180 research outputs found
An evolutionary algorithm for online, resource constrained, multi-vehicle sensing mission planning
Mobile robotic platforms are an indispensable tool for various scientific and
industrial applications. Robots are used to undertake missions whose execution
is constrained by various factors, such as the allocated time or their
remaining energy. Existing solutions for resource constrained multi-robot
sensing mission planning provide optimal plans at a prohibitive computational
complexity for online application [1],[2],[3]. A heuristic approach exists for
an online, resource constrained sensing mission planning for a single vehicle
[4]. This work proposes a Genetic Algorithm (GA) based heuristic for the
Correlated Team Orienteering Problem (CTOP) that is used for planning sensing
and monitoring missions for robotic teams that operate under resource
constraints. The heuristic is compared against optimal Mixed Integer Quadratic
Programming (MIQP) solutions. Results show that the quality of the heuristic
solution is at the worst case equal to the 5% optimal solution. The heuristic
solution proves to be at least 300 times more time efficient in the worst
tested case. The GA heuristic execution required in the worst case less than a
second making it suitable for online execution.Comment: 8 pages, 5 figures, accepted for publication in Robotics and
Automation Letters (RA-L
Clustered coverage orienteering problem of unmanned surface vehicles for water sampling
202105 bchyNot applicableOthersNSFC projectsPublished12 month
Multi-vehicle Dynamic Water Surface Monitoring
Repeated exploration of a water surface to detect objects of interest and
their subsequent monitoring is important in search-and-rescue or ocean clean-up
operations. Since the location of any detected object is dynamic, we propose to
address the combined surface exploration and monitoring of the detected objects
by modeling spatio-temporal reward states and coordinating a team of vehicles
to collect the rewards. The model characterizes the dynamics of the water
surface and enables the planner to predict future system states. The state
reward value relevant to the particular water surface cell increases over time
and is nullified by being in a sensor range of a vehicle. Thus, the proposed
multi-vehicle planning approach is to minimize the collective value of the
dynamic model reward states. The purpose is to address vehicles' motion
constraints by using model predictive control on receding horizon, thus fully
exploiting the utilized vehicles' motion capabilities. Based on the evaluation
results, the approach indicates improvement in a solution to the kinematic
orienteering problem and the team orienteering problem in the monitoring task
compared to the existing solutions. The proposed approach has been
experimentally verified, supporting its feasibility in real-world monitoring
tasks
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
Spatial coverage in routing and path planning problems
Routing and path planning problems that involve spatial coverage have received increasing attention in recent years in different application areas. Spatial coverage refers to the possibility of considering nodes that are not directly served by a vehicle as visited for the purpose of the objective function or constraints. Despite similarities between the underlying problems, solution approaches have been developed in different disciplines independently, leading to different terminologies and solution techniques. This paper proposes a unified view of the approaches: Based on a formal introduction of the concept of spatial coverage in vehicle routing, it presents a classification scheme for core problem features and summarizes problem variants and solution concepts developed in the domains of operations research and robotics. The connections between these related problem classes offer insights into common underlying structures and open possibilities for developing new applications and algorithms
Information-Driven Path Planning for UAV with Limited Autonomy in Large-scale Field Monitoring
This paper presents a novel information-based mission planner for a drone
tasked to monitor a spatially distributed dynamical phenomenon. For the sake of
simplicity, the area to be monitored is discretized. The insight behind the
proposed approach is that, thanks to the spatio-temporal dependencies of the
observed phenomenon, one does not need to collect data on the entire area. In
fact, unmeasured states can be estimated using an estimator, such as a Kalman
filter. In this context the planning problem becomes the one of generating a
flight path that maximizes the quality of the state estimation while satisfying
the flight constraints (e.g. flight time). The first result of this paper is to
formulate this problem as a special Orienteering Problem where the cost
function is a measure of the quality of the estimation. This approach provides
a Mixed-Integer Semi-Definite formulation to the problem which can be optimally
solved for small instances. For larger instances, two heuristics are proposed
which provide good sub-optimal results. To conclude, numerical simulations are
shown to prove the capabilities and efficiency of the proposed path planning
strategy. We believe this approach has the potential to increase dramatically
the area that a drone can monitor, thus increasing the number of applications
where monitoring with drones can become economically convenient
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