Multi-Robot Systems (MRS) are increasingly utilized in applications such as surveillance, environmental monitoring, and search and rescue, where maximizing mission rewards under budget constraints is critical. The Team Orienteering Problem (TOP) provides a framework for optimizing task coverage and resource allocation in such scenarios. However, traditional TOP formulations often overlook real-world constraints, such as limited communication ranges and the necessity of persistent connectivity among robots. These constraints are particularly relevant in environments like disaster zones and remote areas, where communication infrastructure is unreliable or absent. To address this gap, we propose a multi-objective formulation that balances task coverage, communication quality and energy expenditure under a fixed budget. Our approach accommodates teams of any size and heterogeneous vehicles with varying velocities and constant thrust. We validate our approach through extensive experiments across diverse scenarios and team configurations.

As robots become increasingly present in human environments, they must exhibit socially appropriate behavior, especially by respecting personal space, a psychological boundary influencing comfort based on proximity. While most existing models focus on 2D representations, the vertical dimension is often overlooked. We propose a novel three-dimensional personal space model that integrates horizontal proximity (XY-plane) with vertical sensitivity (Z-axis). The Z-axis discomfort function is derived using Maximum Permissible Pressure (MPP) to identify sensitive body regions and is converted into a continuous function via a fuzzy system. This is combined with a traditional planar discomfort model using a geometric mean to produce a complete 3D discomfort representation. To our knowledge, this is the first method capable of evaluating discomfort in 3D space at any robot component’s position, accounting for the person’s configuration and height. Our results underscore the importance of vertical modeling and demonstrate adaptability across different individuals heights.

Socially-aware robotic navigation is essential in environments where humans and robots coexist, ensuring both safety and comfort. However, most existing approaches have been primarily developed for mobile robots, leaving a significant gap in research that addresses the unique challenges posed by mobile manipulators. In this paper, we tackle the challenge of navigating a robotic mobile manipulator, carrying a non-negligible load, within a static human-populated environment while adhering to social norms. Our goal is to develop a method that enables the robot to simultaneously manipulate an object and navigate between locations in a socially-aware manner. We propose an approach based on the Risk-RRT* framework that enables the coordinated actuation of both the mobile base and manipulator. This approach ensures collision-free navigation while adhering to human social preferences. We compared our approach in a simulated environment to socially-aware mobile-only methods applied to a mobile manipulator. The results highlight the necessity for mobile manipulator-specific techniques, with our method outperforming mobile-only approaches. Our method enabled the robot to navigate, transport an object, avoid collisions, and minimize social discomfort effectively.

The navigation of mobile robots in social semi-structured environments, such as airports, poses significant challenges due to human presence and unpredictable crowd dynamics. Effective robot navigation requires not only collision avoidance but also adherence to socially acceptable movement patterns. Traditional path planning algorithms, such as Dijkstra and A*, are designed for static environments and do not account for social factors like human density or dynamic environmental changes, potentially leading to inefficient or unsafe navigation. This paper proposes an adaptive navigation framework that integrates social information into both global and local planning. The global planner constructs a medial axis-based navigation graph, dynamically adjusted according to a human density map, while the local planner employs the Elastic Band technique to refine trajectories in real time, responding to unforeseen obstacles. The proposed system was implemented in a simulated environment inspired by an airport and compared with a traditional, non-social planning method. The results showed superior adaptability, efficiency, and safety when navigating within human-centered environments.

Vehicles capable of operating in more than one environment have been developed to solve real problems. Among them, the hybrid unmanned aerial-underwater vehicle (HUAUV) is receiving attention from the robotics community, mainly with a quadrotor-like configuration. However, this vehicle presents high energy consumption because of the larger mass required compared to the only aerial vehicle, limiting its autonomy. This work addresses the trajectory planning problem for a HUAUV. The method is based on Rapidly-exploring Random Trees (RRTs), a highly customizable planning technique. In addition, we propose two new heuristics to increase the energy efficiency of the hybrid vehicle. The first consists of biasing the tree expansion towards the environment with the lowest navigation cost, while the second one assigns estimated costs to nodes in the tree and chooses the least expensive trajectories. These techniques are evaluated in physically realistic simulation experiments performed in 135 scenarios. A comparative analysis of their performances is presented relative to the state of the art. We show that using efficient heuristics can significantly contribute to reducing energy consumption and even increase the average velocity in the missions performed by these vehicles.

This paper introduces a novel methodology for the partitioning of the social space associated with arbitrary groups of individuals into approachable regions. The proposed method exploits boundary points within pre-identified social spaces and classifies them into three distinct levels based on the field of view between pairs of individuals within the group. The effectiveness of this approach was rigorously assessed across both static and dynamic scenarios, illustrating successful segmentation and classification of approachable regions. Furthermore, the methodology underwent comprehensive testing using publicly available datasets, yielding consistently satisfactory outcomes. This innovative approach represents a substantial contribution to the field of human-robot interaction, showcasing significant potential for future enhancements and advancements.

This article introduces the minimal exposure escape path (MEEP) problem, in which a mobile robot must determine a path from a start position to a safe region that less exposes it to any sort of industrial environmental risks. It is a generalization of the classic minimal exposure path (MEP) problem, where the exposure is associated with the detection by an industrial wireless sensor network (IWSN). The MEEP problem is applicable in many situations when it is necessary to escape from or penetrate predefined zones avoiding threats along the way, a common task in military operations, or search and rescue missions. We model it as an optimal control problem and use a semi-Lagrangian numerical scheme to solve it. We show that our approach asymptotically converges to the optimal solution while still allowing us to incorporate different characteristics and typical real-world constraints. Simulated experiments show the applicability of our method in 2-D and 3-D spaces, considering obstacle-free or cluttered environments, with nonconvex and multiple goal regions, and heterogeneous networks.

Advancements in mobile robotics have allowed humans and robots to interact in different environments and ways. A problem of great interest in Human-Robot Interaction is how to approach individuals, e.g., to gather information, in a socially acceptable manner. We present a new method for planning sequential visits to various groups of people in cluttered environments. The problem is formulated as a Set Orienteering Problem, where each group denotes a cluster with a set of possible approaching points considering different F-formations. We use the concept of a social probabilistic roadmap to determine safe paths between groups. Simulations considering different cases show that methodology produces efficient tours that maximize the number of approached individuals while respecting social norms of distance and a limited budget.

Autonomous Marine Vehicles (AMVs) have gained interest for scientific and commercial applications, including pipeline and algae bloom monitoring, contaminant tracking, and ocean debris removal. The Team Orienteering Problem (TOP) is relevant in this context as Multi-Robot Systems (MRSs) allow for better coverage of the area of interest, simultaneous data collection at different locations, and an increase in the overall robustness and efficiency of the mission. However, route planning for AMVs in dynamic ocean environments is challenging due to the coupling of environmental and vehicle dynamics. We propose a multi-objective formulation that accounts for the trade-offs between visiting multiple task locations and energy consumption by the vehicles subject to a time budget. This work focuses on vehicles that can maintain a constant net speed but can be adapted to vehicles with constant thrust. Different from existing approaches, our method is able to leverage time-varying ocean currents to improve the energy efficiency of resulting routes. We validate our approach experimentally by superimposing ocean flow models with benchmark instances of the TOP.

Modern societies are composed of complex structures that emerge from the relationships between individuals, and the comprehension of these arrangements has the potential to become a powerful tool for intelligent systems. Current image-based social relation recognition methods isolate specific information from the input to capture essential aspects defining these relationships. However, this is an inaccurate approach since the interaction between all these parts form an intricate structure, which is as valuable as the data each piece carries individually. Consequently, capturing this implicit social structure is essential to achieve the high-level reasoning required to identify relationships adequately. In this work, we propose a novel approach to interpret relationships based on three distinct scopes considering individual, relative, and general information. Additionally, it also takes into account prior knowledge and data dependencies between all these different social perspectives. The Social Knowledge Graph (SKG) is proposed based on these concepts, producing a representation capable of replicating the original social structure. This unique representation is exploited with the Social Graph Network (SGN) by employing specific feature aggregation strategies according to the information embedded into the graph. The performance of the proposed method was evaluated in well-known benchmarks for social relation recognition, achieving a new state-of-the-art. Finally, a deep analysis of the methodology and its main concepts is conducted, delivering results that support our interpretation of social relationships. • A new deep learning framework is designed to perform social relation recognition on images. • A novel conceptual organization describing the structure behind social relationships is proposed. • A new representation is introduced, called Social Knowledge Graph (SKG), which enhances the original structure. • The presented deep model performs relation recognition by exploiting this representation.

In many environmental monitoring applications robots are often tasked to visit various distinct locations to make observations and/or collect specific measurements. The problem of scheduling and assigning robots to the various tasks and planning feasible paths for the robots can be posed as an Orienteering Problem (OP). In the standard OP, routing and scheduling is achieved by maximizing an objective function by visiting the most rewarding locations while respecting a limited travel budget. However, traditional formulations for such problems usually neglect some environmental features that can greatly impact the tour, e.g., flows, such as wind or ocean currents. This is of particular importance for applications in marine and atmospheric environments where vehicle motions can be significantly impacted by the environmental dynamics and the environment exerts a non-negligible force on the vehicles. In this paper, we tackle the OP in fluid environments where robots must operate in the presence of ocean and/or atmospheric currents. We introduce a novel multi-objective formulation that combines both task and path planning problems, and whose goals are to (i) maximize the collected reward, while (ii) minimizing the energy expenditure by leveraging the environmental dynamics wherever possible. We validate our strategy using simulated ocean model data to show that our approach can generate a diverse set of solutions that have an adequate compromise between both objectives.

Despite the advances in autonomous navigation and motion planning, there are still several challenges to overcome, especially for confined or underground spaces. Confined scenarios present challenges such as lack of global or accurate external localization, uneven and slippery terrains, and multilevel stages. Exploring and mapping unknown unstructured environments is a fundamental step into the safe and efficient accomplishment of real-world tasks such as search and rescue missions or the autonomous inspection of dangerous areas. This paper proposes a novel three-dimensional autonomous exploration method for ground robots that considers the terrain traversability combined with the frontier expected information gain as a metric for the next best frontier selection in GPS-denied, confined spaces. Safe paths for navigation and frontier extraction are calculated iteratively from multiple 3D map representations such as octrees and meshes. Results in realistic simulated underground scenarios from the DARPA subterranean challenge demonstrate the technique's feasibility, achieving a more reliable and faster exploration rate over competing approaches.

The Correlated Team Orienteering Problem (CTOP) is a routing problem where the objective is to determine a set of routes that maximizes the summation of collected rewards in the environment while respecting the vehicles’ budget. However, solutions to this problem usually consider static instances and may produce poor results in dynamic real-world scenarios. In this paper, we propose an approach to deal with the execution of missions planned as a CTOP instance, especially when vehicles of the team are prone to failure and may not complete their routes. The main contribution of this paper is a novel anytime heuristic that iteratively adapts the initial set of routes, allowing to increase the overall robustness of the mission and still collect the most profitable rewards. The methodology was thoroughly evaluated considering different scenarios and in all the cases was able to achieve comparable or better results in terms of reward than planning a new set of routes, however, spending considerably less time.

In this paper, we propose an evolutionary algorithm for solving the multi-robot orienteering problem where a team of cooperative robots aims to maximize the total information collected by visiting a subset of given nodes within a fixed budget on travel costs. Multi-robot orienteering problems are relevant to applications such as logistic delivery services, precision agriculture, and environmental sampling and monitoring. We consider the case where the information gain at each node is related to the service time each robot spends at the node. As such, we address a variant of the Orienteering Problem where the collected rewards are a function of the time a robot spends at a given location. We present a genetic algorithm solver to this cooperative Team Orienteering Problem with service-time dependent rewards. We evaluate the approach over a diverse set of node configurations and for different team sizes. Lastly, we evaluate the effects of team heterogeneity on overall task performance through numerical simulations.

In this paper, we address the problem of finding cost-efficient three-dimensional paths that satisfy the maximum allowed curvature and the pitch angle of the vehicle. For any given initial and final configurations, the problem is decoupled into finding the horizontal and vertical parts of the path separately. Although the individual paths are modeled as two-dimensional Dubins curves using closed-form solutions, the final 3D path is constructed using the proposed local optimization to find a cost-efficient solution. Moreover, based on the decoupled approach, we provide a lower bound estimation of the optimal path that enables us to determine the quality of the found heuristic solution. The proposed solution has been evaluated using existing benchmark instances and compared with state-of-the-art approaches. Based on the reported results and lower bounds, the proposed approach provides paths close to the optimal solution while the computational requirements are in hundreds of microseconds. Besides, the proposed method provides paths with fewer turns than others, which make them easier to be followed by the vehicle's controller.

Multi-Robot Systems have been recently employed in different applications and have advantages over single-robot systems, such as increased robustness and task performance efficiency. We consider such assemblies specifically in the scenario of perimeter defense, where the task is to defend a circular perimeter by intercepting radially approaching targets. Possible intruders appear randomly at a fixed distance from the perimeter and with azimuthal location determined by some unknown probability density. Coordination among multiple defenders is a complex combinatorial optimization problem. In this work, we focus on the following two aspects: (i) estimating the probability density that describes the direction from which the next intruders are going to arrive, and (ii) partitioning of the space so that the defenders focus on capturing a disjoint subset of intruders. Results show that the proposed strategy increases the number of captures over a naive baseline strategy, especially in scenarios with non-uniform spatial distributions of intruder arrival. The proposed approach is also efficient and able to quickly adapt to time-varying intruder distributions.

We study strategies to deploy defenders/sensors to detect intruders that approach a targeted region. This scenario is formulated as a barrier coverage, which aims to minimize the number of unseen paths. The problem becomes challenging when the number of defenders is insufficient for a full coverage, requiring us to find the most effective location to deploy them. To this end, we use ideas from game theory to account for various paths that the intruders may take. Specifically, we propose an iterative algorithm to refine the set of candidate defender formations, which uses the payoff matrix to directly evaluate the utility of different formations. Given the set of candidate formations, a mixed Nash equilibrium gives a stochastic policy to deploy the defenders. The efficacy of the proposed strategy is demonstrated by a numerical analysis that compares our method with an existing graph-theoretic method.

Different applications in remote sensing, such as crop monitoring and visual surveillance, demand the automatic detection of changes from sets of images acquired over time. Most traditional approaches use satellite imagery, which, besides the known issues such as cloud cover and image acquisition frequency for nongeostationary satellites, are very costly. In this context, with the recent technological advances, unmanned aerial vehicles (UAVs) have become ubiquitous in numerous applications. In this letter, we present a fully convolutional Siamese autoencoder method for change detection in aerial images, in particular for those obtained with UAVs. We show that, by using an autoencoder, we can further reduce the number of labeled samples required to achieve competitive results. We evaluated the performance of our approach on two different data sets, and the results showed that our methodology outperforms the state of the art, while demanding less training data.

The increasing use of autonomous mobile robots in different parts of society, and not restricted only to industrial environments, makes it important to propose techniques that will allow them to behave in the most socially acceptable way as possible. In most real-world scenarios, individuals in the environment are interacting with each other and are arranged into groups. Therefore, it is paramount the proposition of techniques to efficiently and correctly identify and represent such groups. This information can be useful in different tasks such as approaching and initiating an interaction, escorting, and the navigation itself. In this work, we propose a novel graph-based approach to evaluate the possible association of individuals in the environment based on their position and body orientation. Next, based on this association, we propose a representation of the combined social space of individuals in the same group. The methodology was evaluated using synthetic and real-world datasets, showing that it achieves results comparable to or better than the state-of-the-art.

Considering the widespread use of mobile robots in different parts of society, it is important to provide them with the capability to behave in a socially acceptable manner. Therefore, a research topic of great importance recently has been the study of Human-Robot Interaction. Autonomous navigation is a fundamental task in Robotics, and several different strategies that produce paths that are either length or time optimized can be found in the literature. However, considering the recent use of mobile robots in a more social context, the use of such classical techniques is restricted. Therefore, in this article we present a social navigation approach considering environments with groups of people. The proposal uses a density function to efficiently represent groups of people, and modify the navigation architecture in order to include the social behaviour of the robot during its motion. This architecture is based on the combined use of the Probabilistic Road Mapping (PRM) and the Rapidly-exploring Random Tree (RRT) path planners and an adaptation of the elastic band algorithm. Experimental evaluation was carried out in different simulated environments, providing insight on the performance of the proposed technique, which surpasses classical techniques with no proxemics awareness in terms of social impact.

Path planning is a fundamental task for any kind of autonomous mobile robot. In this paper, we present a motion planning technique on the three-dimensional space considering vehicles with spatial curvature and pitch (climb or dive) angle constraints. Concerning real fixed-wing Unmanned Aerial Vehicles or underwater Remotely Operated Vehicles, we face the problem of calculating paths with continuous acceleration profiles, preventing the robot from abrupt turning or climbing/diving rates during its navigation. We also focus on the generation of paths with reduced length, allowing energy savings and minimizing the time for the task fulfillments. The proposed methodology provides, in a fast and iterative way, near-optimal paths in some scenarios, but with the advantage of continuous curvature and pitch rate profiles, a fundamental issue regarding the dynamics of real-world robots. The methodology was validated through numerous trials, under different scenarios in a simulated environment, and the results compared to the ones obtained by state-of-the-art techniques, providing a thorough evaluation.

The increasing use of mobile robots in social contexts makes it important to provide them with the ability to behave in the most socially acceptable way possible. In this paper we investigate the problem of making a robot learn how to approach a person in order to increase the chance of a successful engagement. We propose the use of Gaussian Process Regression (GPR), combined with ideas from reinforcement learning to make sure the space is properly and continuously explored. In the proposed example scenario, this is used by the robot to predict the best decisions in relation to its position in the environment and approach distance, each one accordingly to a certain time of the day. Numerical simulations show a significant performance improvement when compared with a random technique. The robot is able to improve performance after just one day of interaction (a few dozens of trials), and achieves the maximum expected value for the proposed approach within sixty days.

In this paper, we introduce the k-Dubins Traveling Salesman Problem with Neighborhoods (k-DTSPN), the problem of planning efficient paths among target regions for multiple robots with bounded curvature constraints (Dubins vehicles). This paper presents two approaches for the problem. Firstly, we present a heuristic that solves it in two steps, based on classical techniques found in the literature. Secondly, we employ a Memetic Algorithm to solve both combinatorial and continuous phases of the problem in a combined manner. We provide formal analysis about both proposed techniques, presenting upper bounds to the length of the longest tour. Numerous trials in simulated environments were executed, providing statistical examination of the final results.

A Wireless Sensor Network consists of several sensor nodes deployed in an environment having as primary goal to collect data. However, due to limited sensor communication range, oftentimes it is necessary to use a mobile node that will visit other nodes to gather up their collected data. This work addresses the problem of planning efficient paths for data collection by a mobile node modeled as a nonholonomic vehicle with curvature constraints. We propose an efficient algorithm to identify areas of intersection among the nodes RF footprints which will guide the identification of a smaller set of waypoints through which the vehicle needs to traverse in order to collect available data. Then, a metric similar to the classical Traveling Salesman Problem is used to determine the best circuit that includes all these collecting points. In order to reduce the total path length for the mobile node, a meta-heuristic is used. The classical Dubins' path technique is employed to generate a feasible tour for the vehicle and a new heuristic is used to generate the required orientation at the collecting points. The methodology was validated in a simulated environment. Our methodology outperforms the classical Alternating Algorithm and the best performing state-of-the-art algorithm.

In this work we propose an efficient and simple three-stage evolutionary algorithm to tackle the difficult problem of planning shorter paths through regions of an environment which are feasible for a nonholonomic vehicle with curvature constraints (e.g. Dubins' vehicle). Our method is able to efficiently solve both the combinatorial and the continuous steps of the problem in a combined manner. In the first phase, the method varies the position of the waypoints within the boundaries of each region, it then optimizes the path orientation at each waypoint, and finally it chooses the best actual sequence of visit. Numerous trials, under different scenarios in a simulated environment, were executed providing a thorough evaluation and validation of the methodology. The results show that a substantial improvement was obtained on the search for optimal paths in the DTSPN over current works in the literature. Numerical simulations also exhibit a significant performance improvement when compared with classical solutions that use the Alternating Algorithm, and they also show that our method outperforms a random sampling based technique. Our results present a reduction on the final path length of about 25% on average when compared to paths generated by the aforementioned methods.

Interest in telepresence robots is at an all time high, and several companies are already commercializing early or basic versions. There seems to be a huge potential for their use in professional applications, where they can help address some of the challenges companies have found in integrating a geographically distributed work force. However, teleoperation of these robots is typically a difficult task. This difficulty can be attributed to limitations on the information provided to the operator and to communication delay and failures. This may compromise the safety of the people and of the robot during its navigation through the environment. Most commercial systems currently control this risk by reducing size and weight of their robots. Research effort in addressing this problem is generally based on “assisted driving”, which typically adds a “collision avoidance” layer, limiting or avoiding movements that would lead to a collision. In this article, we bring assisted driving to a new level, by introducing concepts from collaborative driving to telepresence robots. More specifically, we use the input from the operator as a general guidance to the target direction, then couple that with a variable degree of autonomy to the robot, depending on the task and the environment. Previous work has shown collision avoidance makes operation easier and reduce the number of collisions. In addition (and in contrast to traditional collision avoidance systems), our approach also reduces the time required to complete a circuit, making navigation easier, safer, and faster. The methodology was evaluated through a controlled user study (N=18). Results show that the use of the proposed collaborative control helped reduce the number of collisions (none in most cases) and also decreased the time to complete the designated task.

In this paper we deal with the problem of path length optimization for nonholonomic robots modeled as Dubins' vehicles. We present an improved solution that computes paths through two-dimensional waypoints, that are shorter than those produced by one of the most used techniques in the literature. We initially present an optimization cost function that proves to be better suited for the nonholonomic constraints of the vehicles that are the focus of this work. We also propose an improvement for the Alternating Algorithm, used to determinate the orientation angles on the calculation of the Dubins' Path through the waypoints. Finally, we use a recently developed optimization meta-heuristic, called Continuous-GRASP (C-GRASP), to generate a path that is shorter than paths obtained with classical techniques. Our results show significant improvements on the search for optimal paths for the case of nonholonomic vehicles. Our methodology was thoroughly evaluated and validated in simulation, and the results have shown a decrease on path's length of 28% on average, compared with the classic technique found in literature. In some cases a reduction of approximately 50% was obtained.

This paper presents a methodology based on a variation of the Rapidly-exploring Random Trees (RRTs) that generates feasible trajectories for autonomous vehicles with holonomic constraints in environments with obstacles. Our approach is based on seventh order Bézier curves to connect vertexes of the tree, generating paths that do not violate the main kinematic constraints of the vehicle. The methodology also does not require complex kinematic and dynamic models of the vehicle. The smoothness of the acceleration profile of the entire path is directly guaranteed by controlling the curvature values at the extreme points of each Bézier that composes the tree. The proposed algorithm provides fast convergence to the final result with several other advantages, such as the reduction in the number of vertexes of the tree because the method enable connections between vertexes of the tree with unlimited range. In an environment with few obstacles, a very small quantity of vertexes (sometimes only two) is sufficient to take the robot between two points. The properties of the seventh order Bézier formulation are also used to avoid collisions with static obstacles in the environment.

This paper presents a methodology based on a variation of the Rapidly-exploring Random Trees (RRTs) that generates feasible trajectories for autonomous aerial vehicles with holonomic constraints in environments with obstacles. Our approach uses Pythagorean Hodograph (PH) curves to connect vertices of the tree, which makes it possible to generate paths for which the main kinematic constraints of the vehicle are not violated. The smoothness of the acceleration profile of the vehicle is indirectly guaranteed between two vertices of the RRT tree. The proposed algorithm provides fast convergence to the final trajectory. We still utilize the properties of the RRT to avoid collisions with static obstacles of a environment. We show results for a small unmanned aerial vehicles in environments with different configurations.