# Jam Mitigation for Autonomous Convoys via Behavior-Based Robotics
## 2 Background
### Behavior-Based Robotics  
The development of behavior-based robotics was a response to the prevalent control paradigms of the time, which were rooted in function-based, deliberative models [30], as shown in Figure 3a. Under the _deliberative approach_, robotic control relied heavily on world models. Updates to the world model would be determined in a sensing phase, plans based on the model were calculated in a planning phase, and action would then be taken in an acting phase. This approach responded poorly in dynamic environments due to the slow speed of model updates coupled with the need for re-planning [31]. To address the issues of deliberative control, researchers began to develop more _reactive approaches_ with faster response times that would be more suited to the dynamic, real-world environments. In these reactive control approaches, there is a tight coupling between sensors and actuators. Control is performed via concurrent condition-action rules so that no complex computations or world models are needed, as shown in Figure 3b. A sensor reads the environment and acts upon the information given the condition-action rules in place. The tight coupling minimizes the need for heavy computation and complex world model [31]. Despite the benefits afforded by reactive control approaches, a purely reactive approach limits an autonomous system's ability to perform complex tasks. While there are various approaches that leverage reactive control with internal states and models, one approach that is particularly well suited to dynamic environments and multi-vehicle control is behavior-based robotics. In a _behavior-based robotics approach_, robotic control is built as a collection of basic behaviors, with behaviors being defined as something that generates a motor response given some sensory stimulus. These behaviors are modular, and run in a concurrent and distributed manner to achieve desired system functionality. For instance, an autonomous vehicle that moves from its origin to some target location may be concurrently running behaviors such as Move-to-Goal, Avoid-Obstacles, and Stay-on-Path at all times in order to meet the system objective. These different behaviors may be purely reactive, in which they take sensor readings and use rule-based logic to immediately generate some desired motor response, or they can maintain their own state and memory to allow for more complex behaviors. With behaviors being run concurrently, an arbitration mechanism is used to decide on what actions to take, whether it be a single behavior's output or a fusion of multiple behaviors [32]. Arbitration techniques vary based on the specific behavior-based robotics architecture that is being used. Some examples include subsumption architectures [30],  
Figure 3: Robotic control approaches: (**a**) deliberative approach; and (**b**) reactive approach [31]. motor schemas [33], and circuit architectures [34]. Figure 4 shows an example of how a generalized behavioral-based robotics architecture can be laid out. The behavior-based robotics architecture developed for this effort was based on Motor Schemas, a neurological-based approach towards behavioral robotics inspired by schema theory [33]. A schema is a unit of behavior that describes how an agent should react in a given situation and how the reaction can be performed [32]. The concurrent control of many different schemas is used to explain motor behavior. In the Motor Schema approach, each motor schema has an associated perceptual schema that provides the sensor information, which the motor schema uses to generate motor responses per its defined behavior. This is traditionally conducted through potential field response vectors around the robot. Coordination of motor schemas is achieved cooperatively, with no hierarchy of behaviors. Instead, weightings for the motor schemas are set based on a robot's current needs and combined through vector addition.