###### Abstract  
The autonomous vehicle technology has recently been developed rapidly in a wide variety of applications. However, coordinating a team of autonomous vehicles to complete missions in an unknown and changing environment has been a challenging and complicated task. We modify the consensus-based auction algorithm (CBAA) so that it can dynamically reallocate tasks among autonomous vehicles that can flexibly find a path to reach multiple dynamic targets while avoiding unexpected obstacles and staying close as a group as possible simultaneously. We propose the core algorithms and simulate with many scenarios empirically to illustrate how the proposed framework works. Specifically, we show that how autonomous vehicles could reallocate the tasks among each other in finding dynamically changing paths while certain targets may appear and disappear during the movement mission. We also discuss some challenging problems as a future work. autonomous vehicles; consensus decision making; task reallocation; team formation; obstacle avoidance path planning; auction mechanism +
Footnote †: journal: Applied Sciences  
1
Footnote 1: email: _Applied_  
27 May 2021  
Footnote †: journal: Applied Sciences  
1
Footnote 1: email: _Applied_  
Footnote †: journal: Applied Sciences  
without special mention in many situations of the paper. An agent could be a vehicle, a robot or an UAV operating in a 2D situation. A MAS is a system that adopts multiple interacting intelligent agents to simulate distributed decision making as a coordinated group to solve a complex problem. MAS becomes popular and important because of the following advantages but not limited to:  
1. Easy to scale up to deal with complex problems: A single agent can only execute a limited function and task. In a more complicated situation with multiple tasks, MAS is more suitable to fulfill the required demands. 2. Increase the efficiency: The required task can be completed faster with MAS than a single agent system because multiple agents can solve several tasks in parallel rather than execute the tasks in sequence. 3. Improve the reliability: A malfunction of a single agent system results from the mission delay or failure. However, a MAS executes tasks cooperatively as a group. The effective collaboration and synchronization of multiple agents can construct a more reliable system even when partial agents fail. 4. Save the cost: It is cheaper and easier to implement a batch of simple systems than to build a complicated multi-functional system. It is in a divide-and-conquer strategy than implementing a closely coupled complex system. The maintenance cost is also much less. However, there are still many challenges in building an applicable and robust MAS for real applications, particularly in autonomous multi-vehicle problems. The challenges include task allocation, group formation, cooperative object detection and tracking, path and trajectory planning, collision avoidance and much more in a complicated dynamic environment and task requirements [3]. For example, [PERSON] et al. [4] point out unpredictable weather and energy consumption will post challenges to the routing and scheduling of UAVs to deliver goods from a pot to customer locations. In the study of [PERSON] et al. [5], time for robotic planning (path planning, task planning, and mission planning) during SWAT action is an critical factor for the mission. Based on the structure of decision-making, an autonomous multi-vehicle system can be categorized from centralized to distributed [6]. In this paper, we model our system as a distributed MAS which does not require a central decision maker. Each agent can make a local optimal decision based on its own environment and goals. It does not rely on complete global information across the entire domain. However, each agent can make an optimal decision and action based on local information and communication with neighbors and achieve collective behaviors that benefit as a whole. It has the advantages of high stability and flexibility.