Autonomous Marine Vehicles (AMVs) have gained interest for scientific and commercial applications, including pipeline and algae bloom monitoring, contaminant tracking, and ocean debris removal. The Team Orienteering Problem (TOP) is relevant in this context as Multi-Robot Systems (MRSs) allow for better coverage of the area of interest, simultaneous data collection at different locations, and an increase in the overall robustness and efficiency of the mission. However, route planning for AMVs in dynamic ocean environments is challenging due to the coupling of environmental and vehicle dynamics. We propose a multi-objective formulation that accounts for the trade-offs between visiting multiple task locations and energy consumption by the vehicles subject to a time budget. This work focuses on vehicles that can maintain a constant net speed but can be adapted to vehicles with constant thrust. Different from existing approaches, our method is able to leverage time-varying ocean currents to improve the energy efficiency of resulting routes. We validate our approach experimentally by superimposing ocean flow models with benchmark instances of the TOP.

In many environmental monitoring applications robots are often tasked to visit various distinct locations to make observations and/or collect specific measurements. The problem of scheduling and assigning robots to the various tasks and planning feasible paths for the robots can be posed as an Orienteering Problem (OP). In the standard OP, routing and scheduling is achieved by maximizing an objective function by visiting the most rewarding locations while respecting a limited travel budget. However, traditional formulations for such problems usually neglect some environmental features that can greatly impact the tour, e.g., flows, such as wind or ocean currents. This is of particular importance for applications in marine and atmospheric environments where vehicle motions can be significantly impacted by the environmental dynamics and the environment exerts a non-negligible force on the vehicles. In this paper, we tackle the OP in fluid environments where robots must operate in the presence of ocean and/or atmospheric currents. We introduce a novel multi-objective formulation that combines both task and path planning problems, and whose goals are to (i) maximize the collected reward, while (ii) minimizing the energy expenditure by leveraging the environmental dynamics wherever possible. We validate our strategy using simulated ocean model data to show that our approach can generate a diverse set of solutions that have an adequate compromise between both objectives.

In this paper, we propose an evolutionary algorithm for solving the multi-robot orienteering problem where a team of cooperative robots aims to maximize the total information collected by visiting a subset of given nodes within a fixed budget on travel costs. Multi-robot orienteering problems are relevant to applications such as logistic delivery services, precision agriculture, and environmental sampling and monitoring. We consider the case where the information gain at each node is related to the service time each robot spends at the node. As such, we address a variant of the Orienteering Problem where the collected rewards are a function of the time a robot spends at a given location. We present a genetic algorithm solver to this cooperative Team Orienteering Problem with service-time dependent rewards. We evaluate the approach over a diverse set of node configurations and for different team sizes. Lastly, we evaluate the effects of team heterogeneity on overall task performance through numerical simulations.

We present a path planning strategy for a magnetic millirobot where the nonlinearities in the external magnetic force field (MFF) are encoded in the graph used for planning. The strategy creates a library of candidate MFFs and characterizes their topologies by identifying the unstable manifolds in the workspace. The path planning problem is then posed as a graph search problem where the computed path consists of a sequence of unstable manifold segments and their associated MFFs. By tracking the robot's position and sequentially applying the MFFs, the robot navigates along each unstable manifold until it reaches the goal. We discuss the theoretical guarantees of the proposed strategy and experimentally validate the strategy.