Communication constraints can significantly impact robots’ ability to share information, coordinate their movements, and synchronize their actions, thus limiting coordination in Multi-Robot Exploration (MRE) applications. In this work, we address these challenges by modeling the MRE application as a DEC-POMDP and designing a joint policy that follows a rendezvous plan. This policy allows robots to explore unknown environments while intermittently sharing maps opportunistically or at rendezvous locations without being constrained by joint path optimizations. To generate the rendezvous plan, robots represent the MRE task as an instance of the Job Shop Scheduling Problem (JSSP) and minimize JSSP metrics. They aim to reduce waiting times and increase connectivity, which correlates to the DEC-POMDP rewards and time to complete the task. Our simulation results suggest that our method is more efficient than using relays or maintaining intermittent communication with a base station, being a suitable approach for Multi-Robot Exploration. We developed a proof-of-concept using the Robot Operating System (ROS) that is available at: https://github.com/multirobotplayground/Noetic-Multi-Robot-Sandbox.

The recent increase in data availability and reliability has led to a surge in the development of learning-based model predictive control (MPC) frameworks for robot systems. Despite attaining substantial performance improvements over their non-learning counterparts, many of these frameworks rely on an offline learning procedure to synthesize a dynamics model. This implies that uncertainties encountered by the robot during deployment are not accounted for in the learning process. On the other hand, learning-based MPC methods that learn dynamics models online are computationally expensive and often require a significant amount of data. To alleviate these shortcomings, we propose a novel learning-enhanced MPC framework that incorporates components from C1adaptive control into learning-based MPC. This integration enables the accurate compensation of both matched and unmatched uncertainties in a sample-efficient way, enhancing the control performance during deployment. In our proposed framework, we present two variants and apply them to the control of a quadrotor system. Through simulations and physical experiments, we demonstrate that the proposed framework not only allows the synthesis of an accurate dynamics model on-the-fly, but also significantly improves the closed-loop control performance under a wide range of spatio-temporal uncertainties.

With the rapidly changing climate and an increase in extreme weather events, it is necessary to have better methods to monitor and study the impacts of these phenomena on urban river environments. Multi-robot environmental monitoring has long focused on strategies that assign individual robots to distinct regions or task objectives. While these methods have seen success for Autonomous Surface Vehicles (ASVs), the spatial expanse and temporal variability of rivers impose an increased burden on existing techniques, necessitating computationally intensive replanning. Alternative methods aim to model and control teams of robots by prescribing global constraints on the system, using the insight that robots' transitions between tasks are stochastic and time-based. These methods do not require replanning because robots will perform different tasks achieving the overall desired system state, focusing on temporal switching alone limits their overall descriptive power. In this paper, we present a method that considers collaborations between robots to inform task switching based on spatial proximity. Our results suggest that in unknown environments macroscopic models provide increased flexibility for individual robot task execution as compared to coverage control methods.