Robotic Coverage for Continuous Mapping Ahead of a Moving Vehicle

Abstract

In this paper we investigate the problem of using a UAV to provide current map information of the environment in front of a moving ground vehicle. We propose a simple coverage plan called a conformal lawn mower plan that enables a UAV to scan the route ahead of the ground vehicle. The plan requires only limited knowledge of the ground vehicle's future path. For a class of curvature-constrained ground vehicle paths, we show that the proposed plan requires a UAV velocity that is no more than twice the velocity required to cover the optimal plan. We also establish necessary and sufficient UAV velocities, relative to the ground vehicle velocity, required to successfully cover any path in the curvature restricted set. In simulation, we validate the proposed plan, showing that the required velocity to provide coverage is strongly related to the curvature of the ground vehicle's path. Our results also illustrate the relationship between mapping requirements and the relative velocities of the UAV and ground vehicle.

Hierarchical Planning for Heterogeneous Multi-Robot Routing Problems via Learned Subteam Performance

“…Let η, θ be the auxiliary variables, and let c be a constant such that 0 < c ≤ 1. Taking constraint (7) as an example:…”

“…The last decade witnessed a growing interest in this field from the robotics community as well, under flavors that more closely match the application scenarios of multi-robot systems. Examples can be found in applications like search [5], monitoring [6], coverage [7], agriculture [8], package delivery [9], [10], and, more generally, the execution of spatially distributed "tasks" [11], [12]. Oberlin et al [11] and Prasad et al [12] study routing problems that are similar to the basic routing task considered in our testbed problem, but with modeling assumptions that might not be applicable to many robotic scenarios.…”

An Analytic Model for Negative Obstacle Detection with Lidar and Numerical Validation Using Physics-Based Simulation

“…More recently, Nakano et al [ 9 ], Ravi et al [ 17 ] and Péntek et al [ 18 ] have performed fundamental analysis of lidar performance for various detection tasks, while Roberts et al [ 19 ], Azevedo et al [ 20 ] and Gilhuly and Smith [ 21 ] investigated the capability of UAV-mounted lidar in classifying ground points, detecting power lines, and terrain mapping, respectively. Additionally, Kandath et al [ 22 ] have shown the viability of incorporating sensor information from a low-flying UAV into UGV path planning algorithms.…”

A bio-inspired localization-free stochastic coverage algorithm with verified reachability

“…Using the mapping mentioned for the 3D case, a given nth trajectory can be represented by a vector s n as defined in (27). The probability transition matrix can similarly be estimated for the 3D PTW model by counting the number of state transitions in the simulated trajectories by using (28) where N s = 26.…”

“…These algorithms can be classified on the basis of whether the agent has prior knowledge about the global obstacle map of the search-space or not [9,22]. Coverage algorithms in known environments can be designed to perform in an efficient and predictable manner since the optimal movement pattern of the agent can be devised beforehand, a common example being the lawn mower strategy [26,27] and the spiraling strategy [28]. These algorithms usually rely on the availability of an external localization service (such as a global positioning system (GPS)) which enables the agent to keep track of the area covered and yet to be covered.…”