Alphan Ulusoy

Wireless Model-Based Predictive Networked Control System Over Cooperative Wireless Network

Owing to their distributed architecture, networked control systems (NCSs) are proven to be feasible in scenarios where a spatially distributed feedback control system is required. Traditionally, such NCSs operate over real-time wired networks. Recently, in order to achieve the utmost flexibility, scalability, ease of deployment, and maintainability, wireless networks such as IEEE 802.11 wireless local area networks (LANs) are being preferred over dedicated wired networks. However, conventional NCSs with event-triggered controllers and actuators cannot operate over such general purpose wireless networks since the stability of the system is compromised due to unbounded delays and unpredictable packet losses that are typical in the wireless medium. Approaching the wireless networked control problem from two perspectives, this work introduces a practical wireless NCS and an implementation of a cooperative medium access control protocol that work jointly to achieve decent control under severe impairments, such as unbounded delay, bursts of packet loss and ambient wireless traffic. The proposed system is evaluated on a dedicated test platform under numerous scenarios and significant performance gains are observed, making cooperative communications a strong candidate for improving the reliability of industrial wireless networks.

Optimality and Robustness in Multi-Robot Path Planning with Temporal Logic Constraints

In this paper we present a method for automatic planning of optimal paths for a group of robots that satisfy a common high-level mission specification. The motion of each robot is modeled as a weighted transition system, and the mission is given as a linear temporal logic (LTL) formula over a set of propositions satisfied at the regions of the environment. In addition, an optimizing proposition must repeatedly be satisfied. The goal is to minimize a cost function that captures the maximum time between successive satisfactions of the optimizing proposition while guaranteeing that the formula is satisfied. When the robots can follow a given trajectory exactly, our method computes a set of optimal satisfying paths that minimize the cost function and satisfy the LTL formula. However, if the traveling times of the robots are uncertain, then the robots may not be able to follow a given trajectory exactly, possibly violating the LTL formula during deployment. We handle such cases by leveraging the communication capabilities of the robots to guarantee correctness during deployment and provide bounds on the deviation from the optimal values. We implement and experimentally evaluate our method for various persistent surveillance tasks in a road network environment.

Incremental synthesis of control policies for heterogeneous multi-agent systems with linear temporal logic specifications

We consider automatic synthesis of control policies for non-independent, heterogeneous multi-agent systems with the objective of maximizing the probability of satisfying a given specification. The specification is expressed as a formula in linear temporal logic. The agents are modeled by Markov decision processes with a common set of actions. These actions, however, may or may not affect the behaviors of all the agents. To alleviate the well-known state explosion problem, an incremental approach is proposed where only a small subset of agents is incorporated in the synthesis procedure initially and more agents are successively added until the limitations on computational resources are reached. The proposed algorithm runs in an anytime fashion, where the probability of satisfying the specification increases as the algorithm progresses.

Optimal multi-robot path planning with temporal logic constraints

In this paper we present a method for automatically planning optimal paths for a group of robots that satisfy a common high level mission specification. Each robots motion in the environment is modeled as a weighted transition system. The mission is given as a Linear Temporal Logic formula. In addition, an optimizing proposition must repeatedly be satisfied. The goal is to minimize the maximum time between satisfying instances of the optimizing proposition. Our method is guaranteed to compute an optimal set of robot paths. We utilize a timed automaton representation in order to capture the relative position of the robots in the environment. We then obtain a bisimulation of this timed automaton as a finite transition system that captures the joint behavior of the robots and apply our earlier algorithm for the single robot case to optimize the group motion. We present a simulation of a persistent monitoring task in a road network environment.

Incremental control synthesis in probabilistic environments with Temporal Logic constraints

In this paper, we present a method for optimal control synthesis of a plant that interacts with a set of agents in a graph-like environment. The control specification is given as a temporal logic statement about some properties that hold at the vertices of the environment. The plant is assumed to be deterministic, while the agents are probabilistic Markov models. The goal is to control the plant such that the probability of satisfying a syntactically co-safe Linear Temporal Logic formula is maximized. We propose a computationally efficient incremental approach based on the fact that temporal logic verification is computationally cheaper than synthesis. We present a case-study where we compare our approach to the classical non-incremental approach in terms of computation time and memory usage.

Temporal logic robot control based on automata learning of environmental dynamics

We develop a technique to automatically generate a control policy for a robot moving in an environment that includes elements with unknown, randomly changing behavior. The robot is required to achieve a surveillance mission, in which a certain request needs to be serviced repeatedly, while the expected time inbetween consecutive services is minimized and additional temporal logic constraints are satisfied. We define a fragment of linear temporal logic to describe such a mission and formulate the problem as a temporal logic game. Our approach is based on two main ideas. First, we extend results in automata learning to detect patterns of the unknown behavior of the elements in the environment. Second, we employ an automata–theoretic method to generate the control policy. We show that the obtained control policy converges to an optimal one when the partially unknown behavior patterns are fully learned. In addition, we illustrate the method in an experimental setup, in which an unmanned ground vehicle, with the help of a cooperating unmanned aerial vehicle (UAV), satisfies a temporal logic requirement in a partitioned environment whose regions are controlled by barriers with unknown behavior.

Optimal Multi-Robot Path Planning with LTL Constraints: Guaranteeing Correctness through Synchronization

In this paper, we consider the automated planning of optimal paths for a robotic team satisfying a high level mission specification. Each robot in the team is modeled as a weighted transition system where the weights have associated deviation values that capture the non-determinism in the traveling times of the robot during its deployment. The mission is given as a Linear Temporal Logic (LTL) formula over a set of propositions satisfied at the regions of the environment. Additionally, we have an optimizing proposition capturing some particular task that must be repeatedly completed by the team. The goal is to minimize the maximum time between successive satisfying instances of the optimizing proposition while guaranteeing that the mission is satisfied even under non-deterministic traveling times. After computing a set of optimal satisfying paths for the members of the team, we also compute a set of synchronization sequences for each robot to ensure that the LTL formula is never violated during deployment. We implement and experimentally evaluate our method considering a persistent monitoring task in a road network environment.

Incremental controller synthesis in probabilistic environments with temporal logic constraints

In this paper, we consider automatic computation of optimal control strategies for a robot interacting with a set of independent uncontrollable agents in a graph-like environment. The mission specification is given as a syntactically co-safe Linear Temporal Logic formula over some properties that hold at the vertices of the environment. The robot is assumed to be a deterministic transition system, while the agents are probabilistic Markov models. The goal is to control the robot such that the probability of satisfying the mission specification is maximized. We propose a computationally efficient incremental algorithm based on the fact that temporal logic verification is computationally cheaper than synthesis. We present several case studies where we compare our approach to the classical non-incremental approach in terms of computation time and memory usage.

Receding Horizon Control in Dynamic Environments from Temporal Logic Specifications

We present a control strategy for an autonomous vehicle that is required to satisfy a rich mission specification over service requests occurring at the regions of a partitioned environment. The overall mission specification consists of a temporal logic statement over a set of static, a priori known requests, and a servicing priority order over a set of dynamic requests that can be sensed locally. Our approach is based on two main steps. First, we construct an abstraction for the motion of the vehicle in the environment by using input output linearization and assignment of vector fields to the regions in the partition. Second, a receding horizon controller computes local plans within the sensing range of the vehicle such that both local and global mission specifications are satisfied. We implement and evaluate our method in an experimental setup consisting of a quadrotor performing a persistent surveillance task over a planar grid environment.

Temporal logic control for an autonomous quadrotor in a nondeterministic environment

We present an experimental setup for automatic deployment of a quadrotor in an environment with known topology and nondeterministically changing properties. The missions are specified as rich, temporal logic statements about the satisfaction of the properties. The main objective is to be able to synthesize, test, and evaluate control policies for complex aerial missions. Our testbed consists of quadrotors, a motion capture system that provides precise and continuous position information of the quadrotor, projectors that can emulate dynamically changing environments, physical obstacles, and computers that control the quadrotor, the motion capture system, and the projectors. Our computational approach is hierarchical. At the bottom level, we partition the environment and construct an abstraction in the form of a finite transition system such that the quadrotor can execute its transitions by using low level feedback controllers. At the top level, we draw inspiration from LTL model checking and use a value iteration algorithm to determine an optimal control policy that guarantees the satisfaction of the specification under nondeterministically changing properties. We illustrate the approach for the particular case of a surveillance mission in a city-like environment.