Publications

This paper presents a comprehensive approach for planning for teams of heterogeneous robots with different capabilities and the transportation of resources. We use Capability Temporal Logic (CaTL), a formal language that helps express tasks involving robots with multiple capabilities with spatial, temporal, and logical constraints. We extend CaTL to also capture resource constraints, where resources can be divisible and indivisible, for instance, sand and bricks, respectively. Robots transport resources using various storage types, such as uniform (shared storage among resources) and compartmental (individual storage per resource). Robots' resource transportation capacity is defined based on resource type and robot class. Robot and resource dynamics and the CaTL mission are jointly encoded in a Mixed Integer Linear Programming (MILP), which maximizes disjoint robot and resource robustness while minimizing spurious movement of both. We propose a multi-robustness approach for Multi-Class Signal Temporal Logic (mcSTL), allowing for generalized quantitative semantics across multiple predicate classes. Thus, we compute availability robustness scores for robots and resources separately. Finally, we conduct multiple experiments demonstrating functionality and time performance by varying resources and storage types.

Many existing approaches for coordinating heterogeneous teams of robots either consider small numbers of agents, are application-specific, or do not adequately address common real-world requirements, e.g., strict deadlines or intertask dependencies. We introduce scalable and robust algorithms for task-based coordination from high-level specifications (ScRATCHeS) to coordinate such teams. We define a specification language, capability temporal logic, to describe rich, temporal properties involving tasks requiring the participation of multiple agents with multiple capabilities, e.g., sensors or end effectors. Arbitrary missions and team dynamics are jointly encoded as constraints in a mixed integer linear program, and solved efficiently using commercial off-the-shelf solvers. ScRATCHeS optionally allows optimization for maximal robustness to agent attrition at the penalty of increased computation time. We include an online replanning algorithm that adjusts the plan after an agent has dropped out. The flexible specification language, fast solution time, and optional robustness of ScRATCHeS provide a first step toward a multipurpose on-the-fly planning tool for tasking large teams of agents with multiple capabilities enacting missions with multiple tasks. We present randomized computational experiments to characterize scalability and hardware demonstrations to illustrate the applicability of our methods.

We focus on decomposing large multi-agent path planning problems with global temporal logic goals (common to all agents) into smaller sub-problems that can be solved and executed independently. Crucially, the sub-problems' solutions must jointly satisfy the common global mission specification. The agents' missions are given as Capability Temporal Logic (CaTL) formulas, a fragment of Signal Temporal Logic (STL) that can express properties over tasks involving multiple agent capabilities (i.e., different combinations of sensors, effectors, and dynamics) under strict timing constraints. The approach we take is to jointly decompose both the temporal logic specification and the team of agents, using a satisfiability modulo theories (SMT) approach and heuristics for handling temporal operators. The output of the SMT is then distributed to subteams and leads to a significant speed up in planning time compared to planning for the entire team and specification. We include computational results to evaluate the efficiency of our solution, as well as the trade-offs introduced by the conservative nature of the SMT encoding and heuristics.

We extend Signal Temporal Logic (STL) to enable the specification of importance and priorities. The extension, called Weighted STL (wSTL), has the same qualitative (Boolean) semantics as STL, but additionally defines weights associated with Boolean and temporal operators that modulate its quantitative semantics (robustness). We show that the robustness of wSTL can be defined as weighted generalizations of all known compatible robustness functionals (i.e., robustness scores that are recursively defined over formulae) that can take into account the weights in wSTL formulae. We utilize this weighted robustness to distinguish signals with respect to a desired wSTL formula that has subformulae with different importance or priorities and time preferences, and demonstrate its usefulness in problems with conflicting tasks where satisfaction of all tasks cannot be achieved. We also employ wSTL robustness in an optimization framework to synthesize controllers that maximize satisfaction of a specification with user specified preferences.

In this work, we propose a novel approach for integrating rules into traffic agent trajectory prediction. Consideration of rules is important for understanding how people behave-yet, it cannot be assumed that rules are always followed. To address this challenge, we evaluate different approaches of integrating rules as inductive biases into deep learning-based prediction models. We propose a framework based on generative adversarial networks that uses tools from formal methods, namely signal temporal logic and syntax trees. This allows us to leverage information on rule obedience as features in neural networks and improves prediction accuracy without biasing towards lawful behavior. We evaluate our method on a real-world driving dataset and show improvement in performance over off-the-shelf predictors.

We present a solution for operating a vehicle without global positioning infrastructure while satisfying constraints on its temporal behavior, and on the uncertainty of its position estimate. The proposed solution is an end-to-end framework for mapping an unknown environment using aerial vehicles, synthesizing a control policy for a ground vehicle in that environment, and using a quadrotor to localize the ground vehicle within the map while it executes its control policy. This vision-based localization is noisy, necessitating planning in the belief space of the ground robot. The ground robot's mission is given using a language called Gaussian Distribution Temporal Logic (GDTL), an extension of Boolean logic that incorporates temporal evolution and noise mitigation directly into the task specifications. We use a sampling-based algorithm to generate a transition system in the belief space and use local feedback controllers to break the curse of history associated with belief space planning. To localize the vehicle, we build a high-resolution map of the environment by flying a team of aerial vehicles in formation with sensor information provided by their onboard cameras. The control policy for the ground robot is synthesized under temporal and uncertainty constraints given the semantically labeled map. Then the ground robot can execute the control policy given pose estimates from a dedicated aerial robot that tracks and localizes the ground robot. The proposed method is validated using two quadrotors to build a map, followed by a two-wheeled ground robot and a quadrotor with a camera for ten successful experimental trials.

This paper introduces time window temporal logic (TWTL), a rich expressivity language for describing various time bounded specifications. In particular, the syntax and semantics of TWTL enable the compact representation of serial tasks, which are prevalent in various applications including robotics, sensor systems, and manufacturing systems. This paper also discusses the relaxation of TWTL formulae with respect to the deadlines of the tasks. Efficient automata-based frameworks are presented to solve synthesis, verification and learning problems. The key ingredient to the presented solution is an algorithm to translate a TWTL formula to an annotated finite state automaton that encodes all possible temporal relaxations of the given formula. Some case studies are presented to illustrate the expressivity of the logic and the proposed algorithms.

In this work, we present a novel method for automating persistent surveillance missions involving multiple vehicles. Automata-based techniques are used to generate collision-free motion plans for a team of vehicles to satisfy a temporal logic specification. Vector fields are created for use with a differential flatness-based controller, allowing vehicle flight and deployment to be fully automated according to the motion plans. The use of charging platforms with the vehicles allows for truly persistent missions. Experiments were performed with two quadrotors for two different missions over 50 runs each to validate the theoretical results.

We study the computing power of a class of numerical P systems introduced in the framework of autonomous robot control, namely enzymatic numerical P systems. Three ways of using the evolution programs are investigated: sequential, all-parallel and one-parallel (with the same variable used in all programs or in only one, respectively); moreover, both deterministic and non-deterministic systems are considered. The Turing universality of some of the obtained classes of numerical P systems is proved (for polynomials with the smallest possible degree, one, also introducing a new proof technique in this area, namely starting the universality proof from the characterization of computable sets of numbers by means of register machines). The power of many other classes remains to be investigated.

Background

This paper presents PyElph, a software tool which automatically extracts data from gel images, computes the molecular weights of the analyzed molecules or fragments, compares DNA patterns which result from experiments with molecular genetic markers and, also, generates phylogenetic trees computed by five clustering methods, using the information extracted from the analyzed gel image. The software can be successfully used for population genetics, phylogenetics, taxonomic studies and other applications which require gel image analysis. Researchers and students working in molecular biology and genetics would benefit greatly from the proposed software because it is free, open source, easy to use, has a friendly Graphical User Interface and does not depend on specific image acquisition devices like other commercial programs with similar functionalities do.

Results

PyElph software tool is entirely implemented in Python which is a very popular programming language among the bioinformatics community. It provides a very friendly Graphical User Interface which was designed in six steps that gradually lead to the results. The user is guided through the following steps: image loading and preparation, lane detection, band detection, molecular weights computation based on a molecular weight marker, band matching and finally, the computation and visualization of phylogenetic trees. A strong point of the software is the visualization component for the processed data. The Graphical User Interface provides operations for image manipulation and highlights lanes, bands and band matching in the analyzed gel image. All the data and images generated in each step can be saved. The software has been tested on several DNA patterns obtained from experiments with different genetic markers. Examples of genetic markers which can be analyzed using PyElph are RFLP (Restriction Fragment Length Polymorphism), AFLP (Amplified Fragment Length Polymorphism), RAPD (Random Amplification of Polymorphic DNA) and STR (Short Tandem Repeat). The similarity between the DNA sequences is computed and used to generate phylogenetic trees which are very useful for population genetics studies and taxonomic classification.

Conclusions

PyElph decreases the effort and time spent processing data from gel images by providing an automatic step-by-step gel image analysis system with a friendly Graphical User Interface. The proposed free software tool is suitable for researchers and students which do not have access to expensive commercial software and image acquisition devices.