Distributed multi-robot patrol: A scalable and fault-tolerant framework

This paper addresses the Multi-Robot Patrolling Problem, where agents must coordinate their actions while continuously deciding which place to move next after clearing their locations. This problem is commonly addressed using centralized planners with global knowledge and/or calculating a priori routes for all robots before the beginning of the mission. In this work, two distributed techniques to solve the problem are proposed. These are motivated by the need to adapt to the changes in the system at any time and the possibility to add or remove patrolling agents (e.g., due to faults).The first technique presented is greedy and aims to maximize robot’s local gain. The second one is an extension of the former, which takes into account the distribution of agents in the space to reduce interference and foster scalability.The validation of the proposed solution is preliminarily conducted through realistic simulations as well as experiments with robot platforms in a small lab scenario. Subsequently, the work is verified in a large indoor real-world environment with a team of autonomous mobile robots with scalability and fault-tolerance assessment.     

Map focus: A way to reconcile reactivity and deliberation in multirobot systems

Cooperative multirobot systems require both real-time responsiveness and some form of coordination to get the desired overall behavior. This can be obtained with a combined use of reactive and deliberative subsystems. In this paper, we illustrate an effective technique for putting together these two components. The method is based on the idea that every robot maintains a local map and then dynamically focuses its attention on the part which is relevant in the current context. The framework, which is fully distributed and scalable, is enriched with cooperative behaviors, i.e. behaviors pursued by more than one robot. We provide the details of how the proposed idea has been studied in a simulated cooperative foraging task and proved to be effective.     

Robust control of multi-robot systems: The generalized energy accumulation approach

This paper is concerned with the control problem of a multi-robot system handling a common payload with unknown mass properties. Force constraints at the grasp points are considered. Robust control schemes are proposed to cope with the model uncertainty and achieve asymptotic path tracking. To deal with the force constraints, a strategy for optimally sharing the task is suggested. This strategy basically consists of two steps. The first is to detect the roots that need help and the second to arrange for help. It is shown that the overall system is robust to uncertain payload parameters, and satisfies the force constraints.     

From insect to Internet: Situated control for networked robot teams

Ant-like systems take advantage of agents' situatedness to reduce or eliminate the need for centralized control or global knowledge. This reduces the need for complexity of individuals and leads to robust, scalable systems. Such insect-inspired situated approaches have proven effective both for task performance and task allocation. The desire for general, principled techniques for situated interaction has led us to study the exploitation of abstract situatedness – situatedness in non-physical environments. The port-arbitrated behavior-based control approach provides a well-structured abstract behavior space in which agents can participate in situated interaction. We focus on the problem of role assumption, distributed task allocation in which each agent selects its own task-performing role. This paper details our general, principled Broadcast of Local Eligibility (BLE) technique for role-assumption in such behavior-space-situated systems, and provides experimental results from the CMOMMT target-tracking task. This revised version was published online in August 2006 with corrections to the Cover Date.     

A fault-tolerant approach to robot teams

As the applications of mobile robotics evolve it has become increasingly less practical for researchers to design custom hardware and control systems for each problem. This paper presents a new approach to control system design in order to look beyond end-of-lifecycle performance, and consider control system structure, flexibility, and extensibility. Towards these ends the Control ad libitum philosophy was proposed, stating that to make significant progress in the real-world application of mobile robot teams the control system must be structured such that teams can be formed in real-time from diverse components. The Control ad libitum philosophy was applied to the design of the HAA (Host, Avatar, Agent) architecture: a modular hierarchical framework built with provably correct distributed algorithms. A control system for mapping, exploration, and foraging was developed using the HAA architecture and evaluated in three experiments. First, the basic functionality of the HAA architecture was studied, specifically the ability to: (a) dynamically form the control system, (b) dynamically form the robot team, (c) dynamically form the processing network, and (d) handle heterogeneous teams and allocate robots between tasks based on their capabilities. Secondly, the control system was tested with different rates of software failure and was able to successfully complete its tasks even when each module was set to fail every 0.5–1.5 min. Thirdly, the control system was subjected to concurrent software and hardware failures, and was still able to complete a foraging task in a 216 m² environment.     

Hierarchical multi-robot navigation and formation in unknown environments via deep reinforcement learning and distributed optimization

Compared with a single robot, Multi-robot Systems (MRSs) can undertake more challenging tasks in complex scenarios benefiting from the increased transportation capacity and fault tolerance. This paper presents a hierarchical framework for multi-robot navigation and formation in unknown environments with static and dynamic obstacles, where the robots compute and maintain the optimized formation while making progress to the target together. In the proposed framework, each single robot is capable of navigating to the global target in unknown environments based on its local perception, and only limited communication among robots is required to obtain the optimal formation. Accordingly, three modules are included in this framework. Firstly, we design a learning network based on Deep Deterministic Policy Gradient (DDPG) to address the global navigation task for single robot, which derives end-to-end policies that map the robot’s local perception into its velocity commands. To handle complex obstacle distributions (e.g. narrow/zigzag passage and local minimum) and stabilize the training process, strategies of Curriculum Learning (CL) and Reward Shaping (RS) are combined. Secondly, for an expected formation, its real-time configuration is optimized by a distributed optimization. This configuration considers surrounding obstacles and current formation status, and provides each robot with its formation target. Finally, a velocity adjustment method considering the robot kinematics is designed which adjusts the navigation velocity of each robot according to its formation target, making all the robots navigate to their targets while maintaining the expected formation. This framework allows for formation online reconfiguration and is scalable with the number of robots. Extensive simulations and 3-D evaluations verify that our method can navigate the MRS in unknown environments while maintaining the optimal formation.     

Spillover Algorithm: A decentralised coordination approach for multi-robot production planning in open shared factories

Open and shared manufacturing factories typically dispose of a limited number of industrial robots and/or other production resources that should be properly allocated to tasks in time for an effective and efficient system performance. In particular, we deal with the dynamic capacitated production planning problem with sequence independent setup costs where quantities of products to manufacture need to be determined at consecutive periods within a given time horizon and products can be anticipated or back-ordered related to the demand period. We consider a decentralised multi-agent variant of this problem in an open factory setting with multiple owners of robots as well as different owners of the items to be produced, both considered self-interested and individually rational. Existing solution approaches to the classic constrained lot-sizing problem are centralised exact methods that require sharing of global knowledge of all the participants’ private and sensitive information and are not applicable in the described multi-agent context. Therefore, we propose a computationally efficient decentralised approach based on the spillover effect that solves this NP-hard problem by distributing decisions in an intrinsically decentralised multi-agent system environment while protecting private and sensitive information. To the best of our knowledge, this is the first decentralised algorithm for the solution of the studied problem in intrinsically decentralised environments where production resources and/or products are owned by multiple stakeholders with possibly conflicting objectives. To show its efficiency, the performance of the Spillover Algorithm is benchmarked against state-of-the-art commercial solver CPLEX 12.8.     

A hybrid control approach to action coordination for mobile robots

In this paper, the problem concerning how to coordinate the contributions from concurrent controllers, when controlling mobile robots, is investigated. It is shown how a behavior based control system for autonomous robots can be modeled as a hybrid automaton, where each node corresponds to a distinct robot behavior. This type of construction gives rise to chattering executions, but it is shown how regularized automata can be used to solve this problem. As an illustration, the obstacle-negotiation problem is solved by using a combination of a robust path-following behavior and a reactive obstacle-avoidance behavior that move the robot around a given obstacle at a predefined safety distance.     

A virtual structure approach to formation control of unicycle mobile robots using mutual coupling

In this article, the formation control problem for unicycle mobile robots is studied. A distributed virtual structure control strategy with mutual coupling between the robots is proposed. The rationale behind the introduction of the coupling terms is the fact that these introduce additional robustness of the formation with respect to perturbations as compared to typical leader–follower approaches. The applicability of the proposed approach is shown in simulations and experiments with a group of wirelessly controlled mobile robots.     

Collective plume tracing: A minimal information approach to collective control

A team of simple robots are used to trace a chemical plume to its source in order to find buried landmines. The goal of this paper is to analyze and design ‘emergent’ behaviors to enable the team of simple robots to perform plume tracing with the assistance of information theory. The first step in the design process is to define a fundamental trade‐off for collective systems between processing, memory, and communications for each robot in order to execute the desired collective behaviors. The baseline problem is to determine the minimum values for processing, memory, and communications simultaneously. This will enable the designer to determine the required information flow and how effectively the information resources are being utilized in collective systems. The solution to this baseline problem is an 8‐bit processor, no memory, and three words to communicate. The details of this solution are described in this paper. The second step is to extend the previous kinematic solution to a more general Hamiltonian‐based solution to develop a Fisher Information Equivalency. The Fisher Information Equivalency leads directly to necessary and sufficient conditions for stability and control of nonlinear collective systems via physical and information exergy flows. In particular, the creation of a ‘virtual/information potential’ by the team of robots via the decentralized distributed networked sensors produces a direct relationship between proportional feedback control, stored exergy in the system, and Fisher Information. This nonlinear stability formulation through Fisher Information directly provides an optimization problem to ‘tune’ the performance of the team of robots as a function of required information resources. Copyright © 2009 John Wiley & Sons, Ltd.     

Tangential Gap Flow (TGF) navigation: A new reactive obstacle avoidance approach for highly cluttered environments

This paper presents a novel reactive collision avoidance method for mobile robots moving in dense and cluttered environments. The proposed method, entitled Tangential Gap flow (TGF), simplifies the navigation problem using a divide and conquer strategy inspired by the well-known Nearness-Diagram Navigation (ND) techniques. At each control cycle, the TGF extracts free openings surrounding the robot and identifies the suitable heading which makes the best progress towards the goal. This heading is then adjusted to avoid the risk of collision with nearby obstacles based on two concepts namely, tangential and gap flow navigation. The tangential navigation steers the robot parallel to the boundary of the closest obstacle while still emphasizing the progress towards the goal. The gap flow navigation safely and smoothly drives the robot towards the free area in between obstacles that lead to the target. The resultant trajectory is faster, shorter and less-oscillatory when compared to the ND methods. Furthermore, identifying the avoidance maneuver is extended to consider all nearby obstacle points and generate an avoidance rule applicable for all obstacle configurations. Consequently, a smoother yet much more stable behavior is achieved. The stability of the motion controller, that guides the robot towards the desired goal, is proved in the Lyapunov sense. Experimental results including a performance evaluation in very dense and complex environments demonstrate the power of the proposed approach. Additionally, a discussion and comparison with existing Nearness-Diagram Navigation variants is presented.     

Optimal solutions to a class of power management problems in mobile robots

This paper studies an approach to minimize the power consumption of a mobile robot by controlling its traveling speed and the frequency of its on-board processor simultaneously. The problem is formulated as a discrete-time optimal control problem with a random terminal time and probabilistic state constraints. A general solution procedure suitable for arbitrary power functions of the motor and the processor is proposed. Furthermore, for a class of realistic power functions, the optimal solution is derived analytically. Interpretations of the optimal solution in the practical context are also discussed. Simulation results show that the proposed method can save a significant amount of energy compared with some heuristic schemes.     

A new approach to 3D reconstruction without camera calibration

In this paper, we present a new approach for 3D scene reconstruction based on projective geometry without camera calibration. Previous works use at least six points to build two projective reference planes. Our contribution is to reduce the number of reference points to four by exploiting some geometrical shapes contained in the scene. The first implemented algorithm allows the reconstruction of a fourth point on each reference plane. The second algorithm is devoted to the 3D reconstruction. We obtained the expected good results and the proposed method is to equip a mobile robot moving in a structured environment.     

A control theoretic approach to malaria immunotherapy with state jumps

We investigate a control method for malaria dynamics to boost the immune response using a model-based approach. The idea of state jump is introduced and discussed using a hybrid model. To implement the state jumps we use a physiologically feasible method for the biological system, whereby the introduction of a pathogen into the system steers it to a desirable status. It is noted that we take advantage of a pathogen to implement state jumps in the system. The study is supported by recently reported experimental results.     

A polynomial dynamic system approach to software design for attractivity requirement

Because of the widespread increasing application of Web services and autonomic computing, self-adaptive software is an area gaining increasing importance. Control theory provides a theoretical foundation for self-adaptive software. In this paper, we propose the use of the supervisory control theory of discrete event dynamic systems (DEDS) to provide a rigorous foundation for designing software for reactive systems. This paper focuses in particular on design of software with an attractivity requirement. It studies this problem using the polynomial dynamic system (PDS) model of DEDS. A necessary and sufficient condition for software existence and two algorithms for such software design are presented.     

一类不确定非线性系统无过参数自适应控制设计新方法

文中研究了一类控制系数未知的更一般高阶不确定非线性系统的全局稳定自适应控制设计问题.尽管现有文献已解决了该问题,但对于n维系统,现有构造稳定自适应控制的方法至少需要设计n+1个未知参数的动态调节律,即动态补偿器的维数至少为n+1,存在较为严重的过参数问题.文中通过定义新的需动态调节的未知参数,运用增加幂积分和有关自适应技术,成功地解决了已有方法中的过参数问题,给出了构造只含有一个参数调节律的稳定自适应控制设计新方法.仿真算例验证了文中所给方法的有效性.     

Special cause management: A knowledge-based approach to statistical process control

In this paper we discuss a set of software tools developed to support the tasks associated with managing special causes of variation in a manufacturing process. These tasks include the detection of significant changes in process variables, a diagnosis of the causes of those changes, the discovery of new causes, the management of performance data, and the reporting of results. The software tools include automatic recognition of out-of-control features in critical process variables, rule-based diagnosis of special causes, a model-based search for symptoms where a diagnosis is not possible, and automated reporting aids. It is hoped that these tools will enhance the efficiency of special cause management.     

Model-based Predictive Control of Hybrid Systems: A Probabilistic Neural-network Approach to Real-time Control

This paper proposes an approach for reducing the computational complexity of a model-predictive-control strategy for discrete-time hybrid systems with discrete inputs only. Existing solutions are based on dynamic programming and multi-parametric programming approaches, while the one proposed in this paper is based on a modified version of performance-driven reachability analyses. The algorithm abstracts the behaviour of the hybrid system by building a ’tree of evolution’. The nodes of the tree represent the reachable states of a process, and the branches correspond to input combinations leading to designated states. A cost-function value is associated with each node and based on this value the exploration of the tree is driven. For any initial state, an input sequence is thus obtained, driving the system optimally over a finite horizon. According to the model predictive strategy, only the first input is actually applied to the system. The number of possible discrete input combinations is finite and the feasible set of the states of the system may be partitioned according to the optimization results. In the proposed approach, the partitioning is performed offline and a probabilistic neural network (PNN) is then trained by the set of points at the borders of the state-space partitions. The trained PNN is used as a system-state-based control-law classifier. Thus, the online computational effort is minimized and the control can be implemented in real time.     

A distributed predictive control approach for periodic flow-based networks: application to drinking water systems

This paper proposes a distributed model predictive control approach designed to work in a cooperative manner for controlling flow-based networks showing periodic behaviours. Under this distributed approach, local controllers cooperate in order to enhance the performance of the whole flow network avoiding the use of a coordination layer. Alternatively, controllers use both the monolithic model of the network and the given global cost function to optimise the control inputs of the local controllers but taking into account the effect of their decisions over the remainder subsystems conforming the entire network. In this sense, a global (all-to-all) communication strategy is considered. Although the Pareto optimality cannot be reached due to the existence of non-sparse coupling constraints, the asymptotic convergence to a Nash equilibrium is guaranteed. The resultant strategy is tested and its effectiveness is shown when applied to a large-scale complex flow-based network: the Barcelona drinking water supply system.     

A new approach to adaptive control design without overparametrization for a class of uncertain nonlinear systems

This paper considers the globally stabilizing adaptive controller design for a class of more general uncertain high-order nonlinear systems with unknown control coefficients.Although the existing literature has solved the problem,for n-dimensional systems,the existing methods need at least n + 1 dynamic updating laws for the unknown parameters to construct the stabilizing adaptive controller;that is,the dimension of the dynamic compensator is not less than n + 1,and therefore,there exists serious overparame...