2,657 research outputs found
Probabilistic Model Checking of Robots Deployed in Extreme Environments
Robots are increasingly used to carry out critical missions in extreme
environments that are hazardous for humans. This requires a high degree of
operational autonomy under uncertain conditions, and poses new challenges for
assuring the robot's safety and reliability. In this paper, we develop a
framework for probabilistic model checking on a layered Markov model to verify
the safety and reliability requirements of such robots, both at pre-mission
stage and during runtime. Two novel estimators based on conservative Bayesian
inference and imprecise probability model with sets of priors are introduced to
learn the unknown transition parameters from operational data. We demonstrate
our approach using data from a real-world deployment of unmanned underwater
vehicles in extreme environments.Comment: Version accepted at the 33rd AAAI Conference on Artificial
Intelligence, Honolulu, Hawaii, 201
Model checking learning agent systems using Promela with embedded C code and abstraction
As autonomous systems become more prevalent, methods for their verification will become more
widely used. Model checking is a formal verification technique that can help ensure the safety of autonomous
systems, but in most cases it cannot be applied by novices, or in its straight \off-the-shelf" form. In order
to be more widely applicable it is crucial that more sophisticated techniques are used, and are presented
in a way that is reproducible by engineers and verifiers alike. In this paper we demonstrate in detail two
techniques that are used to increase the power of model checking using the model checker SPIN. The first
of these is the use of embedded C code within Promela specifications, in order to accurately re
ect robot
movement. The second is to use abstraction together with a simulation relation to allow us to verify multiple
environments simultaneously. We apply these techniques to a fairly simple system in which a robot moves
about a fixed circular environment and learns to avoid obstacles. The learning algorithm is inspired by the
way that insects learn to avoid obstacles in response to pain signals received from their antennae. Crucially,
we prove that our abstraction is sound for our example system { a step that is often omitted but is vital if
formal verification is to be widely accepted as a useful and meaningful approach
Bayesian Learning for the Robust Verification of Autonomous Robots
Autonomous robots used in infrastructure inspection, space exploration and
other critical missions operate in highly dynamic environments. As such, they
must continually verify their ability to complete the tasks associated with
these missions safely and effectively. Here we present a Bayesian learning
framework that enables this runtime verification of autonomous robots. The
framework uses prior knowledge and observations of the verified robot to learn
expected ranges for the occurrence rates of regular and singular (e.g.,
catastrophic failure) events. Interval continuous-time Markov models defined
using these ranges are then analysed to obtain expected intervals of variation
for system properties such as mission duration and success probability. We
apply the framework to an autonomous robotic mission for underwater
infrastructure inspection and repair. The formal proofs and experiments
presented in the paper show that our framework produces results that reflect
the uncertainty intrinsic to many real-world systems, enabling the robust
verification of their quantitative properties under parametric uncertainty.Comment: Accepted by Communications Engineerin
Combining Subgoal Graphs with Reinforcement Learning to Build a Rational Pathfinder
In this paper, we present a hierarchical path planning framework called SG-RL
(subgoal graphs-reinforcement learning), to plan rational paths for agents
maneuvering in continuous and uncertain environments. By "rational", we mean
(1) efficient path planning to eliminate first-move lags; (2) collision-free
and smooth for agents with kinematic constraints satisfied. SG-RL works in a
two-level manner. At the first level, SG-RL uses a geometric path-planning
method, i.e., Simple Subgoal Graphs (SSG), to efficiently find optimal abstract
paths, also called subgoal sequences. At the second level, SG-RL uses an RL
method, i.e., Least-Squares Policy Iteration (LSPI), to learn near-optimal
motion-planning policies which can generate kinematically feasible and
collision-free trajectories between adjacent subgoals. The first advantage of
the proposed method is that SSG can solve the limitations of sparse reward and
local minima trap for RL agents; thus, LSPI can be used to generate paths in
complex environments. The second advantage is that, when the environment
changes slightly (i.e., unexpected obstacles appearing), SG-RL does not need to
reconstruct subgoal graphs and replan subgoal sequences using SSG, since LSPI
can deal with uncertainties by exploiting its generalization ability to handle
changes in environments. Simulation experiments in representative scenarios
demonstrate that, compared with existing methods, SG-RL can work well on
large-scale maps with relatively low action-switching frequencies and shorter
path lengths, and SG-RL can deal with small changes in environments. We further
demonstrate that the design of reward functions and the types of training
environments are important factors for learning feasible policies.Comment: 20 page
An Overview of Verification and Validation Challenges for Inspection Robots
The advent of sophisticated robotics and AI technology makes sending humans into hazardous and distant environments to carry out inspections increasingly avoidable. Being able to send a robot, rather than a human, into a nuclear facility or deep space is very appealing. However, building these robotic systems is just the start and we still need to carry out a range of verification and validation tasks to ensure that the systems to be deployed are as safe and reliable as possible. Based on our experience across three research and innovation hubs within the UK’s “Robots for a Safer World” programme, we present an overview of the relevant techniques and challenges in this area. As the hubs are active across nuclear, offshore, and space environments, this gives a breadth of issues common to many inspection robot
Fast Approximate Clearance Evaluation for Rovers with Articulated Suspension Systems
We present a light-weight body-terrain clearance evaluation algorithm for the
automated path planning of NASA's Mars 2020 rover. Extraterrestrial path
planning is challenging due to the combination of terrain roughness and severe
limitation in computational resources. Path planning on cluttered and/or uneven
terrains requires repeated safety checks on all the candidate paths at a small
interval. Predicting the future rover state requires simulating the vehicle
settling on the terrain, which involves an inverse-kinematics problem with
iterative nonlinear optimization under geometric constraints. However, such
expensive computation is intractable for slow spacecraft computers, such as
RAD750, which is used by the Curiosity Mars rover and upcoming Mars 2020 rover.
We propose the Approximate Clearance Evaluation (ACE) algorithm, which obtains
conservative bounds on vehicle clearance, attitude, and suspension angles
without iterative computation. It obtains those bounds by estimating the lowest
and highest heights that each wheel may reach given the underlying terrain, and
calculating the worst-case vehicle configuration associated with those extreme
wheel heights. The bounds are guaranteed to be conservative, hence ensuring
vehicle safety during autonomous navigation. ACE is planned to be used as part
of the new onboard path planner of the Mars 2020 rover. This paper describes
the algorithm in detail and validates our claim of conservatism and fast
computation through experiments
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