PuRSUE -from specification of robotic environments to synthesis of controllers

Abstract

Developing robotic applications is a complex task, which requires skills that are usually only possessed by highly-qualified robotic developers. While formal methods that help developers in the creation and design of robotic applications exist, they must be explicitly customized to be impactful in the robotics domain and to support effectively the growth of the robotic market. Specifically, the robotic market is asking for techniques that: (i) enable a systematic and rigorous design of robotic applications though high-level languages; and (ii) enable the automatic synthesis of low-level controllers, which allow robots to achieve their missions. To address these problems we present the PuRSUE (Planner for RobotS in Uncontrollable Environments) approach, which aims to support developers in the rigorous and systematic design of high-level run-time control strategies for robotic applications. The approach includes PuRSUE-ML a high-level language that allows for modeling the environment, the agents deployed therein, and their missions. PuRSUE is able to check automatically whether a controller that allows robots to achieve their missions might exist and, then, it synthesizes a controller. We evaluated how PuRSUE helps designers in modeling robotic applications, the effectiveness of its automatic computation of controllers, and how the approach supports the deployment of controllers on actual robots. The evaluation is based on 13 scenarios derived from 3 different robotic applications presented in the literature. The results show that: (i) PuRSUE-ML is effective in supporting designers in the formal modeling of robotic applications compared to a direct encoding of robotic applications in low-level modeling formalisms; (ii) PuRSUE enables the automatic generation of controllers that are difficult to create manually; and (iii) the plans generated with PuRSUE are indeed effective when deployed on actual robots.

Sound Development of Safety Supervisors

“…The approaches in [27,3] from collaborative robotics are perhaps closest to our workflow (P 1 ) as they include platform deployment (P 3 ). While they focus on the synthesis of overall robot controllers, we focus on supervisors but with a testing stage reassuring implementation correctness.…”

“…Given α : S → 2 S for α ∈ A W , by inductively applying W to an initial state s 0 ∈ S, we obtain an MDP W and the set S ⊆ S of states reachable by W(s 0 ). 3 S is labeled with atomic propositions. W is a labeled transition system whose transition relation encodes non-deterministic and probabilistic choice.…”

Complete Test of Synthesised Safety Supervisors for Robots and Autonomous Systems

“…Related Work. In the rich body of literature on verified controller synthesis, the approaches in [21,3] from collaborative robotics are perhaps closest to the one presented here as they include a platform deployment stage. While these authors focus on the synthesis of complete robot controllers, our approach focuses on safety supervisors but includes a testing step reassuring the correctness of platform code generation.…”

“…The discussed features are available in YAP version 0.8+, see https://yap.gleirscher.at 3. Field-programmable gate array (FPGA); VHSIC or Verilog hardware description language (VHDL or Verilog HDL)…”

How to Formally Model Human in Collaborative Robotics

“…Formal methods have been widely used in robotics for decades in a variety of applications including mission planning [6,20,46,53,63], formal verification of properties [3,48,49,55], and controllers [8,24,66]. They could be an effective means for the safety analysis of traditional and collaborative robotics due to their comprehension and exhaustiveness [32,77,78].…”