Multi-robot Teams Research Articles

TIP: A trust inference and propagation model in multi-human multi-robot teams

Trust is a crucial factor for effective human–robot teaming. Existing literature on trust modeling predominantly focuses on dyadic human-autonomy teams where one human agent interacts with one robot. There is little, if not no, research on trust modeling in teams consisting of multiple human and robotic agents. To fill this important research gap, we present the Trust Inference and Propagation (TIP) model to model and estimate human trust in multi-human multi-robot teams. In a multi-human multi-robot team, we postulate that there exist two types of experiences that a human agent has with a robot: direct and indirect experiences. The TIP model presents a novel mathematical framework that explicitly accounts for both types of experiences. To evaluate the model, we conducted a human-subject experiment with 15 pairs of participants (N=30\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N=30$$\\end{document}). Each pair performed a search and detection task with two drones. Results show that our TIP model successfully captured the underlying trust dynamics and significantly outperformed a baseline model. To the best of our knowledge, the TIP model is the first mathematical framework for computational trust modeling in multi-human multi-robot teams.

On-line task allocation for multi-robot teams under dynamic scenarios

Multi-Robot Task Allocation (MRTA) is a complex problem domain with the majority of problem representations categorized as NP-hard. Existing solution approaches handling dynamic MRTA scenarios do not consider the problem structure changes as a possible system dynamic. RoSTAM (Robust and Self-adaptive Task Allocation for Multi-robot teams) presents a novel approach to handle a variety of MRTA problem representations without any alterations to the task allocation framework. RoSTAM’s capabilities against a range of MRTA problem distributions have already been established. This paper further validates RoSTAM’s performance against the more conventional dynamics, such as robot failure and new task arrival, while performing allocations against two of the most frequently faced problem representations. The framework’s performance is evaluated against a state-of-the-art online auction scheme. The results validate RoSTAM’s capability to allocate tasks across a range of dynamics efficiently.

Initial Task Allocation in Multi-Human Multi-Robot Teams: An Attention-Enhanced Hierarchical Reinforcement Learning Approach

Initial Task Allocation in Multi-Human Multi-Robot Teams: An Attention-Enhanced Hierarchical Reinforcement Learning Approach

Distributed Allocation and Scheduling of Tasks with Cross-Schedule Dependencies for Heterogeneous Multi-Robot Teams

Distributed Allocation and Scheduling of Tasks with Cross-Schedule Dependencies for Heterogeneous Multi-Robot Teams

Scalable, Pairwise Collaborations in Heterogeneous Multi-Robot Teams

Scalable, Pairwise Collaborations in Heterogeneous Multi-Robot Teams

Globally Optimal Assignment Algorithm for Collective Object Transport Using Air–Ground Multirobot Teams

Globally Optimal Assignment Algorithm for Collective Object Transport Using Air–Ground Multirobot Teams

End-to-end decentralized formation control using a graph neural network-based learning method.

Multi-robot cooperative control has been extensively studied using model-based distributed control methods. However, such control methods rely on sensing and perception modules in a sequential pipeline design, and the separation of perception and controls may cause processing latencies and compounding errors that affect control performance. End-to-end learning overcomes this limitation by implementing direct learning from onboard sensing data, with control commands output to the robots. Challenges exist in end-to-end learning for multi-robot cooperative control, and previous results are not scalable. We propose in this article a novel decentralized cooperative control method for multi-robot formations using deep neural networks, in which inter-robot communication is modeled by a graph neural network (GNN). Our method takes LiDAR sensor data as input, and the control policy is learned from demonstrations that are provided by an expert controller for decentralized formation control. Although it is trained with a fixed number of robots, the learned control policy is scalable. Evaluation in a robot simulator demonstrates the triangular formation behavior of multi-robot teams of different sizes under the learned control policy.

Sensing and Artificial Perception for Robots in Precision Forestry: A Survey

Artificial perception for robots operating in outdoor natural environments, including forest scenarios, has been the object of a substantial amount of research for decades. Regardless, this has proven to be one of the most difficult research areas in robotics and has yet to be robustly solved. This happens namely due to difficulties in dealing with environmental conditions (trees and relief, weather conditions, dust, smoke, etc.), the visual homogeneity of natural landscapes as opposed to the diversity of natural obstacles to be avoided, and the effect of vibrations or external forces such as wind, among other technical challenges. Consequently, we propose a new survey, describing the current state of the art in artificial perception and sensing for robots in precision forestry. Our goal is to provide a detailed literature review of the past few decades of active research in this field. With this review, we attempted to provide valuable insights into the current scientific outlook and identify necessary advancements in the area. We have found that the introduction of robotics in precision forestry imposes very significant scientific and technological problems in artificial sensing and perception, making this a particularly challenging field with an impact on economics, society, technology, and standards. Based on this analysis, we put forward a roadmap to address the outstanding challenges in its respective scientific and technological landscape, namely the lack of training data for perception models, open software frameworks, robust solutions for multi-robot teams, end-user involvement, use case scenarios, computational resource planning, management solutions to satisfy real-time operation constraints, and systematic field testing. We argue that following this roadmap will allow for robotics in precision forestry to fulfil its considerable potential.

Asymmetric Self-Play-Enabled Intelligent Heterogeneous Multirobot Catching System Using Deep Multiagent Reinforcement Learning

Aiming to develop a more robust and intelligent heterogeneous system for adversarial catching in security and rescue tasks, in this article, we discuss the specialities of applying asymmetric self-play and curriculum learning techniques to deal with the increasing heterogeneity and number of different robots in modern heterogeneous multirobot systems (HMRS). Our method, based on actor-critic multiagent reinforcement learning, provides a framework that can enable cooperative behaviors among heterogeneous multirobot teams. This leads to the development of an HMRS for complex catching scenarios that involve several robot teams and real-world constraints. We conduct simulated experiments to evaluate different mechanisms' influence on our method's performance, and real-world experiments to assess our system's performance in complex real-world catching problems. In addition, a bridging study is conducted to compare our method with a state-of-the-art method called S2M2 in heterogeneous catching problems, and our method performs better in adversarial settings. As a result, we show that the proposed framework, through fusing asymmetric self-play and curriculum learning during training, is able to successfully complete the HMRS catching task under realistic constraints in both simulation and the real world, thus providing a direction for future large-scale intelligent security & rescue HMRS.

Energy Efficient Routing Protocols for Mobile Robots: A Review

Many vital applications benefit from mobile multi-robot teams, including search and rescue, environmental monitoring, military, unmanned space exploration, hazard detection and analysis. Communication between robots is important for group coordination and information sharing in mobile multi-robot teams. Because many mobile robot applications involve scenarios where communication infrastructure is destroyed or unavailable, mobile robot teams must regularly connect with one another through ADHOC networking. In such situations, low over-head and energy efficient routing systems for sending information are essential. The most difficult task is to provide a unified foundation for robot communication. The framework of Mobile Robots is described in this paper, as well as a review and analysis of several research papers on Energy Efficient Routing in Robotic Wireless sensor networks (RWSN).

Source-seeking multi-robot team simulator as container of nature-inspired metaheuristic algorithms and Astar algorithm

One driving application for multi-robots is source seeking, especially in the hazardous environment. It consists of two essential subtasks: source location and path search. Nature inspired meta-heuristic is preferable in addressing the source location subtask which is an inversion problem, while the Astar algorithm and its variants are widely used for the path search subtask. In this paper, we present a multi-robot team simulator as a container which contains both algorithms as components. The simulator takes the constraints into consideration, including the size and the speed bound of each robot, the obstacle and collision avoidance. We provide a python implementation and example problems for research and test purposes. The well-structured code with object-oriented design can be conveniently upgraded by adding new excellent nature inspired metaheuristics, or extended to other source seeking problems in various field applications. The python code can be downloaded from the website: https://github.com/buctlab/source-seeking-multi-robot-team-simulator.

RLSS: real-time, decentralized, cooperative, networkless multi-robot trajectory planning using linear spatial separations

Trajectory planning for multiple robots in shared environments is a challenging problem especially when there is limited communication available or no central entity. In this article, we present Real-time planning using Linear Spatial Separations, or RLSS: a real-time decentralized trajectory planning algorithm for cooperative multi-robot teams in static environments. The algorithm requires relatively few robot capabilities, namely sensing the positions of robots and obstacles without higher-order derivatives and the ability of distinguishing robots from obstacles. There is no communication requirement and the robots’ dynamic limits are taken into account. RLSS generates and solves convex quadratic optimization problems that are kinematically feasible and guarantees collision avoidance if the resulting problems are feasible. We demonstrate the algorithm’s performance in real-time in simulations and on physical robots. We compare RLSS to two state-of-the-art planners and show empirically that RLSS does avoid deadlocks and collisions in forest-like and maze-like environments, significantly improving prior work, which result in collisions and deadlocks in such environments.

DARPA OFFSET: A Vision for Advanced Swarm Systems through Agile Technology Development and ExperimentationDARPA OFFSET: A Vision for Advanced Swarm Systems through Agile Technology Development and Experimentation

As robotics and autonomous technologies continue to see breakthrough innovations, specifically in the areas of large-scale multi-robot teams, their alignment with operationally relevant applications in fielded contexts has been necessary to both obtaining valuable user feedback as well as informing and refining the use cases themselves, i.e., co-evolving the concepts of operations alongside technology maturation. The Defense Advanced Research Projects Agency (DARPA) OFFensive Swarm-Enabled Tactics (OFFSET) program1 was a four-year program which actively embraced this iterative, mission-focused development approach, coupling technology innovation with field experimentation activities to rapidly advance capabilities for large-scale, heterogeneous robotic teams in complex and adversarial urban environments. This vision paper highlights the motivation for the OFFSET program; provides descriptions of the technical objectives and outcomes; and offers insights into the program structure designed to facilitate and inject technology innovations through an ambitious campaign of field tests and learning.

Agile Fixed-Wing UAVs for Urban Swarm Operations

Fixed-wing unmanned aerial vehicles (UAVs) offer significant performance advantages over rotary-wing UAVs in terms of speed, endurance, and efficiency. Such attributes make these vehicles ideally suited for long-range or high-speed reconnaissance operations and position them as valuable complementary members of a heterogeneous multi-robot team. However, these vehicles have traditionally been severely limited with regards to both vertical take-off and landing (VTOL) as well as maneuverability, which greatly restricts their utility in environments characterized by complex obstacle fields (e.g., forests or urban centers). This paper describes a set of algorithms and hardware advancements that enable agile fixed-wing UAVs to operate as members of a swarm in complex urban environments. At the core of our approach is a direct nonlinear model predictive control (NMPC) algorithm that is capable of controlling fixed-wing UAVs through aggressive post-stall maneuvers. We demonstrate in hardware how our online planning and control technique can enable navigation through tight corridors and in close proximity to obstacles.We also demonstrate how our approach can be combined with onboard stereo vision to enable high-speed flight in unknown environments. Finally, we describe our method for achieving swarm system integration; this includes a gimballed propeller design to facilitate automatic take-off, a precision deep-stall landing capability, multi-vehicle collision avoidance, and software integration with an existing swarm architecture.

Deep Reinforcement Learning for Decentralized Multi-Robot Exploration With Macro Actions

Cooperative multi-robot teams need to be able to explore cluttered and unstructured environments while dealing with communication dropouts that prevent them from exchanging local information to maintain team coordination. Therefore, robots need to consider high-level teammate intentions during action selection. In this letter, we present the first Macro Action Decentralized Exploration Network (MADE-Net) using multi-agent deep reinforcement learning (DRL) to address the challenges of communication dropouts during multi-robot exploration in unseen, unstructured, and cluttered environments. Simulated robot team exploration experiments were conducted and compared against classical and DRL methods where MADE-Net outperformed all benchmark methods in terms of computation time, total travel distance, number of local interactions between robots, and exploration rate across various degrees of communication dropouts. A scalability study in 3D environments showed a decrease in exploration time with MADE-Net with increasing team and environment sizes. The experiments presented highlight the effectiveness and robustness of our method.

Deep-Reinforcement-Learning-Based Multitarget Coverage With Connectivity Guaranteed

Deriving a distributed, time-efficient, and connectivity-guaranteed coverage policy in multitarget environment poses huge challenges for a multirobot team with limited coverage and limited communication. In particular, the robot team needs to cover multiple targets while preserving connectivity. In this article, a novel deep-reinforcement-learning-based approach is proposed to take both multitarget coverage and connectivity preservation into account simultaneously, which consists of four parts: a hierarchical observation attention representation, an interaction attention representation, a two-stage policy learning, and a connectivity-guaranteed policy filtering. The hierarchical observation attention representation is designed for each robot to extract the latent features of the relations from its neighboring robots and the targets. To promote the cooperation behavior among the robots, the interaction attention representation is designed for each robot to aggregate information from its neighboring robots. Moreover, to speed up the training process and improve the performance of the learned policy, the two-stage policy learning is presented using two reward functions based on algebraic connectivity and coverage rate. Furthermore, the learned policy is filtered to strictly guarantee the connectivity based on a model of connectivity maintenance. Finally, the effectiveness of the proposed method is validated by numerous simulations. Besides, our method is further deployed to an experimental platform based on quadrotor unmanned aerial vehicles and omnidirectional vehicles. The experiments illustrate the practicability of the proposed method.

Event-Triggered Pose Synchronization in SE(3) for Cooperating Multi-Robot Teams

Consensus and synchronization are fundamental concepts for the coordination of cooperating multi-robot teams. Applications like cooperative manipulation may require not only synchronization of the position, but also of the orientation of the individual agents. The pose of the agent can be described within the special Euclidean group and common results for coordination have to be adapted. We propose a control framework for full-pose synchronization in SE(3) for a team of Euler-Lagrange agents, relying only on relative information in the absence of a global coordinate frame. The framework consists of an inner loop for feedback linearization and an outer loop for pose synchronization. The measurements are taken by external sensors and communicated to the respective agents via a common communication network. To deal with limited communication bandwidth, we propose an event-triggered update strategy for the relative measurements. Finally, the efficacy of the proposed control and triggering law is illustrated in simulations.

A Multirobot Person Search System for Finding Multiple Dynamic Users in Human-Centered Environments.

Multirobot coordination for finding multiple users in an environment can be used in numerous robotic applications, including search and rescue, surveillance/monitoring, and activities of daily living assistance. Existing approaches have limited coordination between robots when generating team plans or do not consider user location probability within these plans. This results in long searches and robots potentially revisiting the same locations in succession. In this article, we present a novel multirobot person search system to generate search plans for multirobot teams to find multiple dynamic users before a deadline. Our approach is unique in that it simultaneously considers the search actions of all robots and user location probabilities when generating team plans, where user location probabilities are represented as conditional spatial-temporal probability density functions. We model this multirobot person search problem as a two-stage optimization problem to maximize the expected number of users found before the deadline. Stage 1 solves the action selection problem to determine a set of team actions, and the second stage solves the action allocation problem to distribute these actions amongst the robots. Namely, in stage 1, a novel conditional multiperiod multiknapsack problem is modeled as a min-flow graph solved sequentially by the Bellman-Ford shortest path algorithm. Stage 2 is a variant of the min-max multitraveling salesperson problem which models the environment topology as a search region network and search times selected by the previous stage. This stage is solved by a novel fuzzy clustering method. Numerous experiments comparing our proposed method to other existing approaches with varying environment sizes, search durations, and the number of users showed that our approach was able to find more target users before a defined deadline.

A Survey on the autonomous exploration of confined subterranean spaces: Perspectives from real-word and industrial robotic deployments

Confined and subterranean areas are common in many civilian and industrial sites, although they are hazardous for humans given the presence of noxious gases, extreme temperatures, narrow spaces, unhealthy oxygen levels, flooding, and collapsing structures. Therefore, exploration, routine inspections, and surveillance tasks can benefit from using autonomous mobile robots to improve safety by reducing the presence of humans in those scenarios. However, despite advances in the field, there are still challenges to overcome for confined and subterranean robot operation. Real word robotic exploration requires robust and reliable map generation, precise localization, safe navigation, and efficient path planning. These requirements make exploration in complex 3D environments with rugged terrain difficult. The challenge is increased when considering multi-robot teams, as there is no guarantee of a functional network infrastructure. Despite consistent increasing interest in the area, there is a lack of research summarizing the results and best practices for exploring such environments. Therefore, in this paper, we provide a review and discuss state-of-the-art robotic exploration techniques, including single and cooperative approaches with homogeneous and heterogeneous teams, with a focus on complex subterranean and confined 3D scenarios. We also present a comprehensive list of insights on open challenges and possible directions for future investigation in the topic.

LAMP 2.0: A Robust Multi-Robot SLAM System for Operation in Challenging Large-Scale Underground Environments

Search and rescue with a team of heterogeneous mobile robots in unknown and large-scale underground environments requires high-precision localization and mapping. This crucial requirement is faced with many challenges in complex and perceptually-degraded subterranean environments, as the onboard perception system is required to operate in off-nominal conditions (poor visibility due to darkness and dust, rugged and muddy terrain, and the presence of self-similar and ambiguous scenes). In a disaster response scenario and in the absence of prior information about the environment, robots must rely on noisy sensor data and perform Simultaneous Localization and Mapping (SLAM) to build a 3D map of the environment and localize themselves and potential survivors. To that end, this letter reports on a multi-robot SLAM system developed by team CoSTAR in the context of the DARPA Subterranean Challenge. We extend our previous work, LAMP, by incorporating a single-robot front-end interface that is adaptable to different odometry sources and lidar configurations, a scalable multi-robot front-end to support inter- and intra-robot loop closure detection for large scale environments and multi-robot teams, and a robust back-end equipped with an outlier-resilient pose graph optimization based on Graduated Non-Convexity. We provide a detailed ablation study on the multi-robot front-end and back-end, and assess the overall system performance in challenging real-world datasets collected across mines, power plants, and caves in the United States. We also release our multi-robot back-end datasets (and the corresponding ground truth), which can serve as challenging benchmarks for large-scale underground SLAM.