Applying a path planner based on RRT to cooperative multirobot box-pushing

Considering robot systems in the real world, a multirobot system where multiple robots work simultaneously without colliding with each other is more practical than a single-robot system where only one robot works. Therefore, solving the path-planning problem in a multirobot system is very important. In this study, we developed a path-planner based on the rapidly exploring random tree (RRT), which is a data structure and algorithm designed for efficiently searching for multirobot box-pushing, and made experiments in real environments. A path planner must construct a plan which avoids the robot colliding with obstacles or with other robots. Moreover, in some cases, a robot must collaborate with other robots to transport the box without colliding with any obstacles. Our proposed path planner constructs a box-transportation plan and the path plans of each robot bearing the above considerations in mind. Experimental results showed that our proposed planner can construct a multirobot box-pushing plan without colliding with obstacles, and that the robots can execute tasks according to the plan in real environments. We also checked that multiple robots can perform problem tasks when only one robot could not transport the box to the goal. This work was presented in part at the 13th International Symposium on Articifial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Leader-following formation of switching multirobot systems via internal model

In this paper, the leader-following formation problem of multirobot systems with switching interconnection topologies is considered. The robots are required to move in a formation with formation constrains described in terms of relative distances of the robots and the formation (as whole entity) is required to track the trajectory generated by an exosystem. The exosystem of the considered multirobot systems provides driving forces or environmental disturbance, whose dynamics is different from the dynamics of the robots. A systematic distributed design approach for the leader-following formation problem is proposed via dynamic output feedback with the help of canonical internal model.     

A Neural Network Approach to Dynamic Task Assignment of Multirobots

In this paper, a neural network approach to task assignment, based on a self-organizing map (SOM), is proposed for a multirobot system in dynamic environments subject to uncertainties. It is capable of dynamically controlling a group of mobile robots to achieve multiple tasks at different locations, so that the desired number of robots will arrive at every target location from arbitrary initial locations. In the proposed approach, the robot motion planning is integrated with the task assignment, thus the robots start to move once the overall task is given. The robot navigation can be dynamically adjusted to guarantee that each target location has the desired number of robots, even under uncertainties such as when some robots break down. The proposed approach is capable of dealing with changing environments. The effectiveness and efficiency of the proposed approach are demonstrated by simulation studies.     

Fuzzy sliding-mode formation control for multirobot systems: design and implementation

This paper mainly addresses the decentralized formation problems for multiple robots, where a fuzzy sliding-mode formation controller (FSMFC) is proposed. The directed networks of dynamic agents with external disturbances and system uncertainties are discussed in consensus problems. To perform a formation control and to guarantee system robustness, a novel formation algorithm combining the concepts of graph theory and fuzzy sliding-model control is presented. According to the communication topology, formation stability conditions can be determined so that an FSMFC can be derived. By Lyapunov stability theorem, not only the system stability can be guaranteed, but the desired formation pattern of a multirobot system can be also achieved. Simulation results are provided to demonstrate the effectiveness of the provided control scheme. Finally, an experimental setup for the e-puck multirobot system is built. Compared to first-order formation algorithm and fuzzy neural network formation algorithm, it shows that real-time experimental results empirically support the promising performance of desire.     

Combining GRN modeling and demonstration-based programming for robot control

In recent years, gene regulatory networks (GRNs) have been proposed to work as reliable and robust control mechanisms for robots. Because recurrent neural networks (RNNs) have the unique characteristic of presenting system dynamics over time, we thus adopt such kind of network structure and the principles of gene regulation to develop a biologically and computationally plausible GRN model for robot control. To simulate the regulatory effects and to make our model inferable from time-series data, we also implement an enhanced network-learning algorithm to derive network parameters efficiently. In addition, we present a procedure of programming-by-demonstration to collect behavior sequence data of the robot as expression profiles, and then employ our network-modeling framework to infer controllers. To verify the proposed approach, experiments have been conducted, and the results show that our regulatory model can be inferred for robot control successfully.     

Adaptive Synchronization-Based Approach for Finite-Time Parameters Identification of Genetic Regulatory Networks

Neural Processing Letters - This paper presents an approach to identify the unknown parameters of genetic regulatory network (GRN) in finite-time. The adaptive synchronization-based method is used...     

A bounded controller for multirobot navigation while maintaining network connectivity in the presence of obstacles

Maintaining the connectivity of networked robots is a challenge in multirobot applications. In this paper, this challenging problem is addressed through the development of a novel controller that can guarantee that robots will approach their individual desired positions while maintaining existing network topology and avoiding obstacles. A new concept of connectivity constraint, along with a continuous modeling approach to obstacle avoidance, is utilized in building the navigation function. The designed potential field integrates the navigation requirement, connectivity constraint, and obstacle avoidance simultaneously, based on which a bounded control input is generated for multirobot control. It is shown that if the initial configurations of the robots are connected and the desired configuration is reachable, the proposed controller is capable of driving multirobots to their individual goal positions conditionally while keeping the underlying network connected. Simulations and experiments are finally performed using a group of mobile robots to demonstrate the effectiveness of the proposed controller.     

动态环境下分布式异构多机器人避障方法研究

多机器人系统在联合搜救、智慧车间、智能交通等领域得到了日益广泛的应用。目前,多个机器人之间、机器人与动态环境之间的路径规划和导航避障仍需依赖精确的环境地图,给多机器人系统在非结构环境下的协调与协作带来了挑战。针对上述问题,本文提出了不依赖精确地图的分布式异构多机器人导航避障方法,建立了基于深度强化学习的多特征策略梯度优化算法,并考虑了人机协同环境下的社会范式,使分布式机器人能够通过与环境的试错交互,学习最优的导航避障策略;并在Gazebo仿真环境下进行了最优策略的训练学习,同时将模型移植到多个异构实体机器人上,将机器人控制信号解码,进行真实环境测试。实验结果表明:本文提出的多特征策略梯度优化算法能够通过自学习获得最优的导航避障策略,为分布式异构多机器人在动态环境下的应用提供了一种技术参考。     

Robot Teams for Intervention in Confined and Structured Environments

Safety, security, and rescue robotics can be extremely useful in emergency scenarios such as mining accidents or tunnel collapses where robot teams can be used to carry out cooperative exploration, intervention, or logistic missions. Deploying a multirobot team in such confined environments poses multiple challenges that involve task planning, motion planning, localization and mapping, safe navigation, coordination, and communications among all the robots. To complete their mission, robots have to be able to move in the environment with full autonomy while at the same time maintaining communication among themselves and with their human operators to accomplish team collaboration. Guaranteeing connectivity enables robots to explicitly exchange information needed in the execution of collaborative tasks and allows operators to monitor and teleoperate the robots and receive information about the environment. In this work, we present a system that integrates several research aspects to achieve a real exploration exercise in a tunnel using a robot team. These aspects are as follows: deployment planning, semantic feature recognition, multirobot navigation, localization, map building, and real‐time communications. Two experimental scenarios have been used for the assessment of the system. The first is the Spanish Santa Marta mine, a large mazelike environment selected for its complexity for all the tasks involved. The second is the Spanish‐French Somport tunnel, an old railway between Spain and France through the Central Pyrenees, used to carry out the real‐world experiments. The latter is a simpler scenario, but it serves to highlight the real communication issues.     

A hierarchical long short term safety framework for efficient robot manipulation under uncertainty

Safe and efficient robot manipulation in uncertain clustered environments has been recognized to be a key element of future intelligent industrial robots. Unlike traditional robots that work in structured and deterministic environments, intelligent industrial robots need to operate in dynamically changing and stochastic environments with limited computation resources. This paper proposed a hierarchical long short term safety system (HLSTS), where the upper layer contains a long term planner for global reference trajectory generation and the lower layer contains a short term planner for real-time emergent safety maneuvers. Additionally, a hierarchical coordinator is proposed to enable smooth coordination of the two layers by compensating the communication delay through trajectory modification. The theoretical results verify that the long term planner can always find a feasible trajectory (feasibility guarantee); and the short term planner can guarantee safety in the probabilistic sense. The proposed architecture is validated in industrial settings in both simulations and real robot experiments, where the robot is interacting with randomly moving obstacles while performing a goal reaching task. Experimental results demonstrate that the proposed HLSTS framework not only guarantees safety but also improves task efficiency.     

Decentralized network topologies in multirobot systems

This paper studies the problem of forming and updating the network topology in a multirobot system that is simultaneously engaged in a given task. The contribution of this paper is to propose a decentralized model of how the network may evolve based on network-related payoff functions and pairwise games. As such, pairwise games provide a practical and general scheme for contacting other robots and revising the network topology. A network is deemed acceptable by all the robots using pairwise stability and pairwise Nash equilibrium. As an application, we consider networks that are generated with mutual link-based payoff functions and show that – under some assumptions regarding changes in the configuration states – each game is ensured of converging to a pairwise stable network. This approach is then integrated with a common robotic task where the network is critical to successful task completion. The resulting performance is evaluated with respect to a variety of measures including task completion, network density, and the average payoff along with comparative results with all-to-all communication.     

移动机器人长期自主环境适应研究进展和展望

真实世界中存在光照、天气、季节及场景结构等复杂环境因素, 这些因素的改变对移动机器人基本行为和任务能力带来巨大挑战.随着机器人与人工智能技术的不断发展, 如何使移动机器人在长期运行中与复杂多变的环境条件相适应是智能机器人领域的研究热点.本文重点从地图构建与动态维护、重定位及场景理解等移动机器人基本行为能力的系统综述入手, 对移动机器人长期自主环境适应的前沿技术与研究方向进行了着重论述与分析.最后对该领域的研究重点和技术发展趋势进行了探讨.     

A semantically-informed multirobot system for exploration of relevant areas in search and rescue settings

Coordinated multirobot exploration involves autonomous discovering and mapping of the features of initially unknown environments by using multiple robots. Autonomously exploring mobile robots are usually driven, both in selecting locations to visit and in assigning them to robots, by knowledge of the already explored portions of the environment, often represented in a metric map. In the literature, some works addressed the use of semantic knowledge in exploration, which, embedded in a semantic map, associates spatial concepts (like ‘rooms’ and ‘corridors’) with metric entities, showing its effectiveness in improving the total area explored by robots. In this paper, we build on these results and propose a system that exploits semantic information to push robots to explore relevant areas of initially unknown environments, according to a priori information provided by human users. Discovery of relevant areas is significant in some search and rescue settings, in which human rescuers can instruct robots to search for victims in specific areas, for example in cubicles if a disaster happened in an office building during working hours. We propose to speed up the exploration of specific areas by using semantic information both to select locations to visit and to determine the number of robots to allocate to those locations. In this way, for example, more robots could be assigned to a candidate location in a corridor, so the attached rooms can be explored faster. We tested our semantic-based multirobot exploration system within a reliable robot simulator and we evaluated its performance in realistic search and rescue indoor settings with respect to state-of-the-art approaches.     

Queues and Artificial Potential Trenches for Multirobot Formations

In this paper, we present a novel approach for representing formation structures in terms of queues and formation vertices, rather than with nodes, as well as the introduction of the new concept of artificial potential trenches, for effectively controlling the formation of a group of robots. The scheme improves the scalability and flexibility of robot formations when the team size changes, and at the same time, allows formations to adapt to obstacles. Furthermore, for multirobot teams to operate successfully in real and unstructured environments, the instant goal method is used to effectively solve the local minima problem.     

A flooding algorithm for multirobot exploration

In this paper, we present a multirobot exploration algorithm that aims at reducing the exploration time and to minimize the overall traverse distance of the robots by coordinating the movement of the robots performing the exploration. Modeling the environment as a tree, we consider a coordination model that restricts the number of robots allowed to traverse an edge and to enter a vertex during each step. This coordination is achieved in a decentralized manner by the robots using a set of active landmarks that are dropped by them at explored vertices. We mathematically analyze the algorithm on trees, obtaining its main properties and specifying its bounds on the exploration time. We also define three metrics of performance for multirobot algorithms. We simulate and compare the performance of this new algorithm with those of our multirobot depth first search (MR-DFS) approach presented in our recent paper and classic single-robot DFS.     

Cooperative hunting by distributed mobile robots based on local interaction

This paper proposes a distributed control approach called local interactions with local coordinate systems (LILCS)to multirobot hunting tasks in unknown environments, where a team of mobile robots hunts a target called evader, which will actively try to escape with a safety strategy. This robust approach can cope with accumulative errors of wheels and imperfect communication networks. Computer simulations show the validity of the proposed approach.     

Path planning for permutation-invariant multirobot formations

In many multirobot applications, the specific assignment of goal configurations to robots is less important than the overall behavior of the robot formation. In such cases, it is convenient to define a permutation-invariant multirobot formation as a set of robot configurations, without assigning specific configurations to specific robots. For the case of robots that translate in the plane, we can represent such a formation by the coefficients of a complex polynomial whose roots represent the robot configurations. Since these coefficients are invariant with respect to permutation of the roots of the polynomial, they provide an effective representation for permutation-invariant formations. In this paper, we extend this idea to build a full representation of a permutation-invariant formation space. We describe the properties of the representation, and show how it can be used to construct collision-free paths for permutation-invariant formations.     

Adaptive Distributed Fault-tolerant Formation Control for Multi-robot Systems under Partial Loss of Actuator Effectiveness

This paper presents an adaptive distributed fault-tolerant formation control for multi-robot systems. Both the kinematics and dynamics of differential wheeled mobile robots are considered. In particular, the problem caused by actuator faults is investigated. Based on dynamic surface control techniques, adaptive formation controllers can be obtained under a directed communication network. The closed-loop stability is guaranteed by using Lyapunov stability analysis such that all followers can exponentially converge to a leader-follower formation pattern. Simulation and experimental results illustrate that the desired formation pattern can be preserved for a group of wheeled robots subject to unknown uncertainties and actuator faults.     

A dynamic priority based path planning for cooperation of multiple mobile robots in formation forming

Robots that work in a proper formation show several advantages compared to a single complex robot, such as a reduced cost, robustness, efficiency and improved performance. Existing researches focused on the method of keeping the formation shape during the motion, but usually neglect collision constraints or assume a simplified model of obstacles. This paper investigates the path planning of forming a target robot formation in a clutter environment containing unknown obstacles. The contribution lies in proposing an efficient path planner for the multiple mobile robots to achieve their goals through the clutter environment and developing a dynamic priority strategy for cooperation of robots in forming the target formation. A multirobot system is set up to verify the proposed method of robot path planning. Simulations and experiments results demonstrate that the proposed method can successfully address the collision avoidance problem as well as the formation forming problem.     

Digital twin-driven deep reinforcement learning for adaptive task allocation in robotic construction

In order to accomplish diverse tasks successfully in a dynamic (i.e., changing over time) construction environment, robots should be able to prioritize assigned tasks to optimize their performance in a given state. Recently, a deep reinforcement learning (DRL) approach has shown potential for addressing such adaptive task allocation. It remains unanswered, however, whether or not DRL can address adaptive task allocation problems in dynamic robotic construction environments. In this paper, we developed and tested a digital twin-driven DRL learning method to explore the potential of DRL for adaptive task allocation in robotic construction environments. Specifically, the digital twin synthesizes sensory data from physical assets and is used to simulate a variety of dynamic robotic construction site conditions within which a DRL agent can interact. As a result, the agent can learn an adaptive task allocation strategy that increases project performance. We tested this method with a case project in which a virtual robotic construction project (i.e., interlocking concrete bricks are delivered and assembled by robots) was digitally twinned for DRL training and testing. Results indicated that the DRL model’s task allocation approach reduced construction time by 36% in three dynamic testing environments when compared to a rule-based imperative model. The proposed DRL learning method promises to be an effective tool for adaptive task allocation in dynamic robotic construction environments. Such an adaptive task allocation method can help construction robots cope with uncertainties and can ultimately improve construction project performance by efficiently prioritizing assigned tasks.