Saurav Agarwal

Kalvik Jakkala, Saurav Agarwal, Jason O'Kane et al.

Environmental monitoring robots often need to estimate data fields (e.g., salinity, temperature, bathymetry) under tight resource constraints. Classical boustrophedon lawnmower surveys provide geometric coverage guarantees but can waste effort by oversampling predictable regions. In contrast, informative path planning (IPP) methods leverage spatial correlations to reduce oversampling, yet typically offer no guarantees on estimation quality. This paper bridges these approaches by addressing IPP with guaranteed estimation uncertainty in complex environments: computing the shortest path whose measurements ensure that the Gaussian process (GP) posterior variance -- an intrinsic uncertainty measure that lower-bounds the mean-squared prediction error under the GP model -- is upper bounded by a user-specified threshold over the monitoring region. We propose a three-stage approach for efficient environmental monitoring: (i) learning a GP model from prior information; (ii) transforming the GP kernel into binary coverage maps that identify locations where uncertainty can be reduced below a target threshold; and (iii) planning a near-shortest route to satisfy the global uncertainty constraint. Our approach incorporates non-stationary kernels to capture spatially varying correlations in heterogeneous phenomena and accommodates non-convex environments with obstacles. We provide near-optimal approximation guarantees for both sensing-location selection and the joint selection-and-routing problem under a travel budget. Experiments on real-world topographic data demonstrate that our planners achieve uncertainty targets with fewer sensing locations and shorter travel distances than representative baselines. Furthermore, field experiments with autonomous surface and underwater vehicles validate the real-world feasibility of the approach. Our code is available at: www.sgp-tools.com

Harsh Goel, Laura Jarin Lipschitz, Saurav Agarwal et al.

Active target sensing is the task of discovering and classifying an unknown number of targets in an environment and is critical in search-and-rescue missions. This paper develops a deep reinforcement learning approach to plan informative trajectories that increase the likelihood for an uncrewed aerial vehicle (UAV) to discover missing targets. Our approach efficiently (1) explores the environment to discover new targets, (2) exploits its current belief of the target states and incorporates inaccurate sensor models for high-fidelity classification, and (3) generates dynamically feasible trajectories for an agile UAV by employing a motion primitive library. Extensive simulations on randomly generated environments show that our approach is more efficient in discovering and classifying targets than several other baselines. A unique characteristic of our approach, in contrast to heuristic informative path planning approaches, is that it is robust to varying amounts of deviations of the prior belief from the true target distribution, thereby alleviating the challenge of designing heuristics specific to the application conditions.

Saurav Agarwal, Frederic Vatnsdal, Romina Garcia Camargo et al.

Collaboration in large robot swarms to achieve a common global objective is a challenging problem in large environments due to limited sensing and communication capabilities. The robots must execute a Perception-Action-Communication (PAC) loop -- they perceive their local environment, communicate with other robots, and take actions in real time. A fundamental challenge in decentralized PAC systems is to decide what information to communicate with the neighboring robots and how to take actions while utilizing the information shared by the neighbors. Recently, this has been addressed using Graph Neural Networks (GNNs) for applications such as flocking and coverage control. Although conceptually, GNN policies are fully decentralized, the evaluation and deployment of such policies have primarily remained centralized or restrictively decentralized. Furthermore, existing frameworks assume sequential execution of perception and action inference, which is very restrictive in real-world applications. This paper proposes a framework for asynchronous PAC in robot swarms, where decentralized GNNs are used to compute navigation actions and generate messages for communication. In particular, we use aggregated GNNs, which enable the exchange of hidden layer information between robots for computational efficiency and decentralized inference of actions. Furthermore, the modules in the framework are asynchronous, allowing robots to perform sensing, extracting information, communication, action inference, and control execution at different frequencies. We demonstrate the effectiveness of GNNs executed in the proposed framework in navigating large robot swarms for collaborative coverage of large environments.

Frederic Vatnsdal, Romina Garcia Camargo, Saurav Agarwal et al.

We propose MADP, a novel diffusion-model-based approach for collaboration in decentralized robot swarms. MADP leverages diffusion models to generate samples from complex and high-dimensional action distributions that capture the interdependencies between agents' actions. Each robot conditions policy sampling on a fused representation of its own observations and perceptual embeddings received from peers. To evaluate this approach, we task a team of holonomic robots piloted by MADP to address coverage control-a canonical multi agent navigation problem. The policy is trained via imitation learning from a clairvoyant expert on the coverage control problem, with the diffusion process parameterized by a spatial transformer architecture to enable decentralized inference. We evaluate the system under varying numbers, locations, and variances of importance density functions, capturing the robustness demands of real-world coverage tasks. Experiments demonstrate that our model inherits valuable properties from diffusion models, generalizing across agent densities and environments, and consistently outperforming state-of-the-art baselines.

Shreyas Muthusamy, Damian Owerko, Charilaos I. Kanatsoulis et al.

Unlabeled motion planning involves assigning a set of robots to target locations while ensuring collision avoidance, aiming to minimize the total distance traveled. The problem forms an essential building block for multi-robot systems in applications such as exploration, surveillance, and transportation. We address this problem in a decentralized setting where each robot knows only the positions of its $k$-nearest robots and $k$-nearest targets. This scenario combines elements of combinatorial assignment and continuous-space motion planning, posing significant scalability challenges for traditional centralized approaches. To overcome these challenges, we propose a decentralized policy learned via a Graph Neural Network (GNN). The GNN enables robots to determine (1) what information to communicate to neighbors and (2) how to integrate received information with local observations for decision-making. We train the GNN using imitation learning with the centralized Hungarian algorithm as the expert policy, and further fine-tune it using reinforcement learning to avoid collisions and enhance performance. Extensive empirical evaluations demonstrate the scalability and effectiveness of our approach. The GNN policy trained on 100 robots generalizes to scenarios with up to 500 robots, outperforming state-of-the-art solutions by 8.6\% on average and significantly surpassing greedy decentralized methods. This work lays the foundation for solving multi-robot coordination problems in settings where scalability is important.

Saurav Agarwal, Ramya Muthukrishnan, Walker Gosrich et al.

Coverage control is the problem of navigating a robot swarm to collaboratively monitor features or a phenomenon of interest not known a priori. The problem is challenging in decentralized settings with robots that have limited communication and sensing capabilities. We propose a learnable Perception-Action-Communication (LPAC) architecture for the problem, wherein a convolutional neural network (CNN) processes localized perception; a graph neural network (GNN) facilitates robot communications; finally, a shallow multi-layer perceptron (MLP) computes robot actions. The GNN enables collaboration in the robot swarm by computing what information to communicate with nearby robots and how to incorporate received information. Evaluations show that the LPAC models -- trained using imitation learning -- outperform standard decentralized and centralized coverage control algorithms. The learned policy generalizes to environments different from the training dataset, transfers to larger environments with more robots, and is robust to noisy position estimates. The results indicate the suitability of LPAC architectures for decentralized navigation in robot swarms to achieve collaborative behavior.

Saurav Agarwal, Vikram Shree, Suman Chakravorty

The SLAM problem is known to have a special property that when robot orientation is known, estimating the history of robot poses and feature locations can be posed as a standard linear least squares problem. In this work, we develop a SLAM framework that uses relative feature-to-feature measurements to exploit this structural property of SLAM. Relative feature measurements are used to pose a linear estimation problem for pose-to-pose orientation constraints. This is followed by solving an iterative non-linear on-manifold optimization problem to compute the maximum likelihood estimate for robot orientation given relative rotation constraints. Once the robot orientation is computed, we solve a linear problem for robot position and map estimation. Our approach reduces the computational burden of non-linear optimization by posing a smaller optimization problem as compared to standard graph-based methods for feature-based SLAM. Further, empirical results show our method avoids catastrophic failures that arise in existing methods due to using odometery as an initial guess for non-linear optimization, while its accuracy degrades gracefully as sensor noise is increased. We demonstrate our method through extensive simulations and comparisons with an existing state-of-the-art solver.

Saurav Agarwal, Amirhossein Tamjidi, Suman Chakravorty

This paper presents a method for motion planning under uncertainty to deal with situations where ambiguous data associations result in a multimodal hypothesis on the robot state. In the global localization problem, sometimes referred to as the "lost or kidnapped robot problem", given little to no a priori pose information, the localization algorithm should recover the correct pose of a mobile robot with respect to a global reference frame. We present a Receding Horizon approach, to plan actions that sequentially disambiguate a multimodal belief to achieve tight localization on the correct pose in finite time, i.e., converge to a unimodal belief. Experimental results are presented using a physical ground robot operating in an artificial maze-like environment. We demonstrate two runs wherein the robot is given no a priori information about its initial pose and the planner is tasked to localize the robot.

Ali-akbar Agha-mohammadi, Saurav Agarwal, Sung-Kyun Kim et al.

Simultaneous localization and Planning (SLAP) is a crucial ability for an autonomous robot operating under uncertainty. In its most general form, SLAP induces a continuous POMDP (partially-observable Markov decision process), which needs to be repeatedly solved online. This paper addresses this problem and proposes a dynamic replanning scheme in belief space. The underlying POMDP, which is continuous in state, action, and observation space, is approximated offline via sampling-based methods, but operates in a replanning loop online to admit local improvements to the coarse offline policy. This construct enables the proposed method to combat changing environments and large localization errors, even when the change alters the homotopy class of the optimal trajectory. It further outperforms the state-of-the-art FIRM (Feedback-based Information RoadMap) method by eliminating unnecessary stabilization steps. Applying belief space planning to physical systems brings with it a plethora of challenges. A key focus of this paper is to implement the proposed planner on a physical robot and show the SLAP solution performance under uncertainty, in changing environments and in the presence of large disturbances, such as a kidnapped robot situation.

Saurav Agarwal, Amirhossein Tamjidi, Suman Chakravorty

Planning under uncertainty is a key requirement for physical systems due to the noisy nature of actuators and sensors. Using a belief space approach, planning solutions tend to generate actions that result in information seeking behavior which reduce state uncertainty. While recent work has dealt with planning for Gaussian beliefs, for many cases, a multi-modal belief is a more accurate representation of the underlying belief. This is particularly true in environments with information symmetry that cause uncertain data associations which naturally lead to a multi-modal hypothesis on the state. Thus, a planner cannot simply base actions on the most-likely state. We propose an algorithm that uses a Receding Horizon Planning approach to plan actions that sequentially disambiguate the multi-modal belief to a uni-modal Gaussian and achieve tight localization on the true state, called a Multi-Modal Motion Planner (M3P). By combining a Gaussian sampling-based belief space planner with M3P, and introducing a switching behavior in the planner and belief representation, we present a holistic end-to-end solution for the belief space planning problem. Simulation results for a 2D ground robot navigation problem are presented that demonstrate our method's performance.