###### Abstract
In addition, when the regional decision-making exchange information is completed based on their respective local information, a rule that can achieve internal consensus decision-making is required to obtain the local optimal solution. This is also a topic that needs to be addressed in decentralized decision-making, i.e., how to achieve the best communication and cost efficiency with limited resources in a complex and unknown environment. It is a challenge to avoid collisions among each other, especially when performing multi-agent cooperative tasks. They must also be able to dodge the obstacles in the external environment automatically. A complete set of movement path simulation software will need to integrate with the movement map to include natural landscape terrain, information of buildings and major objects [7]. The automatic task allocation problem is one of the challenging problems of a multi-agent system as well. Task allocation is defined as assigning several tasks to several agents and the agents have to carry out those assigned tasks. It is a general form of assignment problem, which belongs to fundamental combination optimization in mathematics. Each agent may have different capabilities and cost to perform different tasks. Each task must be assigned to at least one agent to be carried out. The cost of the problem depends highly on the outcome of the agent-task assignment. It cannot easily reach a consensus to an optimal cost solution within feasible time-bound by a team of fast-moving agents. Many novel computer algorithms have developed in recent years to deal with the above problems. The traditional path planning problem combined with the application of multi-agent has also been updated in many new algorithms, such as artificial immune algorithm [8], genetic algorithm [9], random algorithm [10] and other combinations. Deep learning [11], flock of birds or a school of fish algorithm [12], dynamic windows approach [13], Consensus-Base Auction Algorithm (CBAA) [14] and Consensus-Based Bundle Algorithm (CBBA) [15] are dedicated to path optimization multi-agent obstacle avoidance and collaborative task allocation comprehensive algorithms. The research clusters can mainly be divided into two types: centralized and decentralized. Each has its own advantages and disadvantages. The former requires a leader (decision-making) and a centralized system to collect all evidence to complete the final decision. The advantages are faster decision-making and easier to get the best solution in the whole domain. The disadvantage is that once the number of groups and external variation are large and the amount of information is large, the decision-making speed and efficiency may slow down. Furthermore, once a problem occurs in the centralized system network or the decision-maker suffers in the event of an intrusion or attack, the followers or the entire system may break down. In this research, we proposed a method of using distributed multi-agent systems to achieve the autonomous path planning, obstacle avoidance and dynamic task allocation with minimum cost and maximal reward. The proposed method allows the multi-agent system to bid task (target) and thus divide into subgroups and then plan a trajectory to avoid obstacles autonomously. This is especially important for military tactical and strategic applications. Multi-agent systems need the ability to process the collected data, coordinate via communication, make decision autonomously based on the assigned missions and changing environmental situations. This requires the agents to reach agreements and make decisions in real-time [16]. One important factor in increasing efficiency and effectiveness is to group agents into several compacted subgroups. The compacted group can avoid detection by the enemy and increase the information security during the movement mission. Therefore, we add compactness as one of the requirements into the obstacle avoidance algorithm in terms of cost. In summary, the overall goals of our multi-agent system are [17]:  
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