We consider the problem of building the map of an unknown environment using multiple mobile robots that have physical limitations arising from dynamics and a limited onboard battery. We consider the setting where the unknown environment has a set of charging stations that the robots must discover and visit often to recharge their battery during the map building process. We propose an iterative approach to solve the resulting energy-constrained multi-robot exploration problem. Our approach uses a combination of frontier-based exploration, graph-based path planning, and multi-robot task assignment. We show that our algorithm admits a computationally inexpensive implementation that enables rapid replanning, and propose sufficient conditions for recursive feasibility and finite-time termination. We validate our approach in several Gazebo-based realistic simulations.

Autonomous navigation of Unmanned Aerial Vehicles (UAVs) has real-life applications in remote sensing, wildlife surveillance, search and rescue operations. A popular training paradigm to learn optimal actions for navigating such complex, dynamic, and uncertain environments is Reinforcement Learning (RL), where the optimal decisions are learnt over time through a reward-feedback received from the environment. However, manually constructing a feedback function that can help guide the UAV to accomplish the desired objective is often very hard. Preference-based Reinforcement Learning (PbRL) is an emerging sub-field of RL where the manual construction of reward function is replaced with human feedback. In this setting, a human is presented with a pair of trajectories followed by the RL agent to elicit the subject’s preference for one over the other. A PbRL algorithm would then compute an optimal sequence of actions using just the set of preferences collected over different trajectories. In this work, we consider PbRL for UAV navigation and follow an ensemble approach to enhance navigation performance. We demonstrate the efficacy of the proposed algorithm through experiments on a range of complex environments and tasks. Ours is the first work that uses human preferences to solve the UAV navigation problem to the best of our knowledge.

Visual-inertial SLAM is essential for robot navigation in GPS-denied environments, e.g. indoor, underground. Conventionally, the performance of visual-inertial SLAM is evaluated with open-loop analysis, with a focus on the drift level of SLAM systems. In this paper, we raise the question on the importance of visual estimation latency in closed-loop navigation tasks, such as accurate trajectory tracking. To understand the impact of both drift and latency on visualinertial SLAM systems, a closed-loop benchmarking simulation is conducted, where a robot is commanded to follow a desired trajectory using the feedback from visual-inertial estimation. By extensively evaluating the trajectory tracking performance of representative state-of-the-art visual-inertial SLAM systems, we reveal the importance of latency reduction in visual estimation module of these systems. The findings suggest directions of future improvements for visual-inertial SLAM.