Jimmy Chiun

In multi-robot exploration, a team of mobile robot is tasked with efficiently mapping an unknown environments. While most exploration planners assume omnidirectional sensors like LiDAR, this is impractical for small robots such as drones, where lightweight, directional sensors like cameras may be the only option due to payload constraints. These sensors have a constrained field-of-view (FoV), which adds complexity to the exploration problem, requiring not only optimal robot positioning but also sensor orientation during movement. In this work, we propose MARVEL, a neural framework that leverages graph attention networks, together with novel frontiers and orientation features fusion technique, to develop a collaborative, decentralized policy using multi-agent reinforcement learning (MARL) for robots with constrained FoV. To handle the large action space of viewpoints planning, we further introduce a novel information-driven action pruning strategy. MARVEL improves multi-robot coordination and decision-making in challenging large-scale indoor environments, while adapting to various team sizes and sensor configurations (i.e., FoV and sensor range) without additional training. Our extensive evaluation shows that MARVEL's learned policies exhibit effective coordinated behaviors, outperforming state-of-the-art exploration planners across multiple metrics. We experimentally demonstrate MARVEL's generalizability in large-scale environments, of up to 90 m by 90 m, and validate its practical applicability through successful deployment on a team of real drone hardware.

In recent years, the field of aerial robotics has witnessed significant progress, finding applications in diverse domains, including post-disaster search and rescue operations. Despite these strides, the prohibitive acquisition costs associated with deploying physical multi-UAV systems have posed challenges, impeding their widespread utilization in research endeavors. To overcome these challenges, we present STAR (Swarm Technology for Aerial Robotics Research), a framework developed explicitly to improve the accessibility of aerial swarm research experiments. Our framework introduces a swarm architecture based on the Crazyflie, a low-cost, open-source, palm-sized aerial platform, well suited for experimental swarm algorithms. To augment cost-effectiveness and mitigate the limitations of employing low-cost robots in experiments, we propose a landmark-based localization module leveraging fiducial markers. This module, also serving as a target detection module, enhances the adaptability and versatility of the framework. Additionally, collision and obstacle avoidance are implemented through velocity obstacles. The presented work strives to bridge the gap between theoretical advances and tangible implementations, thus fostering progress in the field.

To perform outdoor visual navigation and search, a robot may leverage satellite imagery to generate visual priors. This can help inform high-level search strategies, even when such images lack sufficient resolution for target recognition. However, many existing informative path planning or search-based approaches either assume no prior information, or use priors without accounting for how they were obtained. Recent work instead utilizes large Vision Language Models (VLMs) for generalizable priors, but their outputs can be inaccurate due to hallucination, leading to inefficient search. To address these challenges, we introduce Search-TTA, a multimodal test-time adaptation framework with a flexible plug-and-play interface compatible with various input modalities (e.g., image, text, sound) and planning methods (e.g., RL-based). First, we pretrain a satellite image encoder to align with CLIP's visual encoder to output probability distributions of target presence used for visual search. Second, our TTA framework dynamically refines CLIP's predictions during search using uncertainty-weighted gradient updates inspired by Spatial Poisson Point Processes. To train and evaluate Search-TTA, we curate AVS-Bench, a visual search dataset based on internet-scale ecological data containing 380k images and taxonomy data. We find that Search-TTA improves planner performance by up to 30.0%, particularly in cases with poor initial CLIP predictions due to domain mismatch and limited training data. It also performs comparably with significantly larger VLMs, and achieves zero-shot generalization via emergent alignment to unseen modalities. Finally, we deploy Search-TTA on a real UAV via hardware-in-the-loop testing, by simulating its operation within a large-scale simulation that provides onboard sensing.

Many multi-robot applications require allocating a team of heterogeneous agents to complete a given set of spatially distributed tasks as quickly as possible, such as search and rescue, area inspection/monitoring, and space exploration. We focus on tasks which can only be initiated when all required agents have arrived, such as detection involving sensor fusion or cooperative assembly requiring robots with different tools/skillsets. Robots dynamically form and disband teams based on diverse abilities needed for each task, which, however, can potentially result in extra idle (waiting) times at the task location. Robots (agents) should take into account the schedules of others to maximize their collective efficiency and reduce overall task completion time (makespan). Conventional methods such as mix-integer programming generally require centralized scheduling and long optimization time, which limits their potential for real-world applications. In this work, we propose a decentralized method that fully relies on reinforcement learning (RL) to train a generalized policy applicable to heterogeneous agents. To address the challenge of complex cooperation learning, we further introduce a constrained flashforward mechanism to guide/constrain the agents' exploration and help them make better predictions. Through an attention mechanism that reasons about both short-term cooperation and long-term scheduling dependency, agents learn to reactively choose their next tasks (and subsequent coalitions) to avoid wasting abilities and to shorten the makespan. We compare our method with state-of-the-art heuristic and mixed-integer programming methods, demonstrating its generalization ability and showing it closely matches or outperforms these baselines while remaining at least two orders of magnitude faster.

In visibility-based pursuit-evasion tasks, a team of mobile pursuer robots with limited sensing capabilities is tasked with detecting all evaders in a multiply-connected planar environment, whose map may or may not be known to pursuers beforehand. This requires tight coordination among multiple agents to ensure that the omniscient and potentially arbitrarily fast evaders are guaranteed to be detected by the pursuers. Whereas existing methods typically rely on a relatively large team of agents to clear the environment, we propose ViPER, a neural solution that leverages a graph attention network to learn a coordinated yet distributed policy via multi-agent reinforcement learning (MARL). We experimentally demonstrate that ViPER significantly outperforms other state-of-the-art non-learning planners, showcasing its emergent coordinated behaviors and adaptability to more challenging scenarios and various team sizes, and finally deploy its learned policies on hardware in an aerial search task.

Mapless navigation refers to a challenging task where a mobile robot must rapidly navigate to a predefined destination using its partial knowledge of the environment, which is updated online along the way, instead of a prior map of the environment. Inspired by the recent developments in deep reinforcement learning (DRL), we propose a learning-based framework for mapless navigation, which employs a context-aware policy network to achieve efficient decision-making (ie, maximize the likelihood of finding the shortest route towards the target destination), especially in complex and large-scale environments. Specifically, our robot learns to form a context of its belief over the entire known area, which it uses to reason about long-term efficiency and sequence show-term movements. Additionally, we propose a graph rarefaction algorithm to enable more efficient decision-making in large-scale applications. We empirically demonstrate that our approach reduces average travel time by up to 61.4% and average planning time by up to 88.2% compared to benchmark planners (D* lite and BIT) on hundreds of test scenarios. We also validate our approach both in high-fidelity Gazebo simulations as well as on hardware, highlighting its promising applicability in the real world without further training/tuning.

Accurate mapping of indoor environments is crucial for various real-world applications, including search and rescue operations, as well as home robotics. Utilizing small, cost-effective drones provides significant advantages over traditional methods for mapping confined and unknown spaces. This paper advances existing robotics mapping research by presenting a novel algorithm for three-dimensional (3D) indoor environment mapping, particularly in non-Manhattan World settings. Our approach addresses the limitations of previous methods by introducing an efficient algorithm that filters noise, identifies structural corners using breadth-first search and convolution, and constructs nonintersecting polygons for environment mapping. Experimental results validated the effectiveness of our solution in producing reliable indoor maps where previous methods fail, showing improvements in automatic wall detection, noise resilience, and handling of more complex shapes and scenarios.