4 research outputs found
Sequential Neural Barriers for Scalable Dynamic Obstacle Avoidance
There are two major challenges for scaling up robot navigation around dynamic
obstacles: the complex interaction dynamics of the obstacles can be hard to
model analytically, and the complexity of planning and control grows
exponentially in the number of obstacles. Data-driven and learning-based
methods are thus particularly valuable in this context. However, data-driven
methods are sensitive to distribution drift, making it hard to train and
generalize learned models across different obstacle densities. We propose a
novel method for compositional learning of Sequential Neural Control Barrier
models (SNCBFs) to achieve scalability. Our approach exploits an important
observation: the spatial interaction patterns of multiple dynamic obstacles can
be decomposed and predicted through temporal sequences of states for each
obstacle. Through decomposition, we can generalize control policies trained
only with a small number of obstacles, to environments where the obstacle
density can be 100x higher. We demonstrate the benefits of the proposed methods
in improving dynamic collision avoidance in comparison with existing methods
including potential fields, end-to-end reinforcement learning, and
model-predictive control. We also perform hardware experiments and show the
practical effectiveness of the approach in the supplementary video.Comment: To be published in IROS 202
Autonomous vehicles in mixed trafïŹc conditionsâA bibliometric analysis
Autonomous Vehicles (AVs) with their immaculate sensing and navigating capabilities are expected to revolutionize urban mobility. Despite the expected benefits, this emerging technology has certain implications pertaining to their deployment in mixed traffic streams, owing to different driving logics than Human-driven Vehicles (HVs). Many researchers have been working to devise a sustainable urban transport system by considering the operational and safety aspects of mixed traffic during the transition phase. However, limited scholarly attention has been devoted to mapping an overview of this research area. This paper attempts to map the state of the art of scientific production about autonomous vehicles in mixed traffic conditions, using a bibliometric analysis of 374 documents extracted from the Scopus database from 1999 to 2021. The VOSviewer 1.1.18 and Biblioshiny 3.1 software were used to demonstrate the progress status of the publications concerned. The analysis revealed that the number of publications has continuously increased during the last five years. The text analysis showed that the author keywords âautonomous vehiclesâ and âmixed trafficâ dominated the other author keywords because of their frequent occurrence. From thematic analysis, three research stages associated with AVs were identified; pre-development (1999â2017), development (2017â2020) and deployment (2021). The study highlighted the potential research areas, such as involvement of autonomous vehicles in transportation planning, interaction between autonomous vehicles and human driven vehicles, traffic and energy efficiencies associated with automated driving, penetration rates for autonomous vehicles in mixed traffic scenarios, and safe and efficient operation of autonomous vehicles in mixed traffic environment. Additionally, discussion on the three key aspects was conducted, including the impacts of AVs, their driving characteristics and strategies for their successful deployment in context of mixed traffic. This paper provides ample future directions to the people willing to work in this area of autonomous vehicles in mixed traffic conditions. The study also revealed current trends as well as potential future hotspots in the area of autonomous vehicles in mixed traffic
Reference Governors: From Theory to Practice
Control systems that are subject to constraints due to physical limitations, hardware
protection, or safety considerations have led to challenging control problems that have
piqued the interest of control practitioners and theoreticians for many decades. In
general, the design of constraint management schemes must meet several stringent
requirements, for example: low computational burden, performance, recovery mechanisms
from infeasibility conditions, robustness, and formulation simplicity. These
requirements have been particularly difficult to meet for the following three classes
of systems: stochastic systems, linear systems driven by unmodeled disturbances,
and nonlinear systems. Hence, in this work, we develop three constraint management
schemes, based on Reference Governor (RG), for these classes of systems. The
first scheme, which is referred to as Stochastic RG, leverages the ideas of chance
constraints to construct a Stochastic Robustly Invariant Maximal Output Admissible
set (SR-MAS) in order to enforce constraints on stochastic systems. The second
scheme, which is called Recovery RG (RRG), addresses the problem of recovery from
infeasibility conditions by implementing a disturbance observer to update the MAS,
and hence recover from constraint violations due to unmodeled disturbances. The
third method addresses the problem of constraint satisfaction on nonlinear systems
by decomposing the design of the constraint management strategy into two parts: enforcement
at steady-state, and during transient. The former is achieved by using the
forward and inverse steady-state characterization of the nonlinear system. The latter
is achieved by implementing an RG-based approach, which employs a novel Robust
Output Admissible Set (ROAS) that is computed using data obtained from the nonlinear
system. Added to this, this dissertation includes a detailed literature review
of existing constraint management schemes to compare and highlight advantages and
disadvantages between them. Finally, all this study is supported by a systematic
analysis, as well as numerical and experimental validation of the closed-loop systems
performance on vehicle roll-over avoidance, turbocharged engine control, and inverted
pendulum control problems