RadCloud: Real-Time High-Resolution Point Cloud Generation Using Low-Cost Radars for Aerial and Ground Vehicles

In this work, we present RadCloud, a novel real time framework for directly obtaining higher-resolution lidar-like 2D point clouds from low-resolution radar frames on resource-constrained platforms commonly used in unmanned aerial and ground vehicles (UAVs and UGVs, respectively); such point clouds can then be used for accurate environmental mapping, navigating unknown environments, and other robotics tasks. While high-resolution sensing using radar data has been previously reported, existing methods cannot be used on most UAVs, which have limited computational power and energy; thus, existing demonstrations focus on offline radar processing. RadCloud overcomes these challenges by using a radar configuration with 1/4th of the range resolution and employing a deep learning model with 2.25x fewer parameters. Additionally, RadCloud utilizes a novel chirp-based approach that makes obtained point clouds resilient to rapid movements (e.g., aggressive turns or spins), which commonly occur during UAV flights. In real-world experiments, we demonstrate the accuracy and applicability of RadCloud on commercially available UAVs and UGVs, with off-the-shelf radar platforms on-board.

A Multi-Agent Security Testbed for the Analysis of Attacks and Defenses in Collaborative Sensor Fusion

The performance and safety of autonomous vehicles (AVs) deteriorates under adverse environments and adversarial actors. The investment in multi-sensor, multi-agent (MSMA) AVs is meant to promote improved efficiency of travel and mitigate safety risks. Unfortunately, minimal investment has been made to develop security-aware MSMA sensor fusion pipelines leaving them vulnerable to adversaries. To advance security analysis of AVs, we develop the Multi-Agent Security Testbed, MAST, in the Robot Operating System (ROS2). Our framework is scalable for general AV scenarios and is integrated with recent multi-agent datasets. We construct the first bridge between AVstack and ROS and develop automated AV pipeline builds to enable rapid AV prototyping. We tackle the challenge of deploying variable numbers of agent/adversary nodes at launch-time with dynamic topic remapping. Using this testbed, we motivate the need for security-aware AV architectures by exposing the vulnerability of centralized multi-agent fusion pipelines to (un)coordinated adversary models in case studies and Monte Carlo analysis.

Asymptotic Boundary Shrink Control with Multi-robot Systems

Harmful marine spills, such as algae blooms and oil spills, damage ecosystems and threaten public health tremendously. Hence, an effective spill coverage and removal strategy will play a significant role in environmental protection. In recent years, low-cost water surface robots have emerged as a solution, with their efficacy verified at small scale. However, practical limitations such as connectivity, scalability, and sensing and operation ranges significantly impair their large-scale use. To circumvent these limitations, we propose a novel asymptotic boundary shrink control strategy that enables collective coverage of a spill by autonomous robots featuring customized operation ranges. For each robot, a novel controller is implemented that relies only on local vision sensors with limited vision range. Moreover, the distributedness of this strategy allows any number of robots to be employed without inter-robot collisions. Finally, features of this approach including the convergence of robot motion during boundary shrink control, spill clearance rate, and the capability to work under limited ranges of vision and wireless connectivity are validated through extensive experiments with simulation.