A tethered marsupial robotics system comprises three components: an Unmanned Ground Vehicle (UGV), an Unmanned Aerial Vehicle (UAV), and a tether connecting both robots. Marsupial systems are highly beneficial in industry as they extend the UAV's battery life during flight. This paper introduces a novel strategy for a specific path planning problem in marsupial systems, where each of the three components must avoid collisions with ground and aerial obstacles modeled as 3D cuboids. Given an initial configuration in which the UAV is positioned atop the UGV, the goal is to reach an aerial target with the UAV. We assume that the UGV first moves to a position from which the UAV can take off and fly through a vertical plane to reach an aerial target. We propose an approach that discretizes the space to approximate an optimal solution, minimizing the sum of the lengths of the ground and air paths. First, we assume a taut tether and use a novel algorithm that leverages the convexity of the tether and the geometry of obstacles to efficiently determine the locus of feasible take-off points for the UAV. We then apply this result to scenarios that involve loose tethers. The simulation test results show that our approach can solve complex situations in seconds, outperforming a baseline planning algorithm based on RRT* (Rapidly exploring Random Trees).

We present a framework for Multi-Robot Task Allocation (MRTA) in heterogeneous teams performing long-endurance missions in dynamic scenarios. Given the limited battery of robots, especially for aerial vehicles, we allow for robot recharges and the possibility of fragmenting and/or relaying certain tasks. We also address tasks that must be performed by a coalition of robots in a coordinated manner. Given these features, we introduce a new class of heterogeneous MRTA problems which we analyze theoretically and optimally formulate as a Mixed Integer Linear Program (MILP). We then contribute a heuristic algorithm to compute approximate solutions and integrate it into a mission planning and execution architecture capable of reacting to unexpected events by repairing or recomputing plans online. Our experimental results show the relevance of our newly formulated problem in a realistic use case for inspection with aerial robots. We assess the performance of our heuristic solver in comparison with other variants and with exact optimal solutions in small-scale scenarios. In addition, we evaluate the ability of our replanning framework to repair plans online.

This paper presents a novel approach to optimal multi-robot task allocation in heterogeneous teams of robots. When robots have heterogeneous capabilities and there are diverse objectives and constraints to comply with, computing optimal plans can become especially hard. Moreover, we increase the problem complexity by: 1) considering battery-limited robots that need to schedule recharges; 2) tasks that can be decomposed into multiple fragments; and 3) multi-robot tasks that need to be executed by a coalition synchronously. We define a new problem for heterogeneous multi-robot task allocation and formulate it as a Mixed-Integer Linear Program that includes all the aforementioned features. Then we use an off-the-shelf solver to show the type of optimal solutions that our planner can produce and assess its performance in random scenarios. Our method, which is released as open-source code, represents a first step to formalize and analyze a complex problem that has not been solved in the state of the art.

Automatic inspection of parabolic-trough solar plants is key to preventing failures that can harm the environment and the production of green energy. In this work, we propose a novel methodology to inspect ball joints in parabolic trough collectors, which is a relevant problem that is not adequately covered in the literature. Images collected by an Unmanned Aerial Vehicle are segmented using deep learning to extract ball joint components. In order to generate rich training datasets, we develop a novel data augmentation technique by rotating joints and adding synthetic image background, and demonstrate its impact on the object detection accuracy. Then two types of faults are analyzed: fluid leaks, by means of image color filtering; and geometric shape anomalies, by measuring joint angles of the robotic arms. We propose metrics to quantify these faults and evaluate the damage of the inspected components. Our experimental results with images from operating commercial plants show that we can automatically detect leaks and anomalous angular geometry with a low failure rate compared to human labeling.

Navigation is an essential task for social robots. However, certain rules must be followed to allow them to move without causing distraction or discomfort to people. Considering that the context surrounding robots and persons affects the expected behavior, this work defines a social area around a person that adapts to the real situation. In addition, the social context of a person is extended to identify groups of people, which the robot should take into account while navigating. With this understanding of the surrounding of the robot together with the ability to predict the trajectory of individuals as well as groups, the proposed solution not only effectively addresses collision avoidance while promoting socially acceptable behavior but also outperforms the majority of recent works in terms of accuracy. Furthermore, a dedicated policy is introduced to react to social navigation conflicts. The evaluation performed in a simulated environment shows that the computation of our proposed solution is at least 8 times faster than the best state-of-the-art approach while preserving comparable social conduct. Also, the results of realistic experiments performed using Gazebo and a real robot are reported.

This paper proposes a planning algorithm for autonomous media production with multiple Unmanned Aerial Vehicles (UAVs) in outdoor events. Given filming tasks specified by a media Director, we formulate an optimization problem to maximize the filming time considering battery constraints. As we conjecture that the problem is NP-hard, we consider a discretization version, and propose a graph-based algorithm that can find an optimal solution of the discrete problem for a single UAV in polynomial time. Then, a greedy strategy is applied to solve the problem sequentially for multiple UAVs. We demonstrate that our algorithm is efficient for small teams (3-5 UAVs) and that its performance is close to the optimum. We showcase our system in field experiments carrying out actual media production in an outdoor scenario with multiple UAVs.

Stationary low-power wireless networks are able to continuously monitor large areas for long periods of time. If densely deployed, they can accurately detect intruders and localise them. However such density requires high installation and maintenance costs, and no identification of the intruders can be made. On the other side, mobile robots are able to precisely monitor areas and identify intruders when present in their field of view. Advances in multi-robot coordination allow them to cover large areas as patrolling guards. Nevertheless, they usually leave areas uncovered, and they can only work under battery-constrained time conditions. In this paper, we investigate the trade-offs in a practical hybrid surveillance system. A sparse low-power wireless network continuously monitors the environment and roughly estimates events locations, while robots resting on strategic positions wait for warning calls to survey specific areas and accurately observe the ongoing events. As a result, only a moderate accuracy is required from the stationary network, allowing a notable simplification with respect to similar localisation systems. At the same time the robotic team size can be decreased and its coordination simplified. The on-demand activation drastically saves the energy and wear required by the continuous patrolling. In this paper, we provide an experimental analysis of such trade-offs and we report on the lessons learned.

Target tracking with bearing-only sensors is a challenging problem when the target moves dynamically in complex scenarios. Besides the partial observability of such sensors, they have limited field of views, occlusions can occur, etc. In those cases, cooperative approaches with multiple tracking robots are interesting, but the different sources of uncertain information need to be considered appropriately in order to achieve better estimates. Even though there exist probabilistic filters that can estimate the position of a target dealing with uncertainties, bearing-only measurements bring usually additional problems with initialization and data association. In this paper, we propose a multi-robot triangulation method with a dynamic baseline that can triangulate bearing-only measurements in a probabilistic manner to produce 3D observations. This method is combined with a decentralized stochastic filter and used to tackle those initialization and data association issues. The approach is validated with simulations and field experiments where a team of aerial and ground robots with cameras track a dynamic target.

Planning under uncertainty faces a scalability problem when considering multi-robot teams, as the information space scales exponentially with the number of robots. To address this issue, this paper proposes to decentralize multiagent Partially Observable Markov Decision Process (POMDPs) while maintaining cooperation between robots by using POMDP policy auctions. Furthermore, communication models in the multiagent POMDP literature severely mismatch with real inter-robot communication. We address this issue by applying a decentralized data fusion method in order to efficiently maintain a joint belief state among the robots. The paper focuses on a cooperative tracking application, in which several robots have to jointly track a moving target of interest. The proposed ideas are illustrated in real multi-robot experiments, showcasing the flexible and robust cooperation that our techniques can provide.

This paper presents a decentralized data fusion approach to perform cooperative perception with data gathered from heterogeneous sensors, which can be static or carried by robots. Particularly, a Decentralized Delayed-State Extended Information Filter (DDSEIF) is described, where full state trajectories are considered to fuse the information. This permits to obtain an estimation equal to that obtained by a centralized system, and allows delays and latency in the communications. The sparseness of the information matrix maintains the communications overhead at a reasonable level. The method is applied to cooperative tracking and some results in disaster management scenarios are shown. In this kind of scenarios the target might move in both open field and indoor areas, so fusion of data provided by heterogeneous sensors is beneficial.