A common class of algorithms for informative path planning (IPP) follows boustrophedon ("as the ox turns") patterns, which aim to achieve uniform area coverage. However, IPP is often applied in scenarios where anomalies, such as plant diseases, pollution, or hurricane damage, appear in clusters. In such cases, prioritizing the exploration of anomalous regions over uniform coverage is beneficial. This work introduces a class of algorithms referred to as bounomōdes ("as the ox grazes"), which alternates between uniform boustrophedon sampling and targeted exploration of detected anomaly clusters. While uniform sampling can be designed using geometric principles, close exploration of clusters depends on the spatial distribution of anomalies and must be learned. In our implementation, the close exploration behavior is learned using deep reinforcement learning algorithms. Experimental evaluations demonstrate that the proposed approach outperforms several established baselines.

Gas distribution mapping (GDM) refers to the task of mapping the gas concentrations of an airborne chemical over a region of interest. A mobile robot equipped with a gas sensor can be used potentially autonomously to build such a distribution map. However, modern-day robots might not have enough battery power to cover the entire area of interest. Therefore, a group of n such collaborative mobile robots can be used for this purpose. The goal of the robots is to sample concentrations from a fraction of locations and infer the gas intensities in the rest of the area using a supervised machine learning technique, namely the Gaussian Process (GP). To this end, we propose a novel multi-robot gas distribution mapping framework, named GDM-Net++, which works in both 2D and 3D settings. Our proposed framework first divides the environment into n unique regions using Voronoi partitioning. Next, we employ a multi-agent deep Q-learning framework for the robots to learn a joint policy. As GP is a compute-intensive process, during testing, the learned policy is applied without re-training the GP model. The experiments are performed in simulation using Python on six types of Gaussian plumes to validate our proposed technique. Compared to two baselines – greedy and random walk, GDM-Net++ performs by 278% and 852% better in terms of earned rewards, while outperforming them by 34% and 155%, respectively, in terms of the precision of gas distribution modeling across unseen 2D test cases. Our approach can also gracefully handle 2D GDM scenarios where the distribution is consistently affected by wind.

In a gas distribution mapping (GDM) task, the objective of a mobile robot is to map the gas concentrations of an airborne chemical over a region of interest using onboard sensing. Given the limited battery budget available to the robot, covering the entire area to measure gas concentrations at every location might be infeasible. Assuming that the robot only has a budget for b meters of travel, in the rest of the locations, gas concentrations can be inferred using a supervised machine learning technique, namely the Gaussian Process (GP). In this paper, we propose a novel technique that combines deep reinforcement learning and GP regression to find an effective policy for GDM. We have implemented the proposed technique in Python within a 16×16 4-connected plane. We have used six types of Gaussian plumes to validate our presented approach. Compared to two popular baselines, our approach outperforms greedy and random exploration by 62% and 151% in terms of earned rewards, while outperforming them by 47% and 345%, respectively, in terms of the precision of gas distribution modeling in all test cases without obstacles. Our approach also improves the coverage of the exploration while consequently reducing the uncertainty in the prediction.

Disease control is paramount in modern agriculture to ensure optimal yield. Monitoring the spread of crop diseases is crucial for effective control measures. Traditional methods involve uniform pesticide spraying across entire fields, which can be inefficient and environmentally harmful. In this paper, we propose an intelligent solution employing mobile robots equipped with predictive AI techniques for disease monitoring and targeted intervention. These robots strategically visit select locations within the field, guided by a convolutional and recurrent neural network model trained on limited data to predict disease spread. We introduce a novel weighted path planning algorithm to optimize robot movement within the field considering disease risk and battery constraints. Our approach is implemented in the WaterBerry benchmark, an open-source platform for agricultural robotics. Experimental results demonstrate the efficacy of our technique, showcasing improved prediction accuracy and operational efficiency compared to baseline methods.

This paper introduces Confidence Guided Path-planning (CGP), an algorithm for planning the path of mobile sensor nodes with the goal to increase confidence in the accuracy of the estimated model at any time point in the data collection process. The approach employs a local estimator based on a Gaussian process regressor and takes advantage of the uncertainty estimation to guide the sensor to areas of lower confidence. In an experimental study comparing CGP with systematic lawnmower-type exploration and random waypoint movement, we found that CGP achieves better scores than both during most of the exploration process, being outperformed only by a fully completed systematic exploration. We also found that, as an emergent property of pursuing higher confidence, CGP achieves good coverage of the area of interest. The proposed algorithm has wide applications in precision agriculture, wildlife tracking, and road monitoring, where exhaustive coverage is not feasible.

We study the problem of wildfire surveillance using autonomous unmanned aerial vehicles (UAVs). The objective of the UAVs is to find the maximum number of locations that are under fire, assuming that the UAVs can share their observations. We propose a deep recurrent Q-learning technique that uses these observations to make decisions for the robots, i.e., where to move next. The prohibitively large state space underlying the decision policy motivates a neural network approximation, but prior work used only convolutional layers to extract spatial fire information from the current observations. Our network also incorporates a recurrent module to capture temporal information from the history of observations. Experiments involving two simulated fixed-wing aircraft feature a more realistic physics-based wildfire propagation model than the discrete wildfire models of prior work. Results show that our proposed technique uses about 20 times less memory than the approach of prior work, while performing comparably in terms of finding the fire's locations.

Locating the source of harmful, flammable, or polluting gas leaks is an important task in many practical scenarios. A recently proposed localization approach is to use a mobile robot equipped with a chemical sensor. The localization algorithm guides the movement of the robot based on the previous observations, with the objective of reaching the source as quickly as possible. In this paper, we propose an approach where the robot policy is represented by a neural network combining convolutional and LSTM layers. The approach relies on a gas dispersion model that takes into account obstacles, wind direction, and molecular movement. We found that the trained model provides a 47.34% higher success rate in finding the gas source than an existing greedy approach on test cases with unseen gas plumes and random obstacles.

The movement of a mobile sensor has a critical impact on the information gathered from the area of interest, as well as the quality of the estimate that a model can build from the collected information at any moment in time. Both systematic exploration models, which make the sensor move in regular patterns, and random movement models have specific advantages. There is less research concerning models that are positioned between these two extremes. In this paper, we propose Grid Limited Randomness (GLR), a family of path planning algorithms based on sampling waypoints from a grid of a specific resolution. We propose three variations differentiated by the order in which the mobile sensor visits these waypoints: new samples added to the end of the path (GLR-EOP), smallest detour (GLR-SD), and the shortest path as approximated by Christofides' algorithm. An extensive simulation study in the Waterberry Farms benchmark shows that the GLR variations offer benefits that, in specific circumstances, make them preferable to both fully random and fully systematic exploration paths.

In today's era of automation, mobile robots are being used for collecting meaningful information about an ambient phenomenon such as temperature or moisture distribution in an agricultural field. Most of the studies in the literature assume that the underlying information field is Gaussian, and therefore, Gaussian Process (GP)-based models are extremely popular. Furthermore, we have found that due to the inherent computational complexity of such naive GP-based techniques, most studies in the literature do not scale well beyond small-size environments, i.e., where the number of informative points$n < 1000$. These render such a predictive model more or less useless in many practical applications. In this paper, we posit that a different technique, Generative Adversarial Network-based inpainting, for robotic information gathering can be useful. The state-of-art inpainting techniques 1) do not assume that the underlying data is Gaussian, and 2) easily scale to$n\gg 1000$. Thus, they eliminate the two bottlenecks posed by the GP-based solutions. We have tested our hypothesis on a synthetic and a real-world crop dataset. Results show that while the inpainting technique easily scales to$1024\times 1024$, GP-based predictions cannot. On the other hand, their solution qualities are shown to be comparable.

Multi-robot teams are becoming an increasingly popular approach for information gathering in large geographic areas, with applications in precision agriculture, surveying the aftermath of natural disasters or tracking pollution. These robot teams are often assembled from untrusted devices not owned by the user, making the maintenance of the integrity of the collected samples an important challenge. Furthermore, such robots often operate under conditions of opportunistic, or periodic connectivity and are limited in their energy budget and computational power. In this paper, we propose algorithms that build on blockchain technology to address the data integrity problem, but also take into account the limitations of the robots' resources and communication. We evaluate the proposed algorithms along the perspective of the tradeoffs between data integrity, model accuracy, and time consumption.

Blockchain-powered smart systems deployed in different industrial applications promise operational efficiencies and improved yields, while significantly mitigating cybersecurity risks. Tradeoffs between availability and security arise at implementation, however, triggered by the additional resources (e.g., memory and computation) required by blockchain-enabled hosts. This paper applies an energy-reducing algorithmic engineering technique for Merkle Tree (MT) root calculations and the Proof of Work (PoW) algorithm, two principal elements of blockchain computations, as a means to preserve the promised security benefits but with less compromise to system availability. Using pyRAPL, a python library to measure the energy consumption of a computation, we experiment with both the standard and energy-reduced implementations of both algorithms for different input sizes. Our results show that up to 98% reduction in energy consumption is possible within the blockchain’s MT construction module, with the benefits typically increasing with larger input sizes. For the PoW algorithm, our results show up to 20% reduction in energy consumption, with the benefits being lower for higher difficulty levels. The proposed energy-reducing technique is also applicable to other key elements of blockchain computations, potentially affording even “greener” blockchain-powered systems than implied by only the results obtained thus far on the MT and PoW algorithms.

We study the problem of information sampling of an ambient phenomenon using a group of mobile robots. Autonomous robots are being deployed for various applications such as precision agriculture, search-and-rescue, among others. These robots are usually equipped with sensors and tasked with collecting maximal information for further data processing and decision making. The studied problem is proved to be NP-Hard in the literature. To solve the stated problem approximately, we employ a multi-agent deep reinforcement learning framework and use the concepts of mean field games to potentially scale the solution to larger multi-robot systems. Simulation results show that our presented technique easily scales to 10 robots in a 19 × 19 grid environment, while consistently sampling useful information.

Blockchain-powered smart systems deployed in different industrial applications promise operational efficiencies and improved yields, while mitigating significant cybersecurity risks pertaining to the main application. Associated tradeoffs between availability and security arise at implementation, however, triggered by the additional resources (e.g., memory, computation) required by each blockchain-enabled host. This paper applies an energy-reducing algorithmic engineering technique for Merkle Tree root calculations, a principal element of blockchain computations, as a means to preserve the promised security benefits but with less compromise to system availability. Using pyRAPL, a python library to measure computational energy, we experiment with both the standard and energy-reduced implementations of the Merkle Tree for different input sizes (in bytes). Our results show up to 98% reduction in energy consumption is possible within the blockchain's Merkle Tree construction module, such reductions typically increasing with larger input sizes. The proposed energy-reducing technique is similarly applicable to other key elements of blockchain computations, potentially affording even “greener” blockchain-powered systems than implied by only the Merkle Tree results obtained thus far.

To achieve coordination in a multi-robot system, the robots typically resort to some form of communication among each other. In most of the multi-robot coordination frameworks, high-level coordination strategies are studied but `how' the ground-level communication takes place, is assumed to be taken care of by another program. In this paper, we study the communication routing problem for large multi-robot systems where the robots have limited communication ranges. The objective is to send a message from a robot to another in the network, routed through a low number of other robots. To this end, we propose a communication model between any pair of robots using peer-to-peer radio communication. Our proposed model is generic to any type of message and guarantees a low hop routing between any pair of robots in this network. These help the robots to exchange large messages (e.g., multi-spectral images) in a short amount of time. Results show that our proposed approach easily scales up to 1000 robots while drastically reducing the space complexity for maintaining the network information.

We consider the problem of information collection from a polygonal environment using a multi-robot system, subject to continuous connectivity constraints. In particular, the robots, having a common radius of communication range, must remain connected throughout the exploration maximizing the information collection. The information gained through the exploration of the terrain is wirelessly transmitted to a base station. The base station performs the centralized planning of informative paths for the robots based on the information collected by them and thereafter, the robots follow these paths. This paper formulates the problem of multi-robot informative path planning under continuous connectivity constraints as an integer program leveraging the ideas of bipartite graph matching and minimal node separators. Theoretical analysis of the proposed solution proves that the informative paths will be collision-free and will be free of both livelock and deadlock. Experimental results demonstrate the low computational requirements of our algorithm for planning the informative paths, taking only about 0.75 sec. for planning a joint set of collision-free informative locations for 10 robots.

Consider a network environment with no central authority in which each node gains value when transacting with behaving nodes but risks losing value when transacting with misbehaving nodes. One recently proposed mechanism for curbing the harm by misbehaving nodes is that of an introduction-based reputation protocol [1]: transactions are permitted only between two nodes who consent to being connected through introduction via a third node. This paper models the main decision process in this protocol, namely that of continuing/closing an active connection, as a sequential detection problem in which each stage corresponds to a transaction that is (perhaps erroneously) classified as either benign or harmful. It is shown that the optimal decision takes the form of a reputation threshold policy, the exact threshold determined by a Bellman equation that admits a tractable iterative solution.

Given a directed graphical model with binary-valued hidden nodes and real-valued noisy observations, consider deciding upon the maximum a-posteriori (MAP) or the maximum posterior-marginal (MPM) assignment under the restriction that each node broadcasts only to its children exactly one single-bit message. We present a variational formulation, viewing the processing rules local to all nodes as degrees-of-freedom, that minimizes the loss in expected (MAP or MPM) performance subject to such online communication constraints. The approach leads to a novel message-passing algorithm to be executed offline, or before observations are realized, which mitigates the performance loss by iteratively coupling all rules in a manner implicitly driven by global statistics. We also provide (i) illustrative examples, (ii) assumptions that guarantee convergence and efficiency and (iii) connections to active research areas.

We address the problem of information system survivability, or dynamically preserving intended functionality & computational performance, in the face of malicious intrusive activity. A feedback control approach is proposed which enables tradeoffs between the failure cost of a compromised information system and the maintenance cost of ongoing defensive countermeasures. Online implementation features an inexpensive computation architecture consisting of a sensor-driven recursive estimator followed by an estimate-driven response selector. Offline design features a systematic empirical procedure utilizing a suite of mathematical modeling and numerical optimization tools. The engineering challenge is to generate domain models and decision strategies offline via tractable methods, while achieving online effectiveness. We illustrate the approach with experimentation results for a prototype autonomic defense system which protects its host, a Linux-based web-server, against an automated Internet worm attack. The overall approach applies to other types of computer attacks, network-level security and other domains which could benefit from automatic decision-making based on a sequence of sensor measurements.