20 research outputs found
Epistemic Planning for Heterogeneous Robotic Systems
In applications such as search and rescue or disaster relief, heterogeneous
multi-robot systems (MRS) can provide significant advantages for complex
objectives that require a suite of capabilities. However, within these
application spaces, communication is often unreliable, causing inefficiencies
or outright failures to arise in most MRS algorithms. Many researchers tackle
this problem by requiring all robots to either maintain communication using
proximity constraints or assuming that all robots will execute a predetermined
plan over long periods of disconnection. The latter method allows for higher
levels of efficiency in a MRS, but failures and environmental uncertainties can
have cascading effects across the system, especially when a mission objective
is complex or time-sensitive. To solve this, we propose an epistemic planning
framework that allows robots to reason about the system state, leverage
heterogeneous system makeups, and optimize information dissemination to
disconnected neighbors. Dynamic epistemic logic formalizes the propagation of
belief states, and epistemic task allocation and gossip is accomplished via a
mixed integer program using the belief states for utility predictions and
planning. The proposed framework is validated using simulations and experiments
with heterogeneous vehicles
A Meta-Learning-based Trajectory Tracking Framework for UAVs under Degraded Conditions
Due to changes in model dynamics or unexpected disturbances, an autonomous
robotic system may experience unforeseen challenges during real-world
operations which may affect its safety and intended behavior: in particular
actuator and system failures and external disturbances are among the most
common causes of degraded mode of operation. To deal with this problem, in this
work, we present a meta-learning-based approach to improve the trajectory
tracking performance of an unmanned aerial vehicle (UAV) under actuator faults
and disturbances which have not been previously experienced. Our approach
leverages meta-learning to train a model that is easily adaptable at runtime to
make accurate predictions about the system's future state. A runtime monitoring
and validation technique is proposed to decide when the system needs to adapt
its model by considering a data pruning procedure for efficient learning.
Finally, the reference trajectory is adapted based on future predictions by
borrowing feedback control logic to make the system track the original and
desired path without needing to access the system's controller. The proposed
framework is applied and validated in both simulations and experiments on a
faulty UAV navigation case study demonstrating a drastic increase in tracking
performance.Comment: 2021 IEEE/RSJ International Conference on Intelligent Robots and
Systems (IROS) (to appear) 2021 copyright IEE
A Decision Tree-based Monitoring and Recovery Framework for Autonomous Robots with Decision Uncertainties
Autonomous mobile robots (AMR) operating in the real world often need to make
critical decisions that directly impact their own safety and the safety of
their surroundings. Learning-based approaches for decision making have gained
popularity in recent years, since decisions can be made very quickly and with
reasonable levels of accuracy for many applications. These approaches, however,
typically return only one decision, and if the learner is poorly trained or
observations are noisy, the decision may be incorrect. This problem is further
exacerbated when the robot is making decisions about its own failures, such as
faulty actuators or sensors and external disturbances, when a wrong decision
can immediately cause damage to the robot. In this paper, we consider this very
case study: a robot dealing with such failures must quickly assess
uncertainties and make safe decisions. We propose an uncertainty aware
learning-based failure detection and recovery approach, in which we leverage
Decision Tree theory along with Model Predictive Control to detect and explain
which failure is compromising the system, assess uncertainties associated with
the failure, and lastly, find and validate corrective controls to recover the
system. Our approach is validated with simulations and real experiments on a
faulty unmanned ground vehicle (UGV) navigation case study, demonstrating
recovery to safety under uncertainties
A Model Predictive Path Integral Method for Fast, Proactive, and Uncertainty-Aware UAV Planning in Cluttered Environments
Current motion planning approaches for autonomous mobile robots often assume
that the low level controller of the system is able to track the planned motion
with very high accuracy. In practice, however, tracking error can be affected
by many factors, and could lead to potential collisions when the robot must
traverse a cluttered environment. To address this problem, this paper proposes
a novel receding-horizon motion planning approach based on Model Predictive
Path Integral (MPPI) control theory -- a flexible sampling-based control
technique that requires minimal assumptions on vehicle dynamics and cost
functions. This flexibility is leveraged to propose a motion planning framework
that also considers a data-informed risk function. Using the MPPI algorithm as
a motion planner also reduces the number of samples required by the algorithm,
relaxing the hardware requirements for implementation. The proposed approach is
validated through trajectory generation for a quadrotor unmanned aerial vehicle
(UAV), where fast motion increases trajectory tracking error and can lead to
collisions with nearby obstacles. Simulations and hardware experiments
demonstrate that the MPPI motion planner proactively adapts to the obstacles
that the UAV must negotiate, slowing down when near obstacles and moving
quickly when away from obstacles, resulting in a complete reduction of
collisions while still producing lively motion.Comment: Accepted to IROS 2023, 8 page
Towards Assurance Cases for Resilient Control Systems
The paper studies the problem of constructing assurance cases for embedded control systems developed using a model-based approach. Assurance cases aim to provide a convincing argument that the system delivers certain guarantees, based on the evidence obtained during the design and evaluation of the system. We suggest an argument strategy centered around properties of models used in the development and properties of tools that manipulate these models. The paper presents the case study of a resilient speed estimator for an autonomous ground vehicle and takes the reader through a detailed assurance case arguing that the estimator computes speed estimates with bounded error
Architecture-Centric Software Development for Cyber-Physical Systems
We discuss the problem of high-assurance development of cyber-physical systems. Specifically, we concentrate on the interaction between the development of the control system layer and platform-specific software engineering for system components. We argue that an architecture-centric approach allows us to streamline the development and increase the level of assurance for the resulting system. The case study of an unmanned ground vehicle illustrates the approach