Scalar field features such as extrema, contours, and saddle points are essential for applications in environmental monitoring, search and rescue, and resource exploration. Traditional navigation methods often rely on predefined trajectories, leading to inefficient and resource-intensive mapping. This paper introduces a new adaptive navigation framework that leverages learning techniques to enhance exploration efficiency and effectiveness in scalar fields, even under noisy data and obstacles. The framework employs Partial Differential Equations to model scalar fields and a Gaussian Process Regressor to estimate the fields and their gradients, enabling real-time path adjustments and obstacle avoidance. We provide a theoretical foundation for the approach and address several limitations found in existing methods. The effectiveness of our framework is demonstrated through simulation benchmarks and field experiments with an Autonomous Surface Vehicle, showing improved efficiency and adaptability compared to traditional methods and offering a robust solution for real-time environmental monitoring.

Predicting the near-term behavior of a reactive agent is crucial in many robotic scenarios, yet remains challenging when observations of that agent are sparse or intermittent. Vision-Language Models (VLMs) offer a promising avenue by integrating textual domain knowledge with visual cues, but their one-shot predictions often miss important edge cases and unusual maneuvers. Our key insight is that iterative, counterfactual exploration–where a dedicated module probes each proposed behavior hypothesis, explicitly represented as a plausible trajectory, for overlooked possibilities–can significantly enhance VLM-based behavioral forecasting. We present TRACE (Tree-of-thought Reasoning And Counterfactual Exploration), an inference framework that couples tree-of-thought generation with domain-aware feedback to refine behavior hypotheses over multiple rounds. Concretely, a VLM first proposes candidate trajectories for the agent; a counterfactual critic then suggests edge-case variations consistent with partial observations, prompting the VLM to expand or adjust its hypotheses in the next iteration. This creates a self-improving cycle where the VLM progressively internalizes edge cases from previous rounds, systematically uncovering not only typical behaviors but also rare or borderline maneuvers, ultimately yielding more robust trajectory predictions from minimal sensor data. We validate TRACE on both ground-vehicle simulations and real-world marine autonomous surface vehicles. Experimental results show that our method consistently outperforms standard VLM-driven and purely model-based baselines, capturing a broader range of feasible agent behaviors despite sparse sensing. Evaluation videos and code are available at trace-robotics.github.io.

This paper addresses the multi-robot planning problem to generate trajectories for maximum information gathering from an area of interest. To solve this problem, the proposed framework first leverages a Gaussian Mixture Model (GMM) as prior knowledge for modeling informative regions in the target area. Taking samples from the GMM, informative robot paths are then computed that optimize the travel costs subject to energy budgets. Decomposing these paths into waypoints, robot trajectories are planned while respecting kinematic constraints and subsequently replanned online to avoid collisions among themselves. The GMM model is incrementally updated by incorporating the gathered information. We demonstrate that our framework can achieve a significant amount of information gain with the optimal travel distance. We also provide a realistic simulation with a team of mobile robots in a port infrastructure monitoring setting.

Communication is one of the most popular and efficient means of multi-robot coordination. Due to potential real-world constraints, such as limited bandwidth and contested scenarios, a communication strategy requiring to send, for example, all n bits of an environment representation might not be feasible in situations where the robots' data exchanges are frequent and large. To this end, we propose and implement lightweight, bandwidth-efficient, robot-to-robot communication protocols inspired by communication complexity results for data synchronization without exchanging the originally required n bits. We have tested our proposed approach both in simulation and with real robots. Simulation results show that the proposed method is computationally fast and enables the robots to synchronize the data (near) accurately while exchanging significantly smaller amounts of information (in the order of log n bits). Real-world experiments with two mobile robots show the practical feasibility of our proposed approach.

We consider the problem of multiple robots that must cooperate within a shared environment, but which wish to limit the information they disclose during their coordination efforts. Specifically, we examine the problems of privacy-preserving rendezvous and persistent monitoring. In the former, the robots construct a joint plan to have them meet, without either knowing beforehand where or when the meeting will occur. In the latter, multiple robots dynamically cover a region of space-they plan collective motions which are collision-free but with the assurance that agents remain ignorant of the paths of others. Accordingly, the tasks are sort of inverses in that the robots must collectively determine whether their joint paths collide or not, then, using this, achieve their collective task. Other than what is learned by the outcome of the joint-collision determination, the robots possess no details of the other paths. Our approach builds on garbled circuits and homomorphic encryption to realize basic secure path intersection primitives. We present algorithms, a software implementation, and a physical experiment on mobile robots to test the practical feasibility of our approach. We believe that these ideas provide a valuable direction for adoption in small Unmanned Systems belonging to different stakeholders.

Traditional environmental and structural monitoring often uses static sensor networks deployed at predetermined locations or mobile robots that use a rastering technique for area coverage. These methods rely on the operators making assumptions about the nature of the unknown field that is being measured and are often time-consuming for localizing an area of interest. Here, we aim to quickly localize possible leaks within high-level nuclear waste tanks at the Hanford facility. The structure of these tanks precludes most sensor network approaches and raises many issues with robotic inspection, such as navigation within highly constrained environments. This work uses a Bayesian Optimization approach for guiding a mobile robot's search strategy and implements a utility function that allows for prior knowledge of the structure to be incorporated when selecting future search locations. Compared to traditional exhaustive approaches, our method quickly reduces RMSE error and shortens the distance the robot must travel.

Collisions between automated moving equipment and human workers in job sites are one of the main sources of fatalities and accidents during the execution of construction projects. In this paper, we present a methodology to identify and assess project plans in terms of hazards before their execution. Our methodology has the following steps: 1) several potential plans are extracted from an initial activity graph; 2) plans are translated from a high-level activity graph to a discrete-event simulation model; 3) trajectories and safety policies are generated that avoid static and moving obstacles using existing motion planning algorithms; 4) safety scores and risk-based heatmaps are calculated based on the trajectories of moving equipment; and 5) managerial implications are provided to select an acceptable plan with the aid of a sensitivity analysis of different factors (cost, resources, and deadlines) that affect the safety of a plan. Finally, we present illustrative case study examples to demonstrate the usefulness of our model.

In this paper, we solve the problem of relay robot placement in multi-robot missions to establish or enhance communication between a static operator and a number of remote units in an environment with known obstacles. We study the hardness of two different relay placement problems: 1) a chain formation of multiple relay robots to transmit information from an operator to a single unit; and 2) a spanning tree of relays connecting multiple remote units to the operator. We first build a communication map data structure from a layered graph that contains the positions of the relays as the unit moves. This structure is computed once and reused throughout the mission, significantly reducing plan re-computation time when compared to the best-known solution in the literature. Second, we create a min-arborescence tree that forms a connected component among the operator, relays, and units, and that has an optimal communication cost. Finally, we validate our ideas through software simulations, hardware experiments, and a comparison of our approach to state-of-the-art methods.

Collisions between moving machinery and human workers in construction job sites are one of the main sources of fatalities and accidents during the execution of construction projects. In this paper, we present a methodology to identify and assess construction project plan dangers before their execution. Our methodology has the following steps: 1) Plans are translated from a high-level activity graph to a discrete event simulation model; 2) Trajectories are simulated using sampling based and combinatorial motion planning algorithms; and 3) Safety scores and risk-based heatmaps are calculated based on the trajectories of moving equipment. Finally, we present an illustrative case study to demonstrate the usability of our model.

This paper presents strategies for controlling the distribution of large numbers of minimalist robots (ones containing no sensors or computers). The strategies are implemented by varying area, speed, gate length, or gate configuration in environments composed of regions connected by gates and modelled by Continuous Time Markov chains. We demonstrate the effectiveness and practical feasibility of our strategies through physical experiments and simulation. We use Continuous Stochastic Logic to verify high level properties of our system and to evaluate the accuracy of our model. Also, we prove that our model is accurate and that our algorithms are efficient with respect to the number of regions and number of bodies.

The tight integration of mobile devices and apps into our daily routine impacts our approach to health. While existing solutions propose to take advantage of recent developments in mobile and sensor technologies to encourage users to lead healthier lives, we still lack a viable approach that motivates user participation. In this paper we aim to integrate fitness challenges into the daily routine of users, in order to motivate them to participate more frequently. We develop GeoFit, a mobile app that enables users to discover and add novel fitness challenges in their proximity. In addition to achievement badges, GeoFit relies on top-k lists to motivate users to become top performers. Since participation incentives may also raise cheating concerns, GeoFit leverages GPS and accelerometer sensors of the mobile device to verify the authenticity of fitness challenges performed by users. We have implemented and tested GeoFit on Android devices. Our experimental results show that the GeoFit client imposes only little overhead on the user device, while the server side can support hundreds of client interactions per second.

A problem is introduced in which a moving body (robot, human, animal, vehicle, and so on) travels among obstacles and binary detection beams that connect between obstacles or barriers. Each beam can be viewed as a virtual sensor that may have many possible alternative implementations. The task is to determine the possible body paths based only on sensor observations that each simply report that a beam crossing occurred. This is a basic filtering problem encountered in many settings, under a variety of sensing modalities. Filtering methods are presented that reconstruct the set of possible paths at three levels of resolution: (1) the possible sequences of regions (bounded by beams and obstacles) visited, (2) equivalence classes of homo-topic paths, and (3) the possible numbers of times the path winds around obstacles. In the simplest case, all beams are disjoint, distinguishable, and directed. More complex cases are then considered, allowing for any amount of beams overlapping, indistinguishability, and lack of directional information. The method was implemented in simulation. An inexpensive, low-energy, easily deployable architecture was also created which implements the beam model and validates the methods of the article with experiments.

We consider the problem of determining the paths of multiple, unpredictable moving bodies in a cluttered environment using weak detection sensors that provide simple crossing information. Each sensor is a beam that, when broken, provides the direction of the crossing (one bit) and nothing else. Using a simple network of beams, the individual paths are separated and reconstructed as well as possible, up to combinatorial information about the route taken. In this setup, simple filtering algorithms are introduced, and a low-cost hardware implementation that demonstrates the practicality of the approach is shown. The results may apply in settings such as verification of multirobot system execution, surveillance and security, and unobtrusive behavioral monitoring for wildlife and the elderly.

Developing computational methods for assigning protein function from tertiary structure is a very important problem, predicting a catalytic mechanism based only on structural information being a particularly challenging task. This work focuses on helping to understand the molecular basis of catalysis by exploring the nature of catalytic residues, their environment and characteristic properties in a large data set of enzyme structures and using this information to predict enzyme structures' active sites. A machine learning approach that performs feature extraction, clustering and classification on a protein structure data set is proposed. The 6,376 residues directly involved in enzyme catalysis, present in more than 800 proteins structures in the PDB were analyzed. Feature extraction provided a description of critical features for each catalytic residue, which were consistent with prior knowledge about them. Results from k-fold-cross-validation for classification showed more than 80% accuracy. Complete enzymes were scanned using these classifiers to locate catalytic residues.

Since there is a strong need for computational methods to predict and characterize functional sites for initial anno- tations of protein structures, a new methodology that relies on descriptions of the functional sites based on local prop- erties is proposed in this paper. This new approach is in- dependent of conserved residues and conserved residue ge- ometry and takes advantage of the large number of protein structures available to construct models using a machine learning approach. Particularly, the proposed method per- formed feature extraction, clustering and classification on a protein structure data set, and it was validated on metal- binding sites (Ca2+, Zn2+, Na+,K+, Mg2+, Mn2+, Cu2+, Fe3+, Hg2+, Cl-) present in a non-redundant PDB (a total of 11,959 metal-binding sites in 3,609 proteins). Feature extraction provided a description of critical fea- tures for each metal-binding site, which were consistent with prior knowledge about them. Furthermore, new in- sights about metal-binding site microenvironments could be provided by the descriptors thus obtained. Results using k-fold cross-validation for classification showed accuracy above 90%. Complete proteins were scanned using these classifiers to locate metal-binding sites. Keywords: Functional Genomics, Protein functional sites, Feature Extraction, Clustering, Classification, Metal- binding sites. Java source code available upon request. Supplementary Website: http://dis.unal.edu.co/~biocomp/metals/

In this work, a genetic word clustering algorithm, that classifies words present in the phrases of a linguistic corpus, is proposed. The underlying goal of word classification is to build a good probabilistic model of the language defined by the phrases in the corpus. Some experiments comparing the performance of the proposed algorithm with a classical word clustering algorithm were carried out.