# Jam Mitigation for Autonomous Convoys via Behavior-Based Robotics
###### Abstract
These behaviors are modular and run in a concurrent and distributed manner to achieve desired system functionality. The utilization of behavior-based robotics architectures in multirobot teaming has been previously explored in various research efforts seeking to take advantage of its benefits in dynamic environments. Early efforts include DARPA studies on the integration of navigational behaviors and formation behaviors to create human-led robotic teams to be used in different types of task environments [18]. This effort looked to take four robot teams and set them in a line, column, diamond, or wedge using a motor schema behavior approach. The behaviors used were move-to-goal, avoid-static-obstacle, avoid-robot, maintain-formation, and noise. Member vehicles in the robotic team transmitted position information to properly space out appropriately given the formation and method of formation centering. The path error and duration out of formation of the robots for each formation was compared to one another to determine which one had the best performance in different experimental scenarios. Another effort proposed a decentralized method utilizing a formation matrix [19]. In this effort, each robot leveraged a formation matrix, which defined leader follower pairs. This formation matrix allocated robots to specific positions in a formation before the system began operation by using onboard sensors to detect the location of surrounding robots. Once the proper position of each member robot was allocated, the system was able to traverse to a destination avoiding obstacles, while maintaining the desired formation based on what was defined in the matrix. In this approach, a behavior network architecture was used, in which basic behaviors were linked together and either stimulated or suppressed. This stimulation/suppression was based on input vector values such as detected object location. The output vectors of the network defined the robot's path. While this effort did not use communications between robotics in motion, initial allocation of vehicles in the specific starting positions was required for formation matrix setup. In addition, this effort focused on static robotic formations that maintained their shape, making this method less suitable for applications such as a convoy following defined roads. More recent efforts in behavior-based multi-robot teaming have sought to improve performance by optimizing inter-robot communications, rather than eliminating them. One such effort created a distributed framework for multirobot behavior sequencing that allowed teams of robots to adjust to their configuration to meet communication requirements for the different tasks [20]. The effort focused on the idea that coordinated behaviors between multiple robots must be sequenced together in a way that takes inter-robot distances into account to meet information flow constraints due to communications. The constraints translate into specific robot position configurations that need to be met within a finite time for the behaviors to be performed. The framework leveraged finite-time convergence control barrier functions to adjust configurations to meet the communication requirements for different sequences of behaviors. While these efforts focused on utilizing behavior-based control architectures for multi-vehicle teaming, they either heavily rely on IVC throughout their operations or require a manually arranged initial state. Furthermore, the focus on traveling in formation shapes makes the multi-vehicle movement less suitable for convoy path following operations, which is the outcome being sought by industry and military from autonomous convoying. Our efforts focus on the development of a more flexible framework towards convoy path following that utilizes on IVC when available and having the additional capability to activate more robust behaviors not relying on communications when faced with jamming to continue convoy operations. Refer to Table 1 for a summary of the control approaches referenced in this section.