564 research outputs found
A Real-Time Game Theoretic Planner for Autonomous Two-Player Drone Racing
In this article, we propose an online 3-D planning algorithm for a drone to race competitively against a single adversary drone. The algorithm computes an approximation of the Nash equilibrium in the joint space of trajectories of the two drones at each time step, and proceeds in a receding horizon fashion. The algorithm uses a novel sensitivity term, within an iterative best response computational scheme, to approximate the amount by which the adversary will yield to the ego drone to avoid a collision. This leads to racing trajectories that are more competitive than without the sensitivity term. We prove that the fixed point of this sensitivity enhanced iterative best response satisfies the first-order optimality conditions of a Nash equilibrium. We present results of a simulation study of races with 2-D and 3-D race courses, showing that our game theoretic planner significantly outperforms amodel predictive control (MPC) racing algorithm. We also present results of multiple drone racing experiments on a 3-D track in which drones sense each others'' relative position with onboard vision. The proposed game theoretic planner again outperforms the MPC opponent in these experiments where drones reach speeds up to 1.25m/s
A Real-Time Game Theoretic Planner for Autonomous Two-Player Drone Racing
To be successful in multi-player drone racing, a player must not only follow
the race track in an optimal way, but also compete with other drones through
strategic blocking, faking, and opportunistic passing while avoiding
collisions. Since unveiling one's own strategy to the adversaries is not
desirable, this requires each player to independently predict the other
players' future actions. Nash equilibria are a powerful tool to model this and
similar multi-agent coordination problems in which the absence of communication
impedes full coordination between the agents. In this paper, we propose a novel
receding horizon planning algorithm that, exploiting sensitivity analysis
within an iterated best response computational scheme, can approximate Nash
equilibria in real time. We also describe a vision-based pipeline that allows
each player to estimate its opponent's relative position. We demonstrate that
our solution effectively competes against alternative strategies in a large
number of drone racing simulations. Hardware experiments with onboard vision
sensing prove the practicality of our strategy
Social Interaction-Aware Dynamical Models and Decision Making for Autonomous Vehicles
Interaction-aware Autonomous Driving (IAAD) is a rapidly growing field of
research that focuses on the development of autonomous vehicles (AVs) that are
capable of interacting safely and efficiently with human road users. This is a
challenging task, as it requires the autonomous vehicle to be able to
understand and predict the behaviour of human road users. In this literature
review, the current state of IAAD research is surveyed in this work. Commencing
with an examination of terminology, attention is drawn to challenges and
existing models employed for modelling the behaviour of drivers and
pedestrians. Next, a comprehensive review is conducted on various techniques
proposed for interaction modelling, encompassing cognitive methods, machine
learning approaches, and game-theoretic methods. The conclusion is reached
through a discussion of potential advantages and risks associated with IAAD,
along with the illumination of pivotal research inquiries necessitating future
exploration
Contingency Games for Multi-Agent Interaction
Contingency planning, wherein an agent generates a set of possible plans
conditioned on the outcome of an uncertain event, is an increasingly popular
way for robots to act under uncertainty. In this work, we take a game-theoretic
perspective on contingency planning which is tailored to multi-agent scenarios
in which a robot's actions impact the decisions of other agents and vice versa.
The resulting contingency game allows the robot to efficiently coordinate with
other agents by generating strategic motion plans conditioned on multiple
possible intents for other actors in the scene. Contingency games are
parameterized via a scalar variable which represents a future time at which
intent uncertainty will be resolved. Varying this parameter enables a designer
to easily adjust how conservatively the robot behaves in the game.
Interestingly, we also find that existing variants of game-theoretic planning
under uncertainty are readily obtained as special cases of contingency games.
Lastly, we offer an efficient method for solving N-player contingency games
with nonlinear dynamics and non-convex costs and constraints. Through a series
of simulated autonomous driving scenarios, we demonstrate that plans generated
via contingency games provide quantitative performance gains over
game-theoretic motion plans that do not account for future uncertainty
reduction
Game-theoretic Objective Space Planning
Autonomous Racing awards agents that react to opponents' behaviors with agile
maneuvers towards progressing along the track while penalizing both
over-aggressive and over-conservative agents. Understanding the intent of other
agents is crucial to deploying autonomous systems in adversarial multi-agent
environments. Current approaches either oversimplify the discretization of the
action space of agents or fail to recognize the long-term effect of actions and
become myopic. Our work focuses on addressing these two challenges. First, we
propose a novel dimension reduction method that encapsulates diverse agent
behaviors while conserving the continuity of agent actions. Second, we
formulate the two-agent racing game as a regret minimization problem and
provide a solution for tractable counterfactual regret minimization with a
regret prediction model. Finally, we validate our findings experimentally on
scaled autonomous vehicles. We demonstrate that using the proposed
game-theoretic planner using agent characterization with the objective space
significantly improves the win rate against different opponents, and the
improvement is transferable to unseen opponents in an unseen environment.Comment: Submitted to 2023 IEEE International Conference on Robotics and
Automation (ICRA 2023
Interaction-Aware Motion Planning for Automated Vehicles
Die Bewegungsplanung für automatisierte Fahrzeuge (AVs) in gemischtem Verkehr ist eine herausfordernde Aufgabe. Hierbei bezeichnet gemischter Verkehr, Verkehr bestehend aus von Menschen gefahrenen Fahrzeugen sowie automatisierten Fahrzeugen. Um die Komplexität der Aufgabe zu reduzieren, verwenden state-of-the-art Planungsansätze oft die vereinfachende Annahme, dass das zukünftige Verhalten umliegender Fahrzeuge unabhängig vom Plan des AVs vorhergesagt werden kann. Während die Trennung von Prädiktion und Planung für viele Verkehrssituationen eine hilfreiche Vereinfachung darstellt, werden hierbei Interaktionen zwischen den Verkehrsteilnehmern ignoriert, was besonders in interaktiven Verkehrssituationen zu suboptimalem, übermäßig konservativem Fahrverhalten führen kann.
In dieser Arbeit werden zwei interaktionsbewusste Bewegungsplanungsalgorithmen vorgeschlagen, die in der Lage sind übermäßig konservatives Fahrverhalten zu reduzieren. Der Kernaspekt dieser Algorithmen ist, dass Prädiktion und Planung gleichzeitig gelöst werden. Mit diesen Algorithmen können anspruchsvolle Fahrmanöver, wie z. B. das Reißverschlussverfahren in dichtem Verkehr, durchgeführt werden, die mit state-of-the-art Planungsansätzen nicht möglich sind.
Der erste Algorithmus basiert auf Methoden der Multi-Agenten-Planung. Interaktionen zwischen Verkehrsteilnehmern werden durch Optimierung gekoppelter Trajektorien mittels einer gemeinsamen Kostenfunktion approximiert. Das Kernstück des Algorithmus ist eine neuartige Multi-Agenten-Trajektorienplanungsformulierung, die auf gemischt-ganzzahliger quadratischer Programmierung (MIQP) basiert. Die Formulierung garantiert global optimale Lösungen und ist somit in der Lage das kombinatorische Problem zu lösen, welches kontinuierliche Methoden auf lokal optimale Lösungen beschränkt. Desweiteren kann durch den vorgestellten Ansatz ein manöverneutrales Verhalten erzeugt werden, das Manöverentscheidungen in ungewissen Situationen aufschieben kann.
Der zweite Ansatz formuliert Interaktionen zwischen einem menschlichen Fahrer und einem AV als ein Stackelberg-Spiel. Im Gegensatz zu bestehenden Arbeiten kann der Algorithmus allgemeine nichtlineare Zustands- und Eingabebeschränkungen berücksichtigen. Desweiteren führen wir Mechanismen zur Integration von Kooperation und Rücksichtnahme in die Planung ein. Damit wird übermäßig aggressives Fahrverhalten verhindert, was in der Literatur als ein Problem interaktionsbewusster Planungsmethoden identifiziert wurde. Die Wirksamkeit, Robustheit und Echtzeitfähigkeit des Algorithmus wird durch numerische Experimente gezeigt
Motion Planning and Control for Multi Vehicle Autonomous Racing at High Speeds
This paper presents a multi-layer motion planning and control architecture
for autonomous racing, capable of avoiding static obstacles, performing active
overtakes, and reaching velocities above 75 . The used offline global
trajectory generation and the online model predictive controller are highly
based on optimization and dynamic models of the vehicle, where the tires and
camber effects are represented in an extended version of the basic Pacejka
Magic Formula. The proposed single-track model is identified and validated
using multi-body motorsport libraries which allow simulating the vehicle
dynamics properly, especially useful when real experimental data are missing.
The fundamental regularization terms and constraints of the controller are
tuned to reduce the rate of change of the inputs while assuring an acceptable
velocity and path tracking. The motion planning strategy consists of a
Fren\'et-Frame-based planner which considers a forecast of the opponent
produced by a Kalman filter. The planner chooses the collision-free path and
velocity profile to be tracked on a 3 seconds horizon to realize different
goals such as following and overtaking. The proposed solution has been applied
on a Dallara AV-21 racecar and tested at oval race tracks achieving lateral
accelerations up to 25 .Comment: Accepted to the 25th IEEE International Conference on Intelligent
Transportation Systems (IEEE ITSC 2022
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