scholarly journals On foraging strategies for large-scale multi-robot systems

2006 ◽  
Author(s):  
Dylan Shell ◽  
Maja Mataric
Keyword(s):  
Large Scale ◽  
Foraging Strategies ◽  
Robot Systems ◽  
Multi Robot

Distributed Coordination Architecture for Cooperative Task Planning and Execution of Intelligent Multi-Robot Systems

2015 ◽  
pp. 407-426 ◽  
Author(s):  
Gen'ichi Yasuda
Keyword(s):  
Large Scale ◽  
Discrete Event ◽  
Robotic Systems ◽  
Task Planning ◽  
Distributed Coordination ◽  
Robot Systems ◽  
Concurrent Processes ◽  
Manufacturing Applications ◽  
Robot Cooperation ◽  
Multi Robot

This chapter provides a practical and intuitive way of cooperative task planning and execution for complex robotic systems using multiple robots in automated manufacturing applications. In large-scale complex robotic systems, because individual robots can autonomously execute their tasks, robotic activities are viewed as discrete event-driven asynchronous, concurrent processes. Further, since robotic activities are hierarchically defined, place/transition Petri nets can be properly used as specification tools on different levels of control abstraction. Net models representing inter-robot cooperation with synchronized interaction are presented to achieve distributed autonomous coordinated activities. An implementation of control software on hierarchical and distributed architecture is presented in an example multi-robot cell, where the higher level controller executes an activity-based global net model of task plan representing cooperative behaviors performed by the robots, and the parallel activities of the associated robots are synchronized without the coordinator through the transmission of requests and the reception of status.

Distributed multi-robot collision avoidance via deep reinforcement learning for navigation in complex scenarios

2020 ◽  
Vol 39 (7) ◽  
pp. 856-892 ◽  
Author(s):  
Tingxiang Fan ◽  
Pinxin Long ◽  
Wenxi Liu ◽  
Jia Pan
Keyword(s):  
Reinforcement Learning ◽  
Collision Avoidance ◽  
Autonomous Navigation ◽  
Large Scale ◽  
Learning Algorithm ◽  
Free Action ◽  
Parameter Tuning ◽  
Movement Velocity ◽  
Robot Systems ◽  
Multi Robot

Developing a safe and efficient collision-avoidance policy for multiple robots is challenging in the decentralized scenarios where each robot generates its paths with limited observation of other robots’ states and intentions. Prior distributed multi-robot collision-avoidance systems often require frequent inter-robot communication or agent-level features to plan a local collision-free action, which is not robust and computationally prohibitive. In addition, the performance of these methods is not comparable with their centralized counterparts in practice. In this article, we present a decentralized sensor-level collision-avoidance policy for multi-robot systems, which shows promising results in practical applications. In particular, our policy directly maps raw sensor measurements to an agent’s steering commands in terms of the movement velocity. As a first step toward reducing the performance gap between decentralized and centralized methods, we present a multi-scenario multi-stage training framework to learn an optimal policy. The policy is trained over a large number of robots in rich, complex environments simultaneously using a policy-gradient-based reinforcement-learning algorithm. The learning algorithm is also integrated into a hybrid control framework to further improve the policy’s robustness and effectiveness. We validate the learned sensor-level collision-3avoidance policy in a variety of simulated and real-world scenarios with thorough performance evaluations for large-scale multi-robot systems. The generalization of the learned policy is verified in a set of unseen scenarios including the navigation of a group of heterogeneous robots and a large-scale scenario with 100 robots. Although the policy is trained using simulation data only, we have successfully deployed it on physical robots with shapes and dynamics characteristics that are different from the simulated agents, in order to demonstrate the controller’s robustness against the simulation-to-real modeling error. Finally, we show that the collision-avoidance policy learned from multi-robot navigation tasks provides an excellent solution for safe and effective autonomous navigation for a single robot working in a dense real human crowd. Our learned policy enables a robot to make effective progress in a crowd without getting stuck. More importantly, the policy has been successfully deployed on different types of physical robot platforms without tedious parameter tuning. Videos are available at https://sites.google.com/view/hybridmrca .

scholarly journals An intern-sufficient cloud for large-scale multi-robot systems and its application in multitarget navigation

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110158
Author(s):  
Jingtao Zhang ◽  
Jun Zheng ◽  
Xiaodong Zhang ◽  
Kun Zhang ◽  
Pengjie Xu ◽  
...  
Keyword(s):  
Large Scale ◽  
Heterogeneous Computing ◽  
Signal To Noise Ratio ◽  
Scheduling Algorithm ◽  
Schedule Algorithm ◽  
Link Quality ◽  
Available Bandwidth ◽  
Robot Systems ◽  
Hierarchical Communication ◽  
Multi Robot

Due to generally limited computing capability of an individual robot, cloud-based robotic systems are increasingly used. However, applications in large-scale multi-robot systems will be hindered by communication congestion and consequent lack of computing resources. In this study, an intern-sufficient cloud is investigated to alleviate the burden of communication and thus support more robots. At the same time, it enables heterogeneously idle computing resources of robots inside the system to be shared on demand, instead of relying on cloud servers and communication infrastructures, to make the scope of application wider. To this end, a hierarchical communication mechanism and a resource schedule algorithm are proposed. In the mechanism, the transmission power, signal-to-noise ratio, available bandwidth, and other relevant features are taken into account to estimate link quality for data transmission. Then, the constrained communication conditions and heterogeneous computing resources are balanced by the resource scheduling algorithm, so that the most appropriate computing resources of the robots are contributed to the mobile cloud. Furthermore, a multitarget navigation task is applied on the cloud to validate the work. Thereby, simulations and experiments are performed. The results show that the proposed intern-sufficient cloud can provide stable resources of communication and computation for a multi-robot system with 20 physical robots while achieving more effective multitarget navigation.

scholarly journals STyLuS*: A Temporal Logic Optimal Control Synthesis Algorithm for Large-Scale Multi-Robot Systems

2020 ◽  
Vol 39 (7) ◽  
pp. 812-836
Author(s):  
Yiannis Kantaros ◽  
Michael M Zavlanos
Keyword(s):  
Optimal Control ◽  
Temporal Logic ◽  
Large Scale ◽  
Graph Search ◽  
Control Synthesis ◽  
Synthesis Methods ◽  
Synthesis Algorithm ◽  
Optimal Control Synthesis ◽  
Robot Systems ◽  
Multi Robot

This article proposes a new highly scalable and asymptotically optimal control synthesis algorithm from linear temporal logic specifications, called [Formula: see text] for large-Scale optimal Temporal Logic Synthesis, that is designed to solve complex temporal planning problems in large-scale multi-robot systems. Existing planning approaches with temporal logic specifications rely on graph search techniques applied to a product automaton constructed among the robots. In our previous work, we have proposed a more tractable sampling-based algorithm that builds incrementally trees that approximate the state space and transitions of the synchronous product automaton and does not require sophisticated graph search techniques. Here, we extend our previous work by introducing bias in the sampling process that is guided by transitions in the Büchi automaton that belong to the shortest path to the accepting states. This allows us to synthesize optimal motion plans from product automata with hundreds of orders of magnitude more states than those that existing optimal control synthesis methods or off-the-shelf model checkers can manipulate. We show that [Formula: see text] is probabilistically complete and asymptotically optimal and has exponential convergence rate. This is the first time that convergence rate results are provided for sampling-based optimal control synthesis methods. We provide simulation results that show that [Formula: see text] can synthesize optimal motion plans for very large multi-robot systems, which is impossible using state-of-the-art methods.

scholarly journals Distributed and collaborative monocular simultaneous localization and mapping for multi-robot systems in large-scale environments

2018 ◽  
Vol 15 (3) ◽  
pp. 172988141878017 ◽  
Author(s):  
Hui Zhang ◽  
Xieyuanli Chen ◽  
Huimin Lu ◽  
Junhao Xiao
Keyword(s):  
Large Scale ◽  
High Efficiency ◽  
Simultaneous Localization And Mapping ◽  
Small Scale ◽  
Data Set ◽  
Mapping Algorithm ◽  
Localization And Mapping ◽  
Mapping System ◽  
Robot Systems ◽  
Multi Robot

In this article, we propose a distributed and collaborative monocular simultaneous localization and mapping system for the multi-robot system in large-scale environments, where monocular vision is the only exteroceptive sensor. Each robot estimates its pose and reconstructs the environment simultaneously using the same monocular simultaneous localization and mapping algorithm. Meanwhile, they share the results of their incremental maps by streaming keyframes through the robot operating system messages and the wireless network. Subsequently, each robot in the group can obtain the global map with high efficiency. To build the collaborative simultaneous localization and mapping architecture, two novel approaches are proposed. One is a robust relocalization method based on active loop closure, and the other is a vision-based multi-robot relative pose estimating and map merging method. The former is used to solve the problem of tracking failures when robots carry out long-term monocular simultaneous localization and mapping in large-scale environments, while the latter uses the appearance-based place recognition method to determine multi-robot relative poses and build the large-scale global map by merging each robot’s local map. Both KITTI data set and our own data set acquired by a handheld camera are used to evaluate the proposed system. Experimental results show that the proposed distributed multi-robot collaborative monocular simultaneous localization and mapping system can be used in both indoor small-scale and outdoor large-scale environments.

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