14 research outputs found
Symmetry Reduction Enables Model Checking of More Complex Emergent Behaviours of Swarm Navigation Algorithms
The emergent global behaviours of robotic swarms are important to achieve
their navigation task goals. These emergent behaviours can be verified to
assess their correctness, through techniques like model checking. Model
checking exhaustively explores all possible behaviours, based on a discrete
model of the system, such as a swarm in a grid. A common problem in model
checking is the state-space explosion that arises when the states of the model
are numerous. We propose a novel implementation of symmetry reduction, in the
form of encoding navigation algorithms relatively with respect to a reference,
based on the symmetrical properties of swarms in grids. We applied the relative
encoding to a swarm navigation algorithm, Alpha, modelled for the NuSMV model
checker. A comparison of the state-space and verification results with an
absolute (or global) and a relative encoding of the Alpha algorithm highlights
the advantages of our approach, allowing model checking larger grid sizes and
number of robots, and consequently, verifying more complex emergent behaviours.
For example, a property was verified for a grid with 3 robots and a maximum
allowed size of 8x8 cells in a global encoding, whereas this size was increased
to 16x16 using a relative encoding. Also, the time to verify a property for a
swarm of 3 robots in a 6x6 grid was reduced from almost 10 hours to only 7
minutes. Our approach is transferable to other swarm navigation algorithms.Comment: Accepted for presentation in Towards Autonomous Robotic Systems
(TAROS) 2015, Liverpool, U
Risk-Based Triggering of Bio-inspired Self-preservation to Protect Robots from Threats
Safety in autonomous systems has been mostly studied from a human-centered
perspective. Besides the loads they may carry, autonomous systems are also
valuable property, and self-preservation mechanisms are needed to protect them
in the presence of external threats, including malicious robots and
antagonistic humans. We present a biologically inspired risk-based triggering
mechanism to initiate self-preservation strategies. This mechanism considers
environmental and internal system factors to measure the overall risk at any
moment in time, to decide whether behaviours such as fleeing or hiding are
necessary, or whether the system should continue on its task. We integrated our
risk-based triggering mechanism into a delivery rover that is being attacked by
a drone and evaluated its effectiveness through systematic testing in a
simulated environment in Robot Operating System (ROS) and Gazebo, with a
variety of different randomly generated conditions. We compared the use of the
triggering mechanism and different configurations of self-preservation
behaviours to not having any of these. Our results show that triggering
self-preservation increases the distance between the drone and the rover for
many of these configurations, and, in some instances, the drone does not catch
up with the rover. Our study demonstrates the benefits of embedding risk
awareness and self-preservation into autonomous systems to increase their
robustness, and the value of using bio-inspired engineering to find solutions
in this area
Controlador neuro-difuso para aplicaciones colaborativas simples en robótica
El proyecto comprende lograr implementar un controlador neuro-difuso para ser aplicado en tareas que involucren el movimiento colaborativo de un conjunto de micro-robots que intercambien información, mediante el uso y desarrollo de algoritmos
Verification of Control Systems Implemented in Simulink with Assertion Checks and Theorem Proving: A Case Study
This paper presents the verification of control systems implemented in
Simulink. The goal is to ensure that high-level requirements on control
performance, like stability, are satisfied by the Simulink diagram. A two stage
process is proposed. First, the high-level requirements are decomposed into
specific parametrized sub-requirements and implemented as assertions in
Simulink. Second, the verification takes place. On one hand, the
sub-requirements are verified through assertion checks in simulation. On the
other hand, according to their scope, some of the sub-requirements are verified
through assertion checks in simulation, and others via automatic theorem
proving over an ideal mathematical model of the diagram. We compare performing
only assertion checks against the use of theorem proving, to highlight the
advantages of the latter. Theorem proving performs verification by computing a
mathematical proof symbolically, covering the entire state space of the
variables. An automatic translation tool from Simulink to the language of the
theorem proving tool Why3 is also presented. The paper demonstrates our
approach by verifying the stability of a simple discrete linear system.Comment: Accepted, waiting for publication. European Control Conference, July
2015, Linz, Austri