15 research outputs found
Optimal Cost-Preference Trade-off Planning with Multiple Temporal Tasks
Autonomous robots are increasingly utilized in realistic scenarios with
multiple complex tasks. In these scenarios, there may be a preferred way of
completing all of the given tasks, but it is often in conflict with optimal
execution. Recent work studies preference-based planning, however, they have
yet to extend the notion of preference to the behavior of the robot with
respect to each task. In this work, we introduce a novel notion of preference
that provides a generalized framework to express preferences over individual
tasks as well as their relations. Then, we perform an optimal trade-off
(Pareto) analysis between behaviors that adhere to the user's preference and
the ones that are resource optimal. We introduce an efficient planning
framework that generates Pareto-optimal plans given user's preference by
extending A* search. Further, we show a method of computing the entire Pareto
front (the set of all optimal trade-offs) via an adaptation of a
multi-objective A* algorithm. We also present a problem-agnostic search
heuristic to enable scalability. We illustrate the power of the framework on
both mobile robots and manipulators. Our benchmarks show the effectiveness of
the heuristic with up to 2-orders of magnitude speedup.Comment: 8 pages, 4 figures, to appear in International Conference on
Intelligent Robots and Systems (IROS) 202
Minimum Violation Control Synthesis on Cyber-Physical Systems under Attacks
Cyber-physical systems are conducting increasingly complex tasks, which are
often modeled using formal languages such as temporal logic. The system's
ability to perform the required tasks can be curtailed by malicious adversaries
that mount intelligent attacks. At present, however, synthesis in the presence
of such attacks has received limited research attention. In particular, the
problem of synthesizing a controller when the required specifications cannot be
satisfied completely due to adversarial attacks has not been studied. In this
paper, we focus on the minimum violation control synthesis problem under linear
temporal logic constraints of a stochastic finite state discrete-time system
with the presence of an adversary. A minimum violation control strategy is one
that satisfies the most important tasks defined by the user while violating the
less important ones. We model the interaction between the controller and
adversary using a concurrent Stackelberg game and present a nonlinear
programming problem to formulate and solve for the optimal control policy. To
reduce the computation effort, we develop a heuristic algorithm that solves the
problem efficiently and demonstrate our proposed approach using a numerical
case study
From Specifications to Behavior: Maneuver Verification in a Semantic State Space
To realize a market entry of autonomous vehicles in the foreseeable future,
the behavior planning system will need to abide by the same rules that humans
follow. Product liability cannot be enforced without a proper solution to the
approval trap. In this paper, we define a semantic abstraction of the
continuous space and formalize traffic rules in linear temporal logic (LTL).
Sequences in the semantic state space represent maneuvers a high-level planner
could choose to execute. We check these maneuvers against the formalized
traffic rules using runtime verification. By using the standard model checker
NuSMV, we demonstrate the effectiveness of our approach and provide runtime
properties for the maneuver verification. We show that high-level behavior can
be verified in a semantic state space to fulfill a set of formalized rules,
which could serve as a step towards safety of the intended functionality.Comment: Published at IEEE Intelligent Vehicles Symposium (IV), 201
Legal Decision-making for Highway Automated Driving
Compliance with traffic laws is a fundamental requirement for human drivers
on the road, and autonomous vehicles must adhere to traffic laws as well.
However, current autonomous vehicles prioritize safety and collision avoidance
primarily in their decision-making and planning, which will lead to
misunderstandings and distrust from human drivers and may even result in
accidents in mixed traffic flow. Therefore, ensuring the compliance of the
autonomous driving decision-making system is essential for ensuring the safety
of autonomous driving and promoting the widespread adoption of autonomous
driving technology. To this end, the paper proposes a trigger-based layered
compliance decision-making framework. This framework utilizes the decision
intent at the highest level as a signal to activate an online violation monitor
that identifies the type of violation committed by the vehicle. Then, a
four-layer architecture for compliance decision-making is employed to generate
compliantly trajectories. Using this system, autonomous vehicles can detect and
correct potential violations in real-time, thereby enhancing safety and
building public confidence in autonomous driving technology. Finally, the
proposed method is evaluated on the DJI AD4CHE highway dataset under four
typical highway scenarios: speed limit, following distance, overtaking, and
lane-changing. The results indicate that the proposed method increases the
vehicle's overall compliance rate from 13.85% to 84.46%, while reducing the
proportion of active violations to 0%, demonstrating its effectiveness.Comment: 14 pages, 17 figure
Optimizing Task Waiting Times in Dynamic Vehicle Routing
We study the problem of deploying a fleet of mobile robots to service tasks
that arrive stochastically over time and at random locations in an environment.
This is known as the Dynamic Vehicle Routing Problem (DVRP) and requires robots
to allocate incoming tasks among themselves and find an optimal sequence for
each robot. State-of-the-art approaches only consider average wait times and
focus on high-load scenarios where the arrival rate of tasks approaches the
limit of what can be handled by the robots while keeping the queue of
unserviced tasks bounded, i.e., stable. To ensure stability, these approaches
repeatedly compute minimum distance tours over a set of newly arrived tasks.
This paper is aimed at addressing the missing policies for moderate-load
scenarios, where quality of service can be improved by prioritizing
long-waiting tasks. We introduce a novel DVRP policy based on a cost function
that takes the -norm over accumulated wait times and show it guarantees
stability even in high-load scenarios. We demonstrate that the proposed policy
outperforms the state-of-the-art in both mean and percentile wait
times in moderate-load scenarios through simulation experiments in the
Euclidean plane as well as using real-world data for city scale service
requests.Comment: Accepted for publication in IEEE Robotics and Automation Letters
(RA-L
Metrics for Signal Temporal Logic Formulae
Signal Temporal Logic (STL) is a formal language for describing a broad range
of real-valued, temporal properties in cyber-physical systems. While there has
been extensive research on verification and control synthesis from STL
requirements, there is no formal framework for comparing two STL formulae. In
this paper, we show that under mild assumptions, STL formulae admit a metric
space. We propose two metrics over this space based on i) the Pompeiu-Hausdorff
distance and ii) the symmetric difference measure, and present algorithms to
compute them. Alongside illustrative examples, we present applications of these
metrics for two fundamental problems: a) design quality measures: to compare
all the temporal behaviors of a designed system, such as a synthetic genetic
circuit, with the "desired" specification, and b) loss functions: to quantify
errors in Temporal Logic Inference (TLI) as a first step to establish formal
performance guarantees of TLI algorithms.Comment: This paper has been accepted for presentation at, and publication in
the proceedings of, the 2018 IEEE Conference on Decision and Control (CDC),
to be held in Fontainebleau, Miami Beach, FL, USA on Dec. 17-19, 201