194 research outputs found
Clustered coverage orienteering problem of unmanned surface vehicles for water sampling
202105 bchyNot applicableOthersNSFC projectsPublished12 month
Task Planning on Stochastic Aisle Graphs for Precision Agriculture
This work addresses task planning under uncertainty for precision agriculture
applications whereby task costs are uncertain and the gain of completing a task
is proportional to resource consumption (such as water consumption in precision
irrigation). The goal is to complete all tasks while prioritizing those that
are more urgent, and subject to diverse budget thresholds and stochastic costs
for tasks. To describe agriculture-related environments that incorporate
stochastic costs to complete tasks, a new Stochastic-Vertex-Cost Aisle Graph
(SAG) is introduced. Then, a task allocation algorithm, termed Next-Best-Action
Planning (NBA-P), is proposed. NBA-P utilizes the underlying structure enabled
by SAG, and tackles the task planning problem by simultaneously determining the
optimal tasks to perform and an optimal time to exit (i.e. return to a base
station), at run-time. The proposed approach is tested with both simulated data
and real-world experimental datasets collected in a commercial vineyard, in
both single- and multi-robot scenarios. In all cases, NBA-P outperforms other
evaluated methods in terms of return per visited vertex, wasted resources
resulting from aborted tasks (i.e. when a budget threshold is exceeded), and
total visited vertices.Comment: To appear in Robotics and Automation Letter
Planning Algorithms for Multi-Robot Active Perception
A fundamental task of robotic systems is to use on-board sensors and perception algorithms to understand high-level semantic properties of an environment. These semantic properties may include a map of the environment, the presence of objects, or the parameters of a dynamic field. Observations are highly viewpoint dependent and, thus, the performance of perception algorithms can be improved by planning the motion of the robots to obtain high-value observations. This motivates the problem of active perception, where the goal is to plan the motion of robots to improve perception performance. This fundamental problem is central to many robotics applications, including environmental monitoring, planetary exploration, and precision agriculture. The core contribution of this thesis is a suite of planning algorithms for multi-robot active perception. These algorithms are designed to improve system-level performance on many fronts: online and anytime planning, addressing uncertainty, optimising over a long time horizon, decentralised coordination, robustness to unreliable communication, predicting plans of other agents, and exploiting characteristics of perception models. We first propose the decentralised Monte Carlo tree search algorithm as a generally-applicable, decentralised algorithm for multi-robot planning. We then present a self-organising map algorithm designed to find paths that maximally observe points of interest. Finally, we consider the problem of mission monitoring, where a team of robots monitor the progress of a robotic mission. A spatiotemporal optimal stopping algorithm is proposed and a generalisation for decentralised monitoring. Experimental results are presented for a range of scenarios, such as marine operations and object recognition. Our analytical and empirical results demonstrate theoretically-interesting and practically-relevant properties that support the use of the approaches in practice
Informative Path Planning in Random Fields via Mixed Integer Programming
We present a new mixed integer formulation for the discrete informative path
planning problem in random fields. The objective is to compute a budget
constrained path while collecting measurements whose linear estimate results in
minimum error over a finite set of prediction locations. The problem is known
to be NP-hard. However, we strive to compute optimal solutions by leveraging
advances in mixed integer optimization. Our approach is based on expanding the
search space so we optimize not only over the collected measurement subset, but
also over the class of all linear estimators. This allows us to formulate a
mixed integer quadratic program that is convex in the continuous variables. The
formulations are general and are not restricted to any covariance structure of
the field. In simulations, we demonstrate the effectiveness of our approach
over previous branch and bound algorithms
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