Collaborative Planning for Catching and Transporting Objects in Unstructured Environments

Multi-robot teams have attracted attention from industry and academia for their ability to perform collaborative tasks in unstructured environments, such as wilderness rescue and collaborative transportation.In this paper, we propose a trajectory planning method for a non-holonomic robotic team with collaboration in unstructured environments.For the adaptive state collaboration of a robot team to catch and transport targets to be rescued using a net, we model the process of catching the falling target with a net in a continuous and differentiable form.This enables the robot team to fully exploit the kinematic potential, thereby adaptively catching the target in an appropriate state.Furthermore, the size safety and topological safety of the net, resulting from the collaborative support of the robots, are guaranteed through geometric constraints.We integrate our algorithm on a car-like robot team and test it in simulations and real-world experiments to validate our performance.Our method is compared to state-of-the-art multi-vehicle trajectory planning methods, demonstrating significant performance in efficiency and trajectory quality

Detection of bodies in maritime rescue operations using Unmanned Aerial Vehicles with multispectral cameras

In this study, we use unmanned aerial vehicles equipped with multispectral cameras to search for bodies in maritime rescue operations. A series of flights were performed in open‐water scenarios in the northwest of Spain, using a certified aquatic rescue dummy in dangerous areas and real people when the weather conditions allowed it. The multispectral images were aligned and used to train a convolutional neural network for body detection. An exhaustive evaluation was performed to assess the best combination of spectral channels for this task. Three approaches based on a MobileNet topology were evaluated, using (a) the full image, (b) a sliding window, and (c) a precise localization method. The first method classifies an input image as containing a body or not, the second uses a sliding window to yield a class for each subimage, and the third uses transposed convolutions returning a binary output in which the body pixels are marked. In all cases, the MobileNet architecture was modified by adding custom layers and preprocessing the input to align the multispectral camera channels. Evaluation shows that the proposed methods yield reliable results, obtaining the best classification performance when combining green, red‐edge, and near‐infrared channels. We conclude that the precise localization approach is the most suitable method, obtaining a similar accuracy as the sliding window but achieving a spatial localization close to 1 m. The presented system is about to be implemented for real maritime rescue operations carried out by Babcock Mission Critical Services Spain.This study was performed in collaboration with BabcockMCS Spain and funded by the Galicia Region Government through the Civil UAVs Initiative program, the Spanish Government’s Ministry of Economy, Industry, and Competitiveness through the RTC‐2014‐1863‐8 and INAER4‐14Y (IDI‐20141234) projects, and the grant number 730897 under the HPC‐EUROPA3 project supported by Horizon 2020

Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision

Search and rescue (SAR) operations can take significant advantage from supporting autonomous or teleoperated robots and multi-robot systems. These can aid in mapping and situational assessment, monitoring and surveillance, establishing communication networks, or searching for victims. This paper provides a review of multi-robot systems supporting SAR operations, with system-level considerations and focusing on the algorithmic perspectives for multi-robot coordination and perception. This is, to the best of our knowledge, the first survey paper to cover (i) heterogeneous SAR robots in different environments, (ii) active perception in multi-robot systems, while (iii) giving two complementary points of view from the multi-agent perception and control perspectives. We also discuss the most significant open research questions: shared autonomy, sim-to-real transferability of existing methods, awareness of victims' conditions, coordination and interoperability in heterogeneous multi-robot systems, and active perception. The different topics in the survey are put in the context of the different challenges and constraints that various types of robots (ground, aerial, surface, or underwater) encounter in different SAR environments (maritime, urban, wilderness, or other post-disaster scenarios). The objective of this survey is to serve as an entry point to the various aspects of multi-robot SAR systems to researchers in both the machine learning and control fields by giving a global overview of the main approaches being taken in the SAR robotics area

Model predictive altitude and velocity control in ergodic potential field directed multi-UAV search

This research addresses the challenge of executing multi-UAV survey missions over diverse terrains characterized by varying elevations. The approach integrates advanced two-dimensional ergodic search technique with model predictive control of UAV altitude and velocity. Optimization of altitude and velocity is performed along anticipated UAV ground routes, considering multiple objectives and constraints. This yields a flight regimen tailored to the terrain, as well as the motion and sensing characteristics of the UAVs. The proposed UAV motion control strategy is assessed through simulations of realistic search missions and actual terrain models. Results demonstrate the successful integration of model predictive altitude and velocity control with a two-dimensional potential field-guided ergodic search. Adjusting UAV altitudes to near-ideal levels facilitates the utilization of sensing ranges, thereby enhancing the effectiveness of the search. Furthermore, the control algorithm is capable of real-time computation, encouraging its practical application in real-world scenarios

Digital twin-enabled human-robot collaborative teaming towards sustainable and healthy built environments

Development of sustainable and healthy built environments (SHBE) is highly advocated to achieve collective societal good. Part of the pathway to SHBE is the engagement of robots to manage the ever-complex facilities for tasks such as inspection and disinfection. However, despite the increasing advancements of robot intelligence, it is still “mission impossible” for robots to independently undertake such open-ended problems as facility management, calling for a need to “team up” the robots with humans. Leveraging digital twin's ability to capture real-time data and inform decision-making via dynamic simulation, this study aims to develop a human-robot teaming framework for facility management to achieve sustainability and healthiness in the built environments. A digital twin-enabled prototype system is developed based on the framework. Case studies showed that the framework can safely and efficiently incorporate robotics into facility management tasks (e.g., patrolling, inspection, and cleaning) by allowing humans to plan, oversee, manage, and cooperate with the robot via the digital twin's bi-directional mechanism. The study lays out a high-level framework, under which purposeful efforts can be made to unlock digital twin's full potential in collaborating humans and robots in facility management towards SHBE

Graph-based Decentralized Task Allocation for Multi-Robot Target Localization

We introduce a new approach to address the task allocation problem in a system of heterogeneous robots comprising of Unmanned Ground Vehicles (UGVs) and Unmanned Aerial Vehicles (UAVs). The proposed model, \texttt{\method}, or \textbf{G}raph \textbf{A}ttention \textbf{T}ask \textbf{A}llocato\textbf{R} aggregates information from neighbors in the multi-robot system, with the aim of achieving joint optimality in the target localization efficiency.Being decentralized, our method is highly robust and adaptable to situations where collaborators may change over time, ensuring the continuity of the mission. We also proposed heterogeneity-aware preprocessing to let all the different types of robots collaborate with a uniform model.The experimental results demonstrate the effectiveness and scalability of the proposed approach in a range of simulated scenarios. The model can allocate targets' positions close to the expert algorithm's result, with a median spatial gap less than a unit length. This approach can be used in multi-robot systems deployed in search and rescue missions, environmental monitoring, and disaster response

Decentralized Autonomous Navigation Strategies for Multi-Robot Search and Rescue

In this report, we try to improve the performance of existing approaches for search operations in multi-robot context. We propose three novel algorithms that are using a triangular grid pattern, i.e., robots certainly go through the vertices of a triangular grid during the search procedure. The main advantage of using a triangular grid pattern is that it is asymptotically optimal in terms of the minimum number of robots required for the complete coverage of an arbitrary bounded area. We use a new topological map which is made and shared by robots during the search operation. We consider an area that is unknown to the robots a priori with an arbitrary shape, containing some obstacles. Unlike many current heuristic algorithms, we give mathematically proofs of convergence of the algorithms. The computer simulation results for the proposed algorithms are presented using a simulator of real robots and environment. We evaluate the performance of the algorithms via experiments with real robots. We compare the performance of our own algorithms with three existing algorithms from other researchers. The results demonstrate the merits of our proposed solution. A further study on formation building with obstacle avoidance for a team of mobile robots is presented in this report. We propose a decentralized formation building with obstacle avoidance algorithm for a group of mobile robots to move in a defined geometric configuration. Furthermore, we consider a more complicated formation problem with a group of anonymous robots; these robots are not aware of their position in the final configuration and need to reach a consensus during the formation process. We propose a randomized algorithm for the anonymous robots that achieves the convergence to a desired configuration with probability 1. We also propose a novel obstacle avoidance rule, used in the formation building algorithm.Comment: arXiv admin note: substantial text overlap with arXiv:1402.5188 by other author