Synthesizing a Lego Forklift Controller in GR(1): A Case Study

Reactive synthesis is an automated procedure to obtain a correct-by-construction reactive system from a given specification. GR(1) is a well-known fragment of linear temporal logic (LTL) where synthesis is possible using a polynomial symbolic algorithm. We conducted a case study to learn about the challenges that software engineers may face when using GR(1) synthesis for the development of a reactive robotic system. In the case study we developed two variants of a forklift controller, deployed on a Lego robot. The case study employs LTL specification patterns as an extension of the GR(1) specification language, an examination of two specification variants for execution scheduling, traceability from the synthesized controller to constraints in the specification, and generated counter strategies to support understanding reasons for unrealizability. We present the specifications we developed, our observations, and challenges faced during the case study.Comment: In Proceedings SYNT 2015, arXiv:1602.0078

Reactive mission and motion planning with deadlock resolution avoiding dynamic obstacles

In the near future mobile robots, such as personal robots or mobile manipulators, will share the workspace with other robots and humans. We present a method for mission and motion planning that applies to small teams of robots performing a task in an environment with moving obstacles, such as humans. Given a mission specification written in linear temporal logic, such as patrolling a set of rooms, we synthesize an automaton from which the robots can extract valid strategies. This centralized automaton is executed by the robots in the team at runtime, and in conjunction with a distributed motion planner that guarantees avoidance of moving obstacles. Our contribution is a correct-by-construction synthesis approach to multi-robot mission planning that guarantees collision avoidance with respect to moving obstacles, guarantees satisfaction of the mission specification and resolves encountered deadlocks, where a moving obstacle blocks the robot temporally. Our method provides conditions under which deadlock will be avoided by identifying environment behaviors that, when encountered at runtime, may prevent the robot team from achieving its goals. In particular, (1) it identifies deadlock conditions; (2) it is able to check whether they can be resolved; and (3) the robots implement the deadlock resolution policy locally in a distributed manner. The approach is capable of synthesizing and executing plans even with a high density of dynamic obstacles. In contrast to many existing approaches to mission and motion planning, it is scalable with the number of moving obstacles. We demonstrate the approach in physical experiments with walking humanoids moving in 2D environments and in simulation with aerial vehicles (quadrotors) navigating in 2D and 3D environments.Boeing CompanyUnited States. Office of Naval Research. Multidisciplinary University Research Initiative. SMARTS (N00014-09-1051)United States. Office of Naval Research (N00014-12-1-1000)National Science Foundation (U.S.). Expeditions in Computer Augmented Program Engineerin

Probabilistic Hybrid Action Models for Predicting Concurrent Percept-driven Robot Behavior

This article develops Probabilistic Hybrid Action Models (PHAMs), a realistic causal model for predicting the behavior generated by modern percept-driven robot plans. PHAMs represent aspects of robot behavior that cannot be represented by most action models used in AI planning: the temporal structure of continuous control processes, their non-deterministic effects, several modes of their interferences, and the achievement of triggering conditions in closed-loop robot plans. The main contributions of this article are: (1) PHAMs, a model of concurrent percept-driven behavior, its formalization, and proofs that the model generates probably, qualitatively accurate predictions; and (2) a resource-efficient inference method for PHAMs based on sampling projections from probabilistic action models and state descriptions. We show how PHAMs can be applied to planning the course of action of an autonomous robot office courier based on analytical and experimental results

A risk-aware architecture for resilient spacecraft operations

In this paper we discuss a resilient, risk-aware software architecture for onboard, real-time autonomous operations that is intended to robustly handle uncertainty in space-craft behavior within hazardous and unconstrained environments, without unnecessarily increasing complexity. This architecture, the Resilient Spacecraft Executive (RSE), serves three main functions: (1) adapting to component failures to allow graceful degradation, (2) accommodating environments, science observations, and spacecraft capabilities that are not fully known in advance, and (3) making risk-aware decisions without waiting for slow ground-based reactions. This RSE is made up of four main parts: deliberative, habitual, and reflexive layers, and a state estimator that interfaces with all three. We use a risk-aware goal-directed executive within the deliberative layer to perform risk-informed planning, to satisfy the mission goals (specified by mission control) within the specified priorities and constraints. Other state-of-the-art algorithms to be integrated into the RSE include correct-by-construction control synthesis and model-based estimation and diagnosis. We demonstrate the feasibility of the architecture in a simple implementation of the RSE for a simulated Mars rover scenario

Low-Effort Specification Debugging and Analysis

Reactive synthesis deals with the automated construction of implementations of reactive systems from their specifications. To make the approach feasible in practice, systems engineers need effective and efficient means of debugging these specifications. In this paper, we provide techniques for report-based specification debugging, wherein salient properties of a specification are analyzed, and the result presented to the user in the form of a report. This provides a low-effort way to debug specifications, complementing high-effort techniques including the simulation of synthesized implementations. We demonstrate the usefulness of our report-based specification debugging toolkit by providing examples in the context of generalized reactivity(1) synthesis.Comment: In Proceedings SYNT 2014, arXiv:1407.493

Qualitative Analysis of POMDPs with Temporal Logic Specifications for Robotics Applications

We consider partially observable Markov decision processes (POMDPs), that are a standard framework for robotics applications to model uncertainties present in the real world, with temporal logic specifications. All temporal logic specifications in linear-time temporal logic (LTL) can be expressed as parity objectives. We study the qualitative analysis problem for POMDPs with parity objectives that asks whether there is a controller (policy) to ensure that the objective holds with probability 1 (almost-surely). While the qualitative analysis of POMDPs with parity objectives is undecidable, recent results show that when restricted to finite-memory policies the problem is EXPTIME-complete. While the problem is intractable in theory, we present a practical approach to solve the qualitative analysis problem. We designed several heuristics to deal with the exponential complexity, and have used our implementation on a number of well-known POMDP examples for robotics applications. Our results provide the first practical approach to solve the qualitative analysis of robot motion planning with LTL properties in the presence of uncertainty

Logic programming for deliberative robotic task planning

Over the last decade, the use of robots in production and daily life has increased. With increasingly complex tasks and interaction in different environments including humans, robots are required a higher level of autonomy for efficient deliberation. Task planning is a key element of deliberation. It combines elementary operations into a structured plan to satisfy a prescribed goal, given specifications on the robot and the environment. In this manuscript, we present a survey on recent advances in the application of logic programming to the problem of task planning. Logic programming offers several advantages compared to other approaches, including greater expressivity and interpretability which may aid in the development of safe and reliable robots. We analyze different planners and their suitability for specific robotic applications, based on expressivity in domain representation, computational efficiency and software implementation. In this way, we support the robotic designer in choosing the best tool for his application

Coordination schemes for distributed boundary coverage with a swarm of miniature robots:synthesis, analysis and experimental validation

We provide a comparison of a series of original coordination mechanisms for the distributed boundary coverage problem with a swarm of miniature robots. Our analysis is based on real robot experimentation and models at different levels of abstraction. Distributed boundary coverage is an instance of the distributed coverage problem and has applications such as inspection of structures, de-mining, cleaning, and painting. Coverage is a particularly good example for the benefits of a multi-robot approach due to the potential for parallel task execution and additional robustness out of redundancy. The constraints imposed by a potential application, the autonomous inspection of a jet turbine engine, were our motivation for the algorithms considered in this thesis. Thus, there is particular emphasis on how algorithms perform under the influence of sensor and actuator noise, limited computational and communication capabilities, as well as on the policies about how to cope with such problems. The algorithms developed in this dissertation can be classified into reactive and deliberative algorithms, as well as non-collaborative and collaborative algorithms. The performance of these algorithms ranges from very low to very high, corresponding to highly redundant coverage to near-optimal partitioning of the environments, respectively. At the same time, requirements and assumptions on the robotic platform and the environment (from no communication to global communication, and from no localization to global localization) are incrementally raised. All the algorithms are robust to sensor and actuator noise and gracefully decay to the performance of a randomized algorithm as a function of an increased noise level and/or additional hardware constraints. Although the deliberative algorithms are fully deterministic, the actual performance is probabilistic due to inevitable sensor and actuator noise. For this reason, probabilistic models are used for predicting time to complete coverage and take into account sensor and actuator noise calibrated by using real hardware. For reactive systems with limited memory, the performance is captured using a compact representation based on rate equations that track the expected number of robots in a certain state. As the number of states explode for the deliberative algorithms that require a substantial use of memory, this approach becomes less tractable with the amount of deliberation performed, and we use Discrete Event System (DES) simulation in these cases. Our contribution to the domain of multi-robot systems is three-fold. First, we provide a methodology for system identification and optimal control of a robot swarm using probabilistic models. Second, we develop a series of algorithms for distributed coverage by a team of miniature robots that gracefully decay from a near-optimal performance to the performance of a randomized approach under the influence of sensor and actuator noise. Third, we design an implement a miniature inspection platform based on the miniature robot Alice with ZigBee ready communication capabilities and color vision on a foot-print smaller than 2 × 2 × 3 cm3

Motion planning and control: a formal methods approach

Control of complex systems satisfying rich temporal specification has become an increasingly important research area in fields such as robotics, control, automotive, and manufacturing. Popular specification languages include temporal logics, such as Linear Temporal Logic (LTL) and Computational Tree Logic (CTL), which extend propositional logic to capture the temporal sequencing of system properties. The focus of this dissertation is on the control of high-dimensional systems and on timed specifications that impose explicit time bounds on the satisfaction of tasks. This work proposes and evaluates methods and algorithms for synthesizing provably correct control policies that deal with the scalability problems. Ideas and tools from formal verification, graph theory, and incremental computing are used to synthesize satisfying control strategies. Finite abstractions of the systems are generated, and then composed with automata encoding the specifications. The first part of this dissertation introduces a sampling-based motion planning algorithm that combines long-term temporal logic goals with short-term reactive requirements. The specification has two parts: (1) a global specification given as an LTL formula over a set of static service requests that occur at the regions of a known environment, and (2) a local specification that requires servicing a set of dynamic requests that can be sensed locally during the execution. The proposed computational framework consists of two main ingredients: (a) an off-line sampling-based algorithm for the construction of a global transition system that contains a path satisfying the LTL formula, and (b) an on-line sampling-based algorithm to generate paths that service the local requests, while making sure that the satisfaction of the global specification is not affected. The second part of the dissertation focuses on stochastic systems with temporal and uncertainty constraints. A specification language called Gaussian Distribution Temporal Logic is introduced as an extension of Boolean logic that incorporates temporal evolution and noise mitigation directly into the task specifications. A sampling-based algorithm to synthesize control policies is presented that generates a transition system in the belief space and uses local feedback controllers to break the curse of history associated with belief space planning. Switching control policies are then computed using a product Markov Decision Process between the transition system and the Rabin automaton encoding the specification.The approach is evaluated in experiments using a camera network and ground robot. The third part of this dissertation focuses on control of multi-vehicle systems with timed specifications and charging constraints. A rich expressivity language called Time Window Temporal Logic (TWTL) that describes time bounded specifications is introduced. The temporal relaxation of TWTL formulae with respect to the deadlines of tasks is also discussed. The key ingredient of the 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. The annotated automata are composed with transition systems encoding the motion of all vehicles, and with charging models to produce control strategies for all vehicles such that the overall system satisfies the mission specification. The methods are evaluated in simulation and experimental trials with quadrotors and charging stations