Point cloud-based object/place recognition remains a problem of interest in applications such as autonomous driving, scene reconstruction, and localization. Extracting a meaningful global descriptor from a query point cloud that can be matched with the descriptors of the database point clouds is a challenging problem. Furthermore, when the query point cloud is noisy or has been transformed (e.g., rotated), it adds to the complexity. To this end, we propose a novel methodology, named TopoRec, which utilizes Topological Data Analysis (TDA) for extracting local descriptors from a point cloud, thereby eliminating the need for resource-intensive GPU-based machine learning training. More specifically, we used the ATOL vectorization method to generate vectors for point clouds. To test the quality of the proposed TopoRec technique, we have implemented it on multiple real-world (e.g., Oxford RobotCar, NCLT) and realistic (e.g., ShapeNet) point cloud datasets for large-scale place and object recognition, respectively. Unlike existing learning-based approaches such as PointNetVLAD and PCAN, our method does not require extensive training, making it easily adaptable to new environments. Despite this, it consistently outperforms both state-of-the-art learning-based and handcrafted baselines (e.g., M2DP, ScanContext) on standard benchmark datasets, demonstrating superior accuracy and strong generalization.

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.

An exoplanet is a planet, which is not a part of our solar system. Whether life exists in one or more of these exoplanets has fascinated humans for centuries. NASA’s Kepler Space Telescope has discovered more than 70% of known exoplanets in our universe. However, manually determining whether a Kepler light curve indicates an exoplanet or not becomes infeasible with the large volume of data. Due to this, we propose a deep learning-based strategy to automatically classify a Kepler light curve. More specifically, we first convert the light curve time series into its corresponding Markov Transition Field (MTF) image and then classify it. Results show that the accuracy of the proposed technique is 99.39%, which is higher than all current state-of-the-art approaches.

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.

In this paper, we study the problem of shortest path planning for a mobile robot that does not possess unlimited energy for traveling. The robot can travel at most B distance with its battery fully charged. The objective of this energy-constrained robot is to travel from location S to location G in the presence of k charging stations while minimizing the incurred travel cost. As the robot is constrained by its energy, it needs to stop at one or more charging stations in order to recharge its battery. To solve the stated problem, we have proposed a variant of the classical A*search algorithm that plans the path of the robot in such a way that it moves as much distance as possible with a full recharge (the maximum being B). Furthermore, it chooses the one to be the next charging station that minimizes the extra distance that needs to be covered to reach it on top of the shortest distance from S to G. We have designed novel heuristic functions that guide the search towards such charging stations where the deviation from the shortest path without the constraint is the minimum. We prove that the proposed approach is complete. Results show that our proposed algorithm finds an optimal solution while taking a negligible time to execute.

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.

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.

In this paper, we study the NP-Hard problem of multi-robot coalition formation for task allocation. To tackle this notoriously difficult problem, we model it as a variant of classical bipartite matching, which we call One-To-Many Bipartite Matching (OTMaM). Unlike the classical bipartite matching techniques used for matching a unique robot to a unique task, in the OTMaM problem, we let multiple robots to be matched to a single task while restricting the opposite. To this end, we propose a novel heuristic algorithm that allocates robots to tasks by finding mutually best robot-task pairs. Our algorithm provides a similar theoretical worst-case approximation ratio and guarantees a better worst-case time complexity than a comparable algorithm from the literature. The proposed approach in this paper is proved to be deterministic and the resultant matching is perfect. Simulation results also demonstrate the scalability of the presented algorithm (taking less than 1 millisecond with 100 robots and 10 tasks).

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.

Tasks in the real world are complex in nature and often require multiple robots to collaborate in order to be accomplished. However, multiple robots with the same set of sensors working together might not be the optimal solution as one task might require different sensory inputs and actuation outputs. On the other hand, putting all types of sensors and/or actuators on a single robot is not a cost-effective solution. Therefore, multiple robots with different capabilities need to coordinate and form teams in order to accomplish such tasks. In this paper, we study the coalition formation problem for task allocation with multiple heterogeneous (equipped with different sets of sensors) robots. We use a hedonic coalition formation framework, rooted in game theory, to solve the mentioned problem. Our proposed algorithm aims to minimize the total cost of the formed coalitions and to maximize the matching between the required and the allocated types of robots to the tasks. Simulation results show that it produces near-optimal solutions in a negligible amount of time (0.19 ms. with 100 robots and 10 tasks).

The complexity of a vast number of real world tasks provides a great challenge for the currently available robots due to their limited capabilities. Thus, multiple robots would need to form coalitions for the completion of such tasks. In this paper, we examine the multi-robot coalition formation problem for task allocation where a group of robots needs to be allocated to a set of tasks. Our approach for this problem is to use a correlation clustering technique enabling similar robots to form coalitions. The algorithm presented in this paper is fast and scales better in comparison to two existing algorithms.

We propose a coordination mechanism to avoid inter-robot collisions when the robots' paths overlap with each other. Our proposed coordination technique uses a weighted bipartite matching-based formulation to solve this problem. Initially, each robot is given a unique goal location. But the robots do not know about other robots' planned paths until they come within each other's communication ranges. When two or more robots get within an unsafe distance, they coordinate their paths to avoid collisions with each other. The objective of the coordination mechanism is to plan a modified path for each coordinating robot (if needed) so that all the robots can reach their goal locations without collision with each other and also while reducing the extra distance introduced while resolving path conflicts. We have proved the correctness and convergence of our coordination strategy. Our experimental results show that the robots using our proposed strategy travel up to 4.2 times less than a comparable heuristic approach.

Modular self-reconfigurable robots (MSRs) are mostly used in environments where it is difficult to navigate and explore otherwise. Especially, the shape-changing ability of MSRs makes them more dexterous in these situations compared to fixed-body robots. But when the MSR forms a new configuration, usually, the locomotion pattern for that particular configuration is not known by its constituting robotic modules. The main challenge for modules is to learn how to move in that specific configuration within a reasonable amount of time. In this paper, we study the problem where an MSR needs to learn its movement pattern on-the-fly. To solve this problem, we have proposed a game theoretic solution based on multi-agent reinforcement learning using which the constituting modules distributedly learn the best actions that they need to perform to travel more distance in less time. We have implemented this approach in simulation on both ModRED and Yamor MSR platforms. Results show that our approach performs better (up to 7.86 times) in terms of average speed achieved for most of the tested configurations as compared to an existing locomotion learning approach.

We propose a lightweight and fast learning algorithm for classifying the features of an unknown terrain that a robot is navigating in. Most of the existing research on unknown terrain classification by mobile robots relies on a single powerful classifier to correctly identify the terrain using sensor data from a single sensor like laser or camera. In contrast, our proposed approach uses multiple modalities of sensed data and multiple, weak but less-complex classifiers for classifying the terrain types. The classifiers are combined using an ensemble learning algorithm to improve the algorithm's training rate as compared to an individual classifier. Our algorithm was tested with data collected by navigating a four-wheeled, autonomous robot, called Explorer, over different terrains including brick, grass, rock, sand, and concrete. Our results show that our proposed approach performs better with up to 63% better prediction accuracy for some terrains as compared to a support vector machine (SVM)-based learning technique that uses sensor data from a single sensor. Despite using multiple classifiers, our algorithm takes only a fraction (1/65) of the time on average, as compared to the SVM technique.

We consider the configuration formation problem in modular robotic systems where a set of singleton modules that are spatially distributed in an environment are required to assume appropriate positions so that they can configure into a new, user-specified target configuration, while simultaneously maximizing the amount of information collected while navigating from their initial to final positions. Each module has a limited energy budget to expend while moving from its initial to goal location. To solve this problem, we propose a budget-limited, heuristic search-based algorithm that finds a path that maximizes the entropy of the expected information along the path. We have analytically proved that our proposed approach converges within finite time. Experimental results show that our planning approach has lower run-time than an auction-based allocation algorithm for selecting modules' spots.

We study the problem of self-assembly in heterogeneous multi-agent systems where a set of agents initially randomly located in the environment need to form a user-specified pattern. The agents are heterogeneous in nature, i.e., each agent can only become the neighbor of a specific set of agents in the target pattern. To solve the self-assembly problem, we propose a novel technique, based on a constrained bipartite graph matching algorithm which allocates the agents to unique spots in the target pattern and moreover the allocation is done in such a way that any two allocated neighboring agents are compatible with each other. After the allocation is done, agents move to their unique allocated spots to assume their positions and form the target pattern. Experimental results show that our approach takes very nominal time to execute (in order of milliseconds for 100 agents).

We consider the problem of partitioning a set of modules for efficient and dynamic shape or configuration generation in modular robotic systems, where the size of any configuration formed by the modules is constrained by a maximum, user-provided value denoted by [Formula: see text]. The objective is to determine the best partition of the module set that gives the highest utility to the modules. This problem is non-trivial as the set of partitions that needs to be explored grows exponentially with the number of modules and an exhaustive search in the space of partitions makes the problem intractable. To address this problem, we propose a branch and bound based search algorithm, called bottomUpCSGSearch, which is able to intelligently prune the unpromising search space and find the best partition of the modules in a reasonable amount of time. We have provided analytical results related to the completeness, anytime nature and time complexity of our proposed algorithm. Our experimental results show that our proposed algorithm performs better in terms of number of nodes explored (up to [Formula: see text] times fewer nodes explored) and the time required to find the best partition (improvement up to the order of [Formula: see text]) than existing comparable algorithms. We have simulated with different number of modules and different values of maximum coalition size of a modular self-reconfigurable robot (MSR) called ModRED, and, shown that our algorithm takes nominal time to find the best solution.