Multi-agent Control Architecture Research Articles

Hierarchical Integration of Model Predictive and Fuzzy Logic Control for Combined Coverage and Target-Oriented Search-and-Rescue via Robots with Imperfect Sensors

Search-and-rescue (SaR) in unknown environments is a crucial task with life-threatening risks. SaR requires precise, optimal, and fast decisions to be made. Robots are promising candidates expected to execute various SaR tasks autonomously. While humans use heuristics to effectively deal with uncertainties of SaR, optimisation of multiple objectives (e.g., the mission time, the area covered, the number of victims detected), in the presence of physical and control constraints, is a mathematical challenge that requires machine computations. Thus including both human-inspired and mathematical capabilities in decision making of SaR robots is highly desired. However, developing control approaches that exhibit both capabilities has been significantly ignored in literature. Moreover, coordinating the decisions of the robots in large-scale SaR missions with affordable computation costs is an open challenge. Finally, in real-life, due to defects (e.g., in the sensors of the robots) or environmental factors (e.g., smoke) data perceived by SaR robots may be prone to uncertainties. We introduce a hierarchical multi-agent control architecture that simultaneously provides the following advantages: exploiting non-homogeneous and imperfect perception capabilities of SaR robots; improving the global performance as it is provided by centralised controllers; computational efficiency and robustness to failure of the central controller as offered by decentralised control methods. The integrated structure of the proposed control framework allows to combine human-inspired and mathematical decision making methods, via respectively fuzzy logic and model predictive control, in a coordinated and computationally efficient way. Our results for various computer-based simulations show that while the area coverage with the proposed control approach is comparable to existing heuristic methods that are particularly developed for coverage-oriented SaR, our approach has a significantly better performance regarding locating the trapped victims. Furthermore, with comparable computation times, the proposed control approach successfully avoids conflicts that may appear in non-cooperative control methods. In summary, the proposed multi-agent control system is capable of combining coverage-oriented and target-oriented SaR in a balanced and coordinated way.

A Multi-Agent Control Architecture for a Rescue Robot

Cognitive architectures investigate the components and interactions neccessary for construction of an intelligent system. Despite much progress and theory, implementations of architectures are rare. This research presents a novel cognitive architecture grounded in the design of a control system for an autonomous rescue robot. Experiments are conducted in high-fidelity 3D simulation of a rescue environment based on NISTs RoboCup Rescue.

Multi-Agent Coordination of Thermostatically Controlled Loads by Smart Power Sockets for Electric Demand Side Management

This article presents a multi-agent control architecture and an online optimization method based on a dynamic average consensus to coordinate the power consumption of a large population of thermostatically controlled loads (TCLs). Our objective is to penalize peaks of power demand, smooth the load profile, and enable demand-side management. The proposed architecture and methods exploit only local measurements of power consumption via smart power sockets (SPSs) with no access to their internal temperature. No centralized aggregator of information is exploited, and agents preserve their privacy by cooperating anonymously only through consensus-based distributed estimation. The interactions among devices occur through an unstructured peer-to-peer (P2P) network over the Internet. Methods for parameter identification, state estimation, and mixed logical modeling of TCLs and SPSs are included. The architecture is designed from a multi-agent and plug-and-play perspective, in which existing household appliances can interact with each other in an urban environment. Finally, a novel low-cost testbed is proposed along with numerical tests and experimental validation.

Fault-tolerant multi-agent control architecture for autonomous mobile manipulators: Simulation results

This paper presents our ongoing efforts toward the development of a multi-agent distributed framework for autonomous control of mobile manipulators. The proposed scheme assigns a reactive agent to control each degree-of-freedom of the manipulator(s), a hybrid agent to control the mobile base, and a supervisory agent to coordinate and synchronize the work of the control agents. Each control agent implements a Simulation-Verification technique to optimize, locally and independently from the other agents, a predefined objective function. The final goal consists of bringing the end-effector as close as possible to imposed operational targets (reaching tasks).Different simulation scenarios are described and carried out for the case of RobuTER/ULM robot, with and without considering failures of some articulations of the manipulator or the mobile base. Results show that the main advantage of the proposed approach is that the system pledges a fault-tolerant response to some breakdowns without needing any specific additional treatment.

A multi-agent architecture for plug and produce on an industrial assembly platform

Modern manufacturing companies face increased pressures to adapt to shorter product life cycles and the need to reconfigure more frequently their production systems to offer new product variants. This paper proposes a new multi-agent architecture utilising “plug and produce” principles for configuration and reconfiguration of production systems with minimum human intervention. A new decision-making approach for system reconfiguration based on tasks re-allocation is presented using goal driven methods. The application of the proposed architecture is described with a number of architectural views and its deployment is illustrated using a validation scenario implemented on an industrial assembly platform. The proposed methodology provides an innovative application of a multi-agent control environment and architecture with the objective of significantly reducing the time for deployment and ramp-up of small footprint assembly systems.

Multi-Agent Architecture for Control of Heating and Cooling in a Residential Space

Energy demand in a smart grid is directly related to energy consumption, as defined by user needs and comfort experience. This article presents a multi-agent architecture for smart control of space heating and cooling processes, in an attempt to enable flexible ways of monitoring and adjusting energy supply and demand. In this proposed system, control agents are implemented in order to perform temperature set-point delegation for heating and cooling systems in a building, offering a means to observe and learn from both the environment and the occupant. Operation of the proposed algorithms is compared with traditional algorithms utilized for room heating, using a simulated model of a residential building and real data about user behaviour. The results show (i) the performance of machine learning for the occupancy forecasting problem and for the problem of calculating the time to heat or cool a room; and (ii) the performance of the control algorithms, with respect to energy consumption and occupant comfort. The proposed control agents make it possible to significantly improve an occupant comfort with a relatively small increase in energy consumption, compared with simple control strategies that always maintain predefined temperatures. The findings enable the smart grid to anticipate the energy needs of the building.

Chidori – a bio-inspired cognitive architecture for collective robotics applications

A cognitive collaborative multi-agent control architecture that addresses real-world control problems for swarms of mobile robots is proposed. The swarm's emergent behaviour is obtained by using a distributed Particle Swarm Optimization inspired algorithm. A swarm-user interface is also presented and offers a way for a human operator to interact with and guide the robotic swarm without limiting its emergent intelligence. The architecture is designed as a multi-agent system developed using JADE Framework. Three types of agents were defined: local behaviour agent, social behaviour agent and graphical user interface (GUI) agent. Each robot is associated with a pair of a local and a social behaviour agents which implement the reactive component and the interaction between the robots. The swarm forms a hierarchical structure composed of subswarms and neighbourhoods based on tasks and goals defined by the user or the swarm itself. The GUI agent is used as a link between the human expert and the swarm. The architecture was tested on a swarm of e-puck robots.

A Multi-Agent Control Architecture for a Robotic Wheelchair

Assistant robots like robotic wheelchairs can perform an effective and valuable work in our daily lives. However, they eventually may need external help from humans in the robot environment (particularly, the driver in the case of a wheelchair) to accomplish safely and efficiently some tricky tasks for the current technology, i.e. opening a locked door, traversing a crowded area, etc. This article proposes a control architecture for assistant robots designed under a multi-agent perspective that facilitates the participation of humans into the robotic system and improves the overall performance of the robot as well as its dependability. Within our design, agents have their own intentions and beliefs, have different abilities (that include algorithmic behaviours and human skills) and also learn autonomously the most convenient method to carry out their actions through reinforcement learning. The proposed architecture is illustrated with a real assistant robot: a robotic wheelchair that provides mobility to impaired or elderly people.

A multi-agent reinforcement learning approach to obtaining dynamic control policies for stochastic lot scheduling problem

This paper presents a methodology that, for the problem of scheduling of a single server on multiple products, finds a dynamic control policy via intelligent agents. The dynamic (state dependent) policy optimizes a cost function based on the WIP inventory, the backorder penalty costs and the setup costs, while meeting the productivity constraints for the products. The methodology uses a simulation optimization technique called Reinforcement Learning (RL) and was tested on a stochastic lot-scheduling problem (SELSP) having a state–action space of size 1.8 × 10 7. The dynamic policies obtained through the RL-based approach outperformed various cyclic policies. The RL approach was implemented via a multi-agent control architecture where a decision agent was assigned to each of the products. A Neural Network based approach (least mean square (LMS) algorithm) was used to approximate the reinforcement value function during the implementation of the RL-based methodology. Finally, the dynamic control policy over the large state space was extracted from the reinforcement values using a commercially available tree classifier tool.

A multi-agent architecture for control of AGV systems

Agent is an autonomous, computational entity that can be viewed as perceiving its environment and acting upon it. Agents are event-driven objects that can be integrated in automated manufacturing environments to control certain tasks. In this paper a set of agents (a multi-agent system) is introduced to control an automated manufacturing environment. The architecture includes functions at the manufacturing cell level, materials handling and transport level, and factory scheduling level. Communication between these agents is accomplished by using a relational database (blackboard system). The relational database also integrates the requirements of a manufacturing execution system within the multi-agent task structure, which is unique to this architecture. Manufacturing cell and scheduling agents have been previously described in the literature. Here we focus our attention on the functions of the agents of the transport system, which is composed of a set of AGVs.

A hierarchical fuzzy–genetic multi-agent architecture for intelligent buildings online learning, adaptation and control

In this paper, we describe a new application domain for intelligent autonomous systems––intelligent buildings (IB). In doing so we present a novel approach to the implementation of IB agents based on a hierarchical fuzzy genetic multi-embedded-agent architecture comprising a low-level behaviour based reactive layer whose outputs are co-ordinated in a fuzzy way according to deliberative plans. The fuzzy rules related to the room resident comfort are learnt and adapted online using our patented fuzzy–genetic techniques (British patent 99-10539.7). The learnt rule base is updated and adapted via an iterative machine-user dialogue. This learning starts from the best stored rule set in the agent memory (Experience Bank) thereby decreasing the learning time and creating an intelligent agent with memory. We discuss the role of learning in building control systems, and we explain the importance of acquiring information from sensors, rather than relying on pre-programmed models, to determine user needs. We describe how our architecture, consisting of distributed embedded agents, utilises sensory information to learn to perform tasks related to user comfort, energy conservation, and safety. We show how these agents, employing a behaviour-based approach derived from robotics research, are able to continuously learn and adapt to individuals within a building, whilst always providing a fast, safe response to any situation. In addition we show that our system learns similar rules to other offline supervised methods but that our system has the additional capability to rapidly learn and optimise the learnt rule base. Applications of this system include personal support (e.g. increasing independence and quality of life for older people), energy efficiency in commercial buildings or living-area control systems for space vehicles and planetary habitation modules.

A Discrete-Event Formalism to Model Adaptive Multi-Agent Manufacturing Control

A multi-agent control architecture for agile manufacturing is modeled with a discrete event system approach. Two interacting types of autonomous controllers, Part and Machine Agents, control part flow and machine processing. Both are modeled as atomic discrete event systems. Subsequently the entire network of agents is described by relationships between agents. The architecture encompasses a mechanism to adapt agents' decision laws, integrated in agents' state. So, even if there is no explicit interaction between agents of the same type, cooperation is indirectly achieved by adapting the laws to the operational conditions of the manufacturing floor. A sample of simulation results shows the potentialities of the architecture.

Multiagent Architecture Combined with a Multicontract Protocol for FMS Control

This paper describes a new Multiagent architecture for control of flexible manufacturing. In this architecture, agents coordinate their actions following a new negotiation protocol used for scheduling and rescheduling of tasks. The proposed protocol, MultiContract-Net, is an innovation of Contract-Net protocol enabling several tasks to be negotiated concurrently in real time with optimal results. Thus, the multicontract-Net protocol enables both dynamic task allocation and optimization of opportunities provided by manufacturing flexibility by handling knowledge uncertainty characterizing the negotiation process. This paper stresses the efficiency of distributed implementation.

Human–Robot Cooperation Using Multi-Agent-Systems

In this paper a new intelligent robot control scheme is presented which enables a cooperative work of humans and robots through direct contact interaction in a partially known environment. Because of the high flexibility and adaptability, the human–robot cooperation is expected to have a wide range of applications in uncertain environments, not only in future construction and manufacturing industries but also in service branches. A multi-agent control architecture gives an appropriate frame for the flexibility of the human–robot-team. Robots are considered as intelligent autonomous assistants of humans which can mutually interact on a symbolic level and a physical level. This interaction is achieved through the exchange of information between humans and robots, the interpretation of the transmitted information, the coordination of the activities and the cooperation between independent system components. Equipped with sensing modalities for the perception of the environment, the robot system KAMRO (Karlsruhe Autonomous Mobile Robot) is introduced to demonstrate the principles of the cooperation among humans and robot agents. Experiments were conducted to prove the effectiveness of our concept.

QoS control in wireless ATM

This paper introduces a 3-level multiagent architecture for QoS control in WATM. The ultimate aim of the proposed architecture is to provide a self-regulating network congestion control management by means of global network state awareness and agent interactions. The agents dynamically manage the buffer space at the level of a switch and interact to reduce the cell loss ratio while guaranteeing a bounded transit delay. We particularly address video transmission over UBR services using a per-VP queuing approach and an adaptive cell discarding congestion control scheme. Furthermore, a dynamic reconfiguration of the agents is performed during handoffs in order to continue meeting user end-to-end QoS requirements. The handoff delay absorption is also addressed.

A Multi‐Agent Control Architecture for a Robotic Wheelchair

Assistant robots like robotic wheelchairs can perform an effective and valuable work in our daily lives. However, they eventually may need external help from humans in the robot environment (particularly, the driver in the case of a wheelchair) to accomplish safely and efficiently some tricky tasks for the current technology, i.e. opening a locked door, traversing a crowded area, etc. This article proposes a control architecture for assistant robots designed under a multi‐agent perspective that facilitates the participation of humans into the robotic system and improves the overall performance of the robot as well as its dependability. Within our design, agents have their own intentions and beliefs, have different abilities (that include algorithmic behaviours and human skills) and also learn autonomously the most convenient method to carry out their actions through reinforcement learning. The proposed architecture is illustrated with a real assistant robot: a robotic wheelchair that provides mobility to impaired or elderly people.