More Like this

1. Toward Safety-Aware Informative Motion Planning for Legged Robots

   Teng, Sangli; Gong, Yukai; Grizzle, Jessy; Ghaffari, Maani (March 2021, ArXivorg)

   null (Ed.)

   This paper reports on developing an integrated framework for safety-aware informative motion planning suitable for legged robots. The information-gathering planner takes a dense stochastic map of the environment into account, while safety constraints are enforced via Control Barrier Functions (CBFs). The planner is based on the Incrementally-exploring Information Gathering (IIG) algorithm and allows closed-loop kinodynamic node expansion using a Model Predictive Control (MPC) formalism. Robotic exploration and information gathering problems are inherently path-dependent problems. That is, the information collected along a path depends on the state and observation history. As such, motion planning solely based on a modular cost does not lead to suitable plans for exploration. We propose SAFE-IIG, an integrated informative motion planning algorithm that takes into account: 1) a robot’s perceptual field of view via a submodular information function computed over a stochastic map of the environment, 2) a robot’s dynamics and safety constraints via discrete-time CBFs and MPC for closedloop multi-horizon node expansions, and 3) an automatic stopping criterion via setting an information-theoretic planning horizon. The simulation results show that SAFE-IIG can plan a safe and dynamically feasible path while exploring a dense map. 

   more » « less
2. 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. 

   more » « less
3. Spatially temporally distributed informative path planning for multi-robot systems

   https://doi.org/10.23919/ACC63710.2025.11107919  

   Nguyen, Binh; Nguyen, Linh; Nghiem, Truong X; La, Hung; Baca, José; Rangel, Pablo; Montoya, Miguel Cid; Nguyen, Thang (July 2025, IEEE)

   This paper investigates the problem of informative path planning for a mobile robotic sensor network in spatially temporally distributed mapping. The robots are able to gather noisy measurements from an area of interest during their movements to build a Gaussian process (GP) model of a spatio-temporal field. The model is then utilized to predict the spatio-temporal phenomenon at different points of interest. To spatially and temporally navigate the group of robots so that they can optimally acquire maximal information gains while their connectivity is preserved, we propose a novel multi-step prediction informative path planning optimization strategy employing our newly defined local cost functions. By using the dual decomposition method, it is feasible and practical to effectively solve the optimization problem in a distributed manner. The proposed method was validated through synthetic experiments utilizing real-world data sets. 

   more » « less
4. 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. 

   more » « less
5. Multi-robot coordination for underwater Mothership–Passenger systems

   https://doi.org/10.3389/frobt.2026.1799672  

   Butler, Nathan L; Hollinger, Geoffrey A; Yu, Xi; Dutta, Prajjwal; Muto, Atsuhiro; Garwood, Jessica C (May 2026, Frontiers in robotics and AI)

   Multi-robot coordination under communication constraints is a fundamental challenge in autonomous systems, particularly in underwater environments, where low-bandwidth acoustic links restrict centralized planning and limit decentralized information propagation. In this study, we explore a “Mothership–passenger” paradigm, in which a capable underwater vehicle deploys and coordinates many lower-cost autonomous robots, enabling broad spatial coverage while reducing the mission risk and cost. We present two algorithms that tightly couple hierarchical guidance with decentralized planning to improve coordination efficiency under these constraints. The first algorithm integrates centralized and decentralized solutions to an underwater multi-robot orienteering problem, enabling globally coherent yet locally adaptive coordination among robots operating under stochastic travel costs and mission disruptions. The second algorithm introduces a learned policy on a Mothership vehicle that adaptively configures individual robot priorities, allowing globally informed guidance to shape local planning under communication and resource constraints. Through simulation and field experiments at an inland lake, we demonstrate that these contributions significantly improve coordination efficiency and robustness compared to fixed-behavior and fully decentralized baselines. These advances are directly motivated by the long-term goal of deploying coordinated robotic teams in isolated environments such as under-ice ocean regions, where the coordination and communication constraints studied here are most acute. 

   more » « less