Formal methods for controlling networked systems

• Published in: 2015 International Conference on Complex Systems Engineering (ICCSE) pp. 1 - 2
• Main Author: Belta, Calin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.11.2015
• Subjects: Adaptive control; Automobiles; Collision avoidance; Fuses; Planning; Roads; Robots
• DOI: 10.1109/ComplexSys.2015.7385982

Summary form only given. The goal in robot motion planning and control is to be able to specify a motion task in a rich, high level language and have the robot(s) automatically convert this specification into a set of low level primitives, such as feedback controllers and communication protocols, to accomplish the task. In most of the existing works, the motion planning problem is simply specified as "go from A to B while avoiding obstacles". However, there are many situations in which this is not enough to capture the nature of the task. Consider, for example, the miniature Robotic Urban-Like Environment (RULE) shown in Fig. 1, where a robot might be required to "Visit Road R1 or Road R2 without crossing Intersection I3, and then park in an available parking space," while at same time obeying the traffic rules. Such a "rich" specification cannot be trivially converted to a sequence of "go from A to B" primitives. When several robots are available, the problem becomes even more interesting and challenging. Assume that several service requests occur at different locations in the city, and they need to be serviced subject to some temporal and logical constraints. Some of these requests can be serviced by one (possibly specific) robot, while others require the collaboration of two or more (possibly specific) robots. For example, assume that the task is to first gather two pieces of data, one of which is available at P3 only, and the other at either P4 or P5, and and then fuse and transmit the data at one of the transmission locations P1 or P2. Assume that two robotic cars C1and C2 are available; only C1 can read the data at P4, and both cars are necessary to fuse and transmit the data. It has been recently advocated that temporal logics, such as Linear Temporal Logic (LTL) and Computation Tree Logic (CTL), can be used as "rich" specification languages in mobile robotics. Most existing works suggest that the corresponding formal verification (model checking) algorithms can be adapted for motion planning and controller synthesis from such specifications. Some related works show that such techniques can be extended to multi-agent systems through the use of parallel composition. However, such bottom-up approaches are expensive and can lead to state-space explosion even for relatively simple problems. As a result, one of the main challenges in the area of motion planning and control of distributed teams based on formal verification is to create provably-correct, top-down approaches in which a global, "rich" specification can be decomposed into local (individual) specifications, which can then be used to automatically synthesize robot control and communication strategies. In such a framework, the construction of the parallel composition of the individual motions is not necessary, and therefore the state-space explosion problem is avoided. In [1], we draw inspiration from the area of distributed formal synthesis to develop such a top-down approach. We consider a team of robots that can move among the regions of a partitioned environment, and which have known capabilities of servicing a set of requests that can occur in the regions of the partition. Some of these requests can be serviced by a robot individually, while some require the cooperation of groups of robots. We present an algorithm that allows for the fully automatic synthesis of robot control and communication strategies from a task specification given as a regular expression over the set of requests. For simplicity of presentation, we model the (partitioned) environment as a graph and the robots as agents that can move between adjacent vertices and can communicate only when at particular vertices. This framework is quite general and can be used in conjunction with cell decomposition motion planning techniques. In particular, by using feedback controllers for facet reachability in polytopes, this scenario can be extended to robots with continuous dynamics moving in environments with polytopic partitions. A related problem is considered in [2], where a group of robots are tasked with conducting a mission while gathering information about a large environment. For example, a group of robots needs to autonomously tend to an agricultural field. The robots must irrigate crops and apply pesticide (the "mission"), while simultaneously monitoring for crop damage (the "information gathering"). In order to complete this mission, the agents have to satisfy certain motion constraints such as "Always avoid obstacles" and "Visit a centralized station to upload gathered data." Constraints on the mission might require agents to cooperate or perform tasks in a certain order, e.g. harvest grain before depositing grain at a silo. Additionally, the agents face the dual constraints of spreading out to explore the environment while also communicating effectively with each other to share gathered information and ensure cooperative tasks are fulfilled. The motion and communication constraints can naturally be described by a temporal logic formula. We use the framework described above (see [1]) to distribute the temporal logic formula among sub-teams of the agents such that if each sub-team satisfies its individual formula, the global constraints are satisfied. Once a sub-team has been assigned an individual mission, it executes a computationally efficient receding horizon planner that locally maximizes the amount of information gained and is guaranteed to satisfy the individual mission. When compared to [1], the framework from [2] also allows for more typical and more restrictive communication constraints based on agents' distance from each other in the environment. It also allows the agents to act according to reactive control policies rather than follow pre-specified paths, giving the agents a greater degree of flexibility in conducting their mission.