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1. An Extended Bayesian Optimization Approach to Decentralized Swarm Robotic Search

   https://doi.org/10.1115/1.4046587  

   Ghassemi, Payam; Chowdhury, Souma (October 2020, Journal of Computing and Information Science in Engineering)

   null (Ed.)

   Abstract Swarm robotic search aims at searching targets using a large number of collaborating simple mobile robots, with applications to search and rescue and hazard localization. In this regard, decentralized swarm systems are touted for their coverage scalability, time efficiency, and fault tolerance. To guide the behavior of such swarm systems, two broad classes of approaches are available, namely, nature-inspired swarm heuristics and multi-robotic search methods. However, the ability to simultaneously achieve efficient scalability and provide fundamental insights into the exhibited behavior (as opposed to exhibiting a black-box behavior) remains an open problem. To address this problem, this paper extends the underlying search approach in batch-Bayesian optimization to perform search with embodied swarm agents operating in a (simulated) physical 2D arena. Key contributions lie in (1) designing an acquisition function that not only balances exploration and exploitation across the swarm but also allows modeling knowledge extraction over trajectories and (2) developing its distributed implementation to allow asynchronous task inference and path planning by the swarm robots. The resulting collective informative path planning approach is tested on target-search case studies of varying complexity, where the target produces a spatially varying (measurable) signal. Notably, superior performance, in terms of mission completion efficiency, is observed compared to exhaustive search and random walk baselines as well as a swarm optimization-based state-of-the-art method. Favorable scalability characteristics are also demonstrated. 

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2. Fully differentiable sensor placement and informative path planning

   https://doi.org/10.1177/02783649251384993  

   Jakkala, Kalvik; Akella, Srinivas (November 2025, The International Journal of Robotics Research)

   Sensor placement (SP) and informative path planning (IPP) problems are prevalent in environmental monitoring. These problems require gathering the most informative data from a limited number of sensing locations, but existing solutions face a difficult trade-off. Existing methods are often either computationally efficient but less informative, or more informative but too computationally expensive for practical use, especially on resource-constrained robots. Furthermore, many approaches are limited by requiring discretization of the environment or relying on slow, derivative-free optimization techniques. This paper introduces a novel, computationally efficient variational formulation for the SP problem. Our approach is differentiable with respect to the sensing locations, enabling fast gradient-based optimization in continuous spaces and delivering performance comparable to MI-based methods at a fraction of the computational cost. We establish our formulation as a special case of sparse Gaussian processes (SGPs). This connection allows us to generalize the method to solve the IPP problem for single and multi-robot systems, efficiently incorporating differentiable path constraints and diverse sensor types. The approach is validated through extensive benchmarks and field experiments with an autonomous surface vehicle (ASV) and an autonomous underwater vehicle (AUV). We also provide SGP-Tools—an open-source Python library—and a companion ROS 2 package for Ardupilot-based mobile robots. 

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3. Automatic Synthesis of Smooth Infinite Horizon Paths Satisfying Linear Temporal Logic Specifications

   https://doi.org/10.1007/978-3-031-98685-7_12  

   Williams, Samuel; Deshmukh, Jyotirmoy (January 2025, Computer Aided Verification. CAV 2025. Lecture Notes in Computer Science, vol 15934.)

   Piskac, R; Rakamarić, Z (Ed.)

   Abstract Automatically constructing smooth paths that satisfy a formal specification is a challenging problem. Existing methods struggle to scale to long horizon specifications and challenging environments. We present a method that uses abstraction, model checking, and convex optimization to solve for a smooth Bézier spline that is guaranteed to satisfy a Linear Temporal Logic specification. Our approach uses a coarse abstraction to avoid the state explosion of other abstraction based methods, and successfully avoids the computational challenges of directly optimizing the non-convex temporal logic semantics. We prove our method is sound and complete and demonstrate a significant computational advantage relative to state of the art approaches. Generating such smooth paths has natural applications in path planning for autonomous robots, and we demonstrate the applicability of our method on path planning for a quadrotor. 

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4. (Vision Paper) A Vision for Spatio-Causal Situation Awareness, Forecasting, and Planning

   https://doi.org/10.1145/3672556  

   Azad, Fahim Tasneema; Candan, K Selçuk; Kapkiç, Ahmet; Li, Mao-Lin; Liu, Huan; Mandal, Pratanu; Sheth, Paras; Arslan, Bilgehan; Chowell-Puente, Gerardo; Sabo, John; et al (June 2024, ACM Transactions on Spatial Algorithms and Systems)

   Successfully tackling many urgent challenges in socio-economically critical domains, such as public health and sustainability, requires a deeper understanding of causal relationships and interactions among a diverse spectrum of spatio-temporally distributed entities. In these applications, the ability to leverage spatio-temporal data to obtain causally based situational awareness and to develop informed forecasts to provide resilience at different scales is critical. While the promise of a causally grounded approach to these challenges is apparent, the core data technologies needed to achieve these are in the early stages and lack a framework to help realize their potential. In this article, we argue that there is an urgent need for a novel paradigm of spatio-causal research built on computational advances in spatio-temporal data and model integration, causal learning and discovery, large scale data- and model-driven simulations, emulations, and forecasting, as well as spatio-temporal data-driven and model-centric operational recommendations, and effective causally driven visualization and explanation. We thus provide a vision, and a road map, for spatio-causal situation awareness, forecasting, and planning. 

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5. Decentralized Multi-Robot Information Gathering From Unknown Spatial Fields

   https://doi.org/10.1109/LRA.2023.3264720  

   Newaz, Abdullah Al; Alsayegh, Murtadha; Alam, Tauhidul; Bobadilla, Leonardo (May 2023, IEEE Robotics and Automation Letters)

   We present an incremental scalable motion planning algorithm for finding maximally informative trajectories for decentralized mobile robots. These robots are deployed to observe an unknown spatial field, where the informativeness of observations is specified as a density function. Existing works that are typically restricted to discrete domains and synchronous planning often scale poorly depending on the size of the problem. Our goal is to design a distributed control law in continuous domains and an asynchronous communication strategy to guide a team of cooperative robots to visit the most informative locations within a limited mission duration. Our proposed Asynchronous Information Gathering with Bayesian Optimization (AsyncIGBO) algorithm extends ideas from asynchronous Bayesian Optimization (BO) to efficiently sample from a density function. It then combines them with decentralized reactive motion planning techniques to achieve efficient multi-robot information gathering activities. We provide a theoretical justification for our algorithm by deriving an asymptotic no-regret analysis with respect to a known spatial field. Our proposed algorithm is extensively validated through simulation and real-world experiment results with multiple robots. 

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