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distributed Control of a Limited Angular Field-of-View Multi-robot System in Communication-Denied Scenarios: A Probabilistic Approach

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IEEE robotICS AND AUTOMATION LETTERS 2024年第1期9卷 739-746页

作者： Catellani, Mattia Sabattini, Lorenzo Univ Modena & Reggio Emilia Dept Sci & Methods Engn I-41121 Modena Italy

Multi-robot systems are gaining popularity over single-agent systems for their advantages. Although they have been studied in agriculture, search and rescue, surveillance, and environmental exploration, real-world implementation is limited due to agent coordination complexities caused by communication and sensor limitations. In this work, we propose a probabilistic approach to allow coordination among robots in communication-denied scenarios, where agents can only rely on visual information from a camera with a limited angular field-of-view. Our solution utilizes a particle filter to analyze uncertainty in the location of neighbors, together with Control Barrier Functions to address the exploration-exploitation dilemma that arises when robots must balance the mission goal with seeking information on undetected neighbors. This technique was tested with virtual robots required to complete a coverage mission, analyzing how the number of deployed robots affects performances and making a comparison with the ideal case of isotropic sensors and communication. Despite an increase in the amount of time required to fulfill the task, results have shown to be comparable to the ideal scenario in terms of final configuration achieved by the system.

关键词： Multi-robot systems distributed robot systems

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distributed Gossip-Triggered Control for robot Swarms With Limited Communication Range

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2023年第12期70卷 12511-12521页

作者： Zhang, Zhenwei Zhao, Xingwei Tao, Bo Ding, Han Huazhong Univ Sci & Technol Dept Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhanm 430074 Peoples R China

robot swarms usually work in uncertain and unstructured environments. It is challenging to control the robot swarm to accomplish multiple tasks in the shortest possible time when the task operation duration is unknown, and the communication network is time varying and possibly disconnected. To this end, a new distributed gossip-triggered control (DGTC) method is proposed. robots mimic human gossip behavior to disseminate and aggregate local information while triggering decisions to reduce task conflicts and system costs. The swarm dynamics, including discrete decisions and continuous trajectory, are formulated as a giant switched system. On this basis, the system stability and convergence of the DGTC are proved, and a criterion to balance the convergence rate and communication burden is given. More importantly, robot swarms can balance the workload between individuals by self-regulating the number of active agents to match the remaining tasks, thus ensuring a tight schedule without any time knowledge. Simulation results demonstrate the advantages of DGTC in generating tight schedules, and experiments conducted with Mecanum cars validate the effectiveness in dealing with time uncertainties.

关键词： Coordination and scheduling distributed robot systems gossip-triggered control

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Online Trajectory Generation With distributed Model Predictive Control for Multi-robot Motion Planning

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IEEE robotICS AND AUTOMATION LETTERS 2020年第2期5卷 604-611页

作者： Luis, Carlos E. Vukosavljev, Marijan Schoellig, Angela P. Univ Toronto Inst Aerosp Studies Dynam Syst Lab Toronto ON Canada

We present a distributed model predictive control (DMPC) algorithm to generate trajectories in real-time for multiple robots. We adopted the on-demand collision avoidance method presented in previous work to efficiently compute non-colliding trajectories in transition tasks. An event-triggered replanning strategy is proposed to account for disturbances. Our simulation results show that the proposed collision avoidance method can reduce, on average, around 50 of the travel time required to complete a multi-agent point-to-point transition when compared to the well-studied Buffered Voronoi Cells (BVC) approach. Additionally, it shows a higher success rate in transition tasks with a high density of agents, with more than 90 success rate with 30 palm-sized quadrotor agents in a 18$\text{m}<^>3$ arena. The approach was experimentally validated with a swarm of up to 20 drones flying in close proximity.

关键词： Motion and path planning distributed robot systems collision avoidance model predictive control

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Multi-robot Active Sensing and Environmental Model Learning With distributed Gaussian Process

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IEEE robotICS AND AUTOMATION LETTERS 2020年第4期5卷 5905-5912页

作者： Jang, Dohyun Yoo, Jaehyun Son, Clark Youngdong Kim, Dabin Kim, H. Jin Seoul Natl Univ SNU Dept Mech & Aerosp Engn Seoul 08826 South Korea Automat & Syst Res Inst ASRI Seoul 08826 South Korea Hankyong Natl Univ Dept Elect Elect & Control Engn Anseong 10044 South Korea

This letter deals with the problem of multiple robots working together to explore and gather at the global maximum of the unknown field. Given noisy sensor measurements obtained at the location of robots with no prior knowledge about the environmental map, Gaussian process regression can be an efficient solution to construct a map that represents spatial information with confidence intervals. However, because the conventional Gaussian process algorithm operates in a centralized manner, it is difficult to process information coming from multiple distributed sensors in real-time. In this work, we propose a multi-robot exploration algorithm that deals with the following challenges: i) distributed environmental map construction using networked sensing platforms;ii) online learning using successive measurements suitable for a multi-robot team;iii) multi-agent coordination to discover the highest peak of an unknown environmental field with collision avoidance. We demonstrate the effectiveness of our algorithm via simulation and a topographic survey experiment with multiple UAVs.

关键词： Multi-robot systems distributed robot systems networked robots

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A distributed, plug-n-play algorithm for multi-robot applications with a priori non-computable objective functions

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INTERNATIONAL JOURNAL OF robotICS RESEARCH 2019年第7期38卷 813-832页

作者： Kapoutsis, Athanasios Ch Chatzichristofis, Savvas A. Kosmatopoulos, Elias B. Democritus Univ Thrace Dept Elect & Comp Engn GR-67100 Xanthi Greece Ctr Res & Technol Informat Technol Inst Thessaloniki Greece Neapolis Univ Dept Comp Sci Paphos Cyprus

This paper presents a distributed algorithm applicable to a wide range of practical multi-robot applications. In such multi-robot applications, the user-defined objectives of the mission can be cast as a general optimization problem, without explicit guidelines of the subtasks per different robot. Owing to the unknown environment, unknown robot dynamics, sensor nonlinearities, etc., the analytic form of the optimization cost function is not available a priori. Therefore, standard gradient-descent-like algorithms are not applicable to these problems. To tackle this, we introduce a new algorithm that carefully designs each robot's subcost function, the optimization of which can accomplish the overall team objective. Upon this transformation, we propose a distributed methodology based on the cognitive-based adaptive optimization (CAO) algorithm, that is able to approximate the evolution of each robot's cost function and to adequately optimize its decision variables (robot actions). The latter can be achieved by online learning only the problem-specific characteristics that affect the accomplishment of mission objectives. The overall, low-complexity algorithm can straightforwardly incorporate any kind of operational constraint, is fault tolerant, and can appropriately tackle time-varying cost functions. A cornerstone of this approach is that it shares the same convergence characteristics as those of block coordinate descent algorithms. The proposed algorithm is evaluated in three heterogeneous simulation set-ups under multiple scenarios, against both general-purpose and problem-specific algorithms.

关键词： distributed robot systems learning and adaptive systems cognitive robotics surveillance systems autonomous agents

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distributed Voronoi Neighbor Identification From Inter-robot Distances

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IEEE robotICS AND AUTOMATION LETTERS 2017年第3期2卷 1320-1327页

作者： Elwin, Matthew L. Freeman, Randy A. Lynch, Kevin M. Northwestern Univ McCormick Sch Engn Evanston IL 60208 USA

Algorithms for identifying Voronoi neighbors and constructing Voronoi regions are useful in many distributed robotics applications. Existing methods that perform these tasks using only the distances between robots assign coordinates to each potential neighbor before applying another algorithm to find the Voronoi neighbors. Our method finds the Voronoi neighbors more efficiently;identification occurs directly from inter-robot distances, without first assigning coordinates. We prove the algorithm's correctness, analyze its computational complexity, and demonstrate its effectiveness in the presence of noise via simulation with an experimentally validated sensor model.

关键词： Computational geometry distributed robot systems sensor networks

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An Asymmetric distributed Method for Sorting a robot Swarm

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IEEE robotICS AND AUTOMATION LETTERS 2017年第1期2卷 261-268页

作者： Zhou, Yu Goldman, Ron McLurkin, James Rice Univ Dept Comp Sci Houston TX 77005 USA

We consider the problem of sorting a swarm of labeled robots in Euclidean space. Our goal is to organize the robots into a sorted and equally-spaced straight line between the robots with lowest and highest labels, while maintaining a connected communication network. We break the symmetry between the minimum and maximum, in order to keep time, travel distance, and communication costs low, without using central control. We run in parallel a set of functions, including leader election, tree formation, path formation, path modification, and geometric straightening. We show that our algorithm is safe, correct, efficient, and robust to changes in population and connectivity. We validate our theoretical results with simulations. Our implementation uses messages of fixed size and robots of constant memory, and is a practical solution for large populations of low-cost robots.

关键词： distributed robot systems distributed algorithm path planning for multiple mobile robots or agents physical sorting swarms

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DiSCo-SLAM: distributed Scan Context-Enabled Multi-robot LiDAR SLAM With Two-Stage Global-Local Graph Optimization

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IEEE robotICS AND AUTOMATION LETTERS 2022年第2期7卷 1150-1157页

作者： Huang, Yewei Shan, Tixiao Chen, Fanfei Englot, Brendan Stevens Inst Technol Hoboken NJ 07030 USA SRI Int Princeton NJ 08540 USA Exyn Technol Philadelphia PA 19146 USA

We propose a novel framework for distributed,multi-robot SLAM intended for use with 3D LiDAR observations. The framework, DiSCo-SLAM, is the first to use the lightweight Scan Context descriptor for multi-robot SLAM, permitting a data-efficient exchange of LiDAR observations among robots. Additionally, our framework includes a two-stage global and local optimization framework for distributed multi-robot SLAM which provides stable localization results that are resilient to the unknown initial conditions that typify the search for inter-robot loop closures. We compare our proposed framework with the widely used distributed Gauss-Seidel (DGS) approach, over a variety of multi-robot datasets, quantitatively demonstrating its accuracy, stability, and data-efficiency.

关键词： Multi-robot SLAM distributed robot systems range sensing

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DiNNO: distributed Neural Network Optimization for Multi-robot Collaborative Learning

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IEEE robotICS AND AUTOMATION LETTERS 2022年第2期7卷 1896-1903页

作者： Yu, Javier Vincent, Joseph A. Schwager, Mac Stanford Univ Dept Aeronaut & Astronaut Stanford CA 94305 USA

We present DiNNO, a distributed algorithm that enables a group of robots to collaboratively optimize a deep neural network model while communicating over a mesh network. Each robot only has access to its own data and maintains its own version of the neural network, but eventually learns a model that is as good as if it had been trained on all the data centrally. No robot sends raw data over the wireless network, preserving data privacy and ensuring efficient use of wireless bandwidth. At each iteration, each robot approximately optimizes an augmented Lagrangian function, then communicates the resulting weights to its neighbors, updates dual variables, and repeats. Eventually, all robots' local model weights reach a consensus. For convex objective functions, this consensus is a global optimum. Unlike many existing methods we test our algorithm on robotics-related, deep learning tasks with nontrivial model architectures. We compare DiNNO to two benchmark distributed deep learning algorithms in (i) an MNIST image classification task, (ii) a multi-robot implicit mapping task, and (iii) a multi-robot reinforcement learning task. In these experiments we show that DiNNO performs well when faced with nonconvex deep learning objectives, time-varying communication graphs, and streaming data. In all experiments our method outperforms baselines, and was able to achieve validation loss equivalent to centrally trained models. See ***/projects/dist_nn_train for videos and code.

关键词： Deep learning methods distributed robot systems multi-robot systems

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Multi-robot Pickup and Delivery via distributed Resource Allocation

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IEEE TRANSACTIONS ON robotICS 2023年第2期39卷 1106-1118页

作者： Camisa, Andrea Testa, Andrea Notarstefano, Giuseppe Univ Bologna Dept Elect Elect & Informat Engn I-40126 Bologna Italy

In this paper, we consider a large-scale instance of the classical Pickup-and-Delivery Vehicle Routing Problem (PDVRP) that must be solved by a network of mobile cooperating robots. robots must self-coordinate and self-allocate a set of pickup/delivery tasks while minimizing a given cost figure. This results in a large, challenging Mixed-Integer Linear Problem that must be cooperatively solved without a central coordinator. We propose a distributed algorithm based on a primal decomposition approach that provides a feasible solution to the problem in finite time. An interesting feature of the proposed scheme is that each robot computes only its portion of solution, thereby preserving privacy of sensible information. The algorithm also exhibits attractive scalability properties that guarantee solvability of the problem even in large networks. To the best of our knowledge, this is the first attempt to provide a scalable distributed solution to the problem. The algorithm is first tested through Gazebo simulations on a ROS 2 platform, highlighting the effectiveness of the proposed solution. Finally, experiments on a real testbed with a team of ground and aerial robots are provided.

关键词： Cooperating robots distributed optimization distributed robot systems planning scheduling and coordination Cooperating robots distributed optimization distributed robot systems Cooperating robots planning distributed optimization distributed optimization distributed optimization distributed optimization distributed optimization distributed optimization scheduling and coordination scheduling and coordination distributed robot systems planning scheduling and coordination

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