Yushan Chen

Formal Approach to the Deployment of Distributed Robotic Teams

We present a computational framework for automatic synthesis of control and communication strategies for a robotic team from task specifications that are given as regular expressions about servicing requests in an environment. We assume that the location of the requests in the environment and the robot capacities and cooperation requirements to service the requests are known. Our approach is based on two main ideas. First, we extend recent results from formal synthesis of distributed systems to check for the distributability of the task specification and to generate local specifications, while accounting for the service and communication capabilities of the robots. Second, by using a technique that is inspired by linear temporal logic model checking, we generate individual control and communication strategies. We illustrate the method with experimental results in our robotic urban-like environment.

Automatic Deployment of Robotic Teams

A major goal in robot motion planning and control is to be able to specify a task in a high-level, expressive language and have the robot(s) to automatically convert the specification into a set of low-level primitives, such as feedback controllers and communication protocols, to accomplish the task. The robots can vary from manipulator arms used in manufacturing or surgery, to autonomous vehicles used in search and rescue or in planetary exploration, and to smart wheel chairs for disabled people. They are subject to mechanical constraints (e.g., a carlike robot cannot move sideways,an airplane cannot stop in place) and have limited computation, sensing, and communication capabilities. The environments can be cluttered with possibly moving and shape-changing obstacles and can con tain dynamic (moving, appearing, or disappearing) targets. One of the major challenges in this area is the development of a computationally efficient frame work accommodating both the robot constraints and the complexity of the environment, while, at the same time, allowing for a large spectrum of task specifications.

LTL robot motion 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 partially unknown, changing behavior. The robot is required to achieve an optimal surveillance mission, in which a certain request needs to be serviced repeatedly, while the expected time in between consecutive services is minimized. We define a fragment of Linear Temporal Logic (LTL) 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 partially 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 unknown behavior patterns are fully learned. We implemented the proposed computational framework in MATLAB. Illustrative case studies are included.

Automatic Sequencing of Ballet Poses

In this article, we draw inspiration from the formal principals of movement organization in basic classical ballet. A grammar for leg positions in ballet movements restricted to the coronal plane is specified. Ballet is a highly ordered behavior of a truly complex biological system whose attributes have important analogs in systems theory that warrant quantitative study. By formulating aesthetic style from a systems theoretic perspective and, thus, resolving the attributes of human movement that typify and comprise stylized movement, we are beginning to define a metric for a previously abstract concept. Furthermore, the structure of the aesthetic movement explored here provides an interesting challenge for robotics research and formal methods, namely that of how the composition of structured discrete event systems may generate desired behavior for humanoid robotic tasks.

Automatic Generation of Balletic Motions

As cyber-physical systems become more prevalent, specifications for these systems must be formulated in a more nuanced manner. This paper presents a particular instantiation of such specification by proposing a framework that endows robotic motions with a sense of aesthetic style. Drawing inspiration from classical ballet, poses are cast as discrete states and movements as the transitions between these states. Thus, a given movement style is encoded in the availability of transitions at each state, and the dynamics of a complex physical trajectory are abstracted as a system which moves between these states. Using Linear Temporal Logic (LTL), we are able to further constrain the set of possible sequences through the transition system and thus prevent it from evolving through a sequence of states that is physically impossible or aesthetically undesirable. Our overarching objective is to facilitate subtle degrees of control over systems as such subtleties are required, more and more, to interact in a social and aesthetically driven world.

Synthesis of distributed control and communication schemes from global LTL specifications

We introduce a technique for synthesis of control and communication strategies for a team of agents from a global task specification given as a Linear Temporal Logic (LTL) formula over a set of properties that can be satisfied by the agents. We consider a purely discrete scenario, in which the dynamics of each agent is modeled as a finite transition system. The proposed computational framework consists of two main steps. First, we extend results from concurrency theory to check whether the specification is distributable among the agents. Second, we generate individual control and communication strategies by using ideas from LTL model checking. We apply the method to automatically deploy a team of miniature cars in our Robotic Urban-Like Environment.

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.

Multi-agent persistent monitoring in stochastic environments with temporal logic constraints

In this paper, we consider the problem of generating control policies for a team of robots moving in an environment containing elements with probabilistic behaviors. The team is required to achieve an optimal surveillance mission, in which a certain proposition needs to be satisfied infinitely often. The goal is to minimize the average time between satisfying instances of the proposition, while ensuring that the mission is accomplished. By modeling the robots as Transition Systems and the environmental elements as Markov Chains, the problem reduces to finding an optimal control policy satisfying a temporal logic specification on a Markov Decision Process. The existing approaches for this problem are computational intensive and therefore not feasible for a large environment or a large number of robots. To address this issue, we propose an approximate dynamic programming framework. Specifically, we choose a set of basis functions to approximate the optimal cost and find the best parameters for these functions based on the least-square approximation. We develop an approximate policy iteration algorithm to implement our framework. We provide illustrative case studies and evaluate our method through simulations.

A hierarchical approach to automatic deployment of robotic teams with communication constraints

We consider the following problem: GIVEN (1) a set of service requests occurring at known locations in an environment, (2) a set of temporal and logical constraints on how the requests need to be serviced, (3) a team of robots and their capacities to service the requests individually or through collaboration, FIND robot control and communication strategies guaranteeing the correct servicing of the requests. Our approach is hierarchical. At the top level, we check whether the specification, which is a regular expression over the requests, is distributable among the robots given their service and cooperation capabilities; if the answer is positive, we generate individual specifications in the form of finite state automata, and interaction rules in the form of synchronizations on shared requests. At the bottom level, we check whether the local specifications and the synchronizations can be implemented given the motion and communication constraints of the robots; if the answer is positive, we generate robot motion and service plans, which are then mapped to control and communication strategies. We illustrate the method with experimental and simulation results.

An Approximate Dynamic Programming Approach to Multiagent Persistent Monitoring in Stochastic Environments With Temporal Logic Constraints

We consider the problem of generating control policies for a team of robots moving in a stochastic environment. The team is required to achieve an optimal surveillance mission, in which a certain “optimizing proposition” needs to be satisfied infinitely often. In addition, a correctness requirement expressed as a temporal logic formula is imposed. By modeling the robots as game transition systems and the environmental elements as Markov chains, the problem reduces to finding an optimal control policy for a Markov decision process, which also satisfies a temporal logic specification. The existing approaches based on dynamic programming are computationally intensive, thus not feasible for large environments and/or large numbers of robots. We propose an approximate dynamic programming (ADP) framework to obtain suboptimal policies with reduced computational complexity. Specifically, we choose a set of basis functions to approximate the optimal costs and find the best approximation through the least-squares method. We also propose a simulation-based ADP approach to further reduce the computational complexity by employing low-dimensional calculations and simulation samples.