Distributed collaborative complete coverage path planning based on hybrid strategy

Collaborative coverage path planning(CCPP) refers to obtaining the shortest paths passing over all places except obstacles in a certain area or space. A multi-unmanned aerial vehicle(UAV) collaborative CCPP algorithm is proposed for the urban rescue search or military search in outdoor environment.Due to flexible control of small UAVs, it can be considered that all UAVs fly at the same altitude, that is, they perform search tasks on a two-dimensional plane. Based on the agents’ motion characteristics and environmental information, a mathematical model of CCPP problem is established. The minimum time for UAVs to complete the CCPP is the objective function, and complete coverage constraint, no-fly constraint, collision avoidance constraint, and communication constraint are considered. Four motion strategies and two communication strategies are designed. Then a distributed CCPP algorithm is designed based on hybrid strategies. Simulation results compared with patternbased genetic algorithm(PBGA) and random search method show that the proposed method has stronger real-time performance and better scalability and can complete the complete CCPP task more efficiently and stably.

Multi-Agent System for Real Time Planning Using Collaborative Agents

Autonomous agents are an important area of research in the sense that they are proactive, and include: goal-directed and communication capabilities. Furthermore each goals of the agent are constantly changing in a dynamic environment. Part of the challenge is to automate the process corresponding to each agent in order that they find their own objectives. Agents do not have to work individually, but can work with others and develop a coordinated group of actions. These agents are highly appreciated, when real time problems are involved, meaning that an agent must be able to react within a specific time interval, considering external events. Our work focuses on the design of a multi-agent architecture consisting of autonomous agents capable of acting through a goal-directed with: a) constraints, b) real-time, and c) with incomplete knowledge of the environment. This paper shows a model of collaborative agents architecture that share a common knowledge source, allowing knowledge of the environment;where we analyze it and its changes, choosing the most promising way for achieving the goals of the agent, in order to keep the whole system working, even if a fault occurs.

Communication Model for a Process Planning System Based on a Multi-agent

This paper introduces a process planning system communication model based on a Multi-agent and all levels of the communication process are in described in detail. The KQML（ Knowledge Query and Manipulation Language） language communication is introduced emphatically using the communication performatives of the KQML language to achieve communication between the agents among the process planning.

Multi-Agent Path Planning Method Based on Improved Deep Q-Network in Dynamic Environments

The multi-agent path planning problem presents significant challenges in dynamic environments,primarily due to the ever-changing positions of obstacles and the complex interactions between agents’actions.These factors contribute to a tendency for the solution to converge slowly,and in some cases,diverge altogether.In addressing this issue,this paper introduces a novel approach utilizing a double dueling deep Q-network(D3QN),tailored for dynamic multi-agent environments.A novel reward function based on multi-agent positional constraints is designed,and a training strategy based on incremental learning is performed to achieve collaborative path planning of multiple agents.Moreover,the greedy and Boltzmann probability selection policy is introduced for action selection and avoiding convergence to local extremum.To match radar and image sensors,a convolutional neural network-long short-term memory(CNN-LSTM)architecture is constructed to extract the feature of multi-source measurement as the input of the D3QN.The algorithm’s efficacy and reliability are validated in a simulated environment,utilizing robot operating system and Gazebo.The simulation results show that the proposed algorithm provides a real-time solution for path planning tasks in dynamic scenarios.In terms of the average success rate and accuracy,the proposed method is superior to other deep learning algorithms,and the convergence speed is also improved.

Game-theoretic multi-agent motion planning in a mixed environment

The motion planning problem for multi-agent systems becomes particularly challenging when humans or human-controlled robots are present in a mixed environment.To address this challenge,this paper presents an interaction-aware motion planning approach based on game theory in a receding-horizon manner Leveraging the framework provided by dynamic potential games for handling the interactions among agents,this approach formulates the multi-agent motion planning problem as a differential potential game,highlighting the effectiveness of constrained potential games in facilitating interactive motion planning among agents.Furthermore,online learning techniques are incorporated to dynamically learn the unknown preferences and models of humans or human-controlled robots through the analysis of observed data.To evaluate the effectiveness of the proposed approach,numerical simulations are conducted,demonstrating its capability to generate interactive trajectories for all agents,including humans and human-controlled agents,operating within the mixed environment.The simulation results illustrate the effectiveness of the proposed approach in handling the complexities of multi-agent motion planning in real-world scenarios.

An Improved Bounded Conflict-Based Search for Multi-AGV Pathfinding in Automated Container Terminals

As the number of automated guided vehicles(AGVs)within automated container terminals(ACT)continues to rise,conflicts have becomemore frequent.Addressing point and edge conflicts ofAGVs,amulti-AGVconflict-free path planning model has been formulated to minimize the total path length of AGVs between shore bridges and yards.For larger terminalmaps and complex environments,the grid method is employed to model AGVs’road networks.An improved bounded conflict-based search(IBCBS)algorithmtailored to ACT is proposed,leveraging the binary tree principle to resolve conflicts and employing focal search to expand the search range.Comparative experiments involving 60 AGVs indicate a reduction in computing time by 37.397%to 64.06%while maintaining the over cost within 1.019%.Numerical experiments validate the proposed algorithm’s efficacy in enhancing efficiency and ensuring solution quality.

Applications and Challenges of Deep Reinforcement Learning in Multi-robot Path Planning

With the rapid advancement of deep reinforcement learning(DRL)in multi-agent systems,a variety of practical application challenges and solutions in the direction of multi-agent deep reinforcement learning(MADRL)are surfacing.Path planning in a collision-free environment is essential for many robots to do tasks quickly and efficiently,and path planning for multiple robots using deep reinforcement learning is a new research area in the field of robotics and artificial intelligence.In this paper,we sort out the training methods for multi-robot path planning,as well as summarize the practical applications in the field of DRL-based multi-robot path planning based on the methods;finally,we suggest possible research directions for researchers.

Building Intelligent DSS Based on Multi-Agent Cooperation

A general multi-agent architecture is proposed for intelligent decision support system (MAIDSS). The agent in MAIDSS is built based on an extension of BDI framework. Several agents form a team working together on a decision problem; several agent teams are defined to stand for the benefits of different people in the real world. The decision making process is based on multi-agent cooperation, and a logical framework for a team of agents cooperating to create the solution for the decision problem is discussed in detail.

A Study on the Architecture of Flexible ERP Based on Multi-Agent Technology

Traditional ERP software system cannot efficiently su pport new management ideas such as BPR, DEM and virtual enterprise which emphasi zes that enterprise should be adjusted to market changes and business process ch ain and value chain should be integrated tightly. To solve these problems, this paper proposed the conception of Flexible ERP system. F-ERP is a self- adapti ve software system based on multi-agent technology. It developed the followin g kind of agents which are useful for F-ERP: business process agent, interf ace agent, data agent and decision and analysis agent. The F-ERP software syste m is an hierarchy system which is composed of data layer, system tools layer, bu siness application layer and business decision layer. It used component based de velopment mythology and complied with CORBA to development F-ERP. The F-ERP sy stem can support the new management ideas such as BPR, DEM and virtual enterpris e etc. By implementation of it, enterprise can improve its management and promot e its competence.

Process Planning and Scheduling Integration in an Open Manufacturing Environment

New open manufacturing environments have been proposed aiming at realizing more flexible distributed manufacturing paradigms,which can deal with not only dynamic changes in volume and variety of products,but also changes of machining equipments,dispersals of processing locations,and also with unscheduled disruptions.This research is to develop an integrated process planning and scheduling system,which is suited to this open,dynamic,distributed manufacturing environment.Multi-agent system(MAS)approaches are used for integration of manufacturing processing planning and scheduling in an open distributed manufacturing environment,in which process planning can be adjusted dynamically and manufacturing resources can increase/decrease according to the requirements.One kind of multi-level dynamic negotiated approaches to process planning and scheduling is presented for the integration of manufacturing process planning and scheduling.

A Fundamental Study on Multi-agent Pedestrian Model Based on Risk Avoidance Behavior during Road Blockage and Evacuation Simulation of Regional Urban Disaster

Regional cities in Japan are at the risk of experiencing big fire accidents or earthquakes every day.However,neither the number nor the capacity of shelters has increased because local governments might not consider them owing to budget shortfall.By contrast,wide-area evacuation simulations can easily provide an antagonizing image of regional urban disasters.After a disaster,the city collapses and the evacuation routes are closed;consequently,evacuees feel anxious and they cannot move as usual.This anxiety behavior has not been considered in previous related studies and simulations.In this study,a wide-area evacuation simulation is developed;this model can not only calculate the possibility of blocking escape routes when the city is broken but also provide safe and more realistic evacuation plans before a disaster occurs by incorporating into the simulation the risk avoidance behaviors of evacuees from road blockage,such as“the route re-seeking behavior”and“the shelter re-selecting behavior”.

Multi-Objective Loosely Synchronized Search for Multi-Objective Multi-Agent Path Finding with Asynchronous Actions

In recent years,the path planning for multi-agent technology has gradually matured,and has made breakthrough progress.The main difficulties in path planning for multi-agent are large state space,long algorithm running time,multiple optimization objectives,and asynchronous action of multiple agents.To solve the above problems,this paper first introduces the main problem of the research:multi-objective multi-agent path finding with asynchronous action,and proposes the algorithm framework of multi-objective loose synchronous(MO-LS)search.By combining A*and M*,MO-LS-A*and MO-LS-M*algorithms are respectively proposed.The completeness and optimality of the algorithm are proved,and a series of comparative experiments are designed to analyze the factors affecting the performance of the algorithm,verifying that the proposed MO-LS-M*algorithm has certain advantages.

A Distributed Cooperative Dynamic Task Planning Algorithm for Multiple Satellites Based on Multi-agent Hybrid Learning

Traditionally, heuristic re-planning algorithms are used to tackle the problem of dynamic task planning for multiple satellites. However, the traditional heuristic strategies depend on the concrete tasks, which often affect the result’s optimality. Noticing that the historical information of cooperative task planning will impact the latter planning results, we propose a hybrid learning algorithm for dynamic multi-satellite task planning, which is based on the multi-agent reinforcement learning of policy iteration and the transfer learning. The reinforcement learning strategy of each satellite is described with neural networks. The policy neural network individuals with the best topological structure and weights are found by applying co-evolutionary search iteratively. To avoid the failure of the historical learning caused by the randomly occurring observation requests, a novel approach is proposed to balance the quality and efficiency of the task planning, which converts the historical learning strategy to the current initial learning strategy by applying the transfer learning algorithm. The simulations and analysis show the feasibility and adaptability of the proposed approach especially for the situation with randomly occurring observation requests.

Cooperative planning ofmulti-agent systems based on task-oriented knowledge fusion with graph neural networks

Cooperative planning is one of the critical problems in the field of multi-agent system gaming.This work focuses on cooperative planning when each agent has only a local observation range and local communication.We propose a novel cooperative planning architecture that combines a graph neural network with a task-oriented knowledge fusion sampling method.Two main contributions of this paper are based on the comparisons with previous work:(1)we realize feasible and dynamic adjacent information fusion using GraphSAGE(i.e.,Graph SAmple and aggreGatE),which is the first time this method has been used to deal with the cooperative planning problem,and(2)a task-oriented sampling method is proposed to aggregate the available knowledge from a particular orientation,to obtain an effective and stable training process in our model.Experimental results demonstrate the good performance of our proposed method.

Coactive design of explainable agent-based task planning and deep reinforcement learning for human-UAVs teamwork

Unmanned Aerial Vehicles(UAVs)are useful in dangerous and dynamic tasks such as search-and-rescue,forest surveillance,and anti-terrorist operations.These tasks can be solved better through the collaboration of multiple UAVs under human supervision.However,it is still difficult for human to monitor,understand,predict and control the behaviors of the UAVs due to the task complexity as well as the black-box machine learning and planning algorithms being used.In this paper,the coactive design method is adopted to analyze the cognitive capabilities required for the tasks and design the interdependencies among the heterogeneous teammates of UAVs or human for coherent collaboration.Then,an agent-based task planner is proposed to automatically decompose a complex task into a sequence of explainable subtasks under constrains of resources,execution time,social rules and costs.Besides,a deep reinforcement learning approach is designed for the UAVs to learn optimal policies of a flocking behavior and a path planner that are easy for the human operator to understand and control.Finally,a mixed-initiative action selection mechanism is used to evaluate the learned policies as well as the human’s decisions.Experimental results demonstrate the effectiveness of the proposed methods.

An Agent-Based Distributed Manufacturing System

Agent theories have shown their promising capability in solving distributed complex system ever since its development. In this paper,one multi-agent based distributed product design and manufacturing planning system is presented. The objective of the research is to develop a distributed collaborative design environment for supporting cooperation among the existing engineering functions. In the system,the functional agents for design,manufacturability evaluation,process planning and scheduling are efficiently integrated with a facilitator agent. This paper firstly gives an introduction to the system structure,and the definitions for each executive agent are then described and a prototype of the proposed is also included at the end part.

Mobile sensors’patrol path planning in unobservable border region

Purpose-The border control becomes challenging when a protected region is large and there is a limited number of border patrols.This research paper proposes a novel heuristic-based patrol path planning scheme in order to efficiently patrol with resource scarcity.Design/methodology/approach-The trespasser influencing score,which is determined from the environmental characteristics and trespassing statistic of the region,is used as a heuristic for measuring a chance of approaching a trespasser.The patrol plan is occasionally updated with a new trespassing statistic during a border operation.The performance of the proposed patrol path planning scheme was evaluated and compared with other patrol path planning schemes by the empirical experiment under different scenarios.Findings-The result from the experiment indicates that the proposed patrol planning outperforms other patrol path planning schemes in terms of the trespasser detection rate,when more environment-aware trespassers are in the region.Research limitations/implications-The experiment was conducted through simulated agents in simulated environment,which were assumed to mimic real behavior and environment.Originality/value-This research paper contributes a heuristic-based patrol path planning scheme that applies the environmental characteristics and dynamic statistic of the region,as well as a border surveillance problem model that would be useful for mobile sensor planning in a border surveillance application.