18] propose a Monte Carlo tree search method using the solution to the sequential stochastic assignment problem as a roll out action-selection policy to address the problem of planning deployment times and locations of the carrier robots.…”

Marsupial Walking-and-Flying Robotic Deployment for Collaborative Exploration of Unknown Environments

“…The work in [14] considers the problem of deploying two flying robots from an unmanned ground vehicle and solves an optimization problem to minimize the time for reaching multiple target points. A different objective is proposed in [15], where a marsupial system consisting of two aircrafts is tasked to gather information about an object of interest, while minimizing the likelihood of their detection by their opponent. In the context of autonomous exploration, the authors in [16] propose a Monte Carlo tree search method using the solution to the sequential stochastic assignment problem as a roll out action-selection policy to address the problem of planning deployment times and locations of the carrier robots.…”

Minimal time delivery of multiple robots

“…Note that the dimension of the space in which the solutions are computed is independent of the number of AUVs. The gradient ∇ x V i in (7) can be approximated at the grid points via a finite difference approximation and interpolated to points of D not in the grid. The ordinary differential equation (9) will then be numerically solved using any integration method.…”

“…Other aspects are better understood. For example, one generic motion pattern for multi-domain vehicles concerns iterated rendezvous operations, in which vehicles exchange commands and data to decide where and when the next rendezvous takes place [7]. This motion pattern encompasses a significant number of complex motion-planning problems, including, for example, re-fueling, marsupial transportation [5] and cooperative pick-and-place [2].…”