9 research outputs found
Robotic Herding of a Flock of Birds Using an Unmanned Aerial Vehicle
In this paper, we derive an algorithm for enabling a single robotic unmanned aerial vehicle to herd a flock of birds away from a designated volume of space, such as the air space around an airport. The herding algorithm, referred to as the m-waypoint algorithm, is designed using a dynamic model of bird flocking based on Reynolds’ rules. We derive bounds on its performance using a combination of reduced-order modeling of the flock's motion, heuristics, and rigorous analysis. A unique contribution of the paper is the experimental demonstration of several facets of the herding algorithm on flocks of live birds reacting to a robotic pursuer. The experiments allow us to estimate several parameters of the flocking model, and especially the interaction between the pursuer and the flock. The herding algorithm is also demonstrated using numerical simulations
Enhancing State Estimator for Autonomous Race Car : Leveraging Multi-modal System and Managing Computing Resources
This paper introduces an innovative approach to enhance the state estimator
for high-speed autonomous race cars, addressing challenges related to
unreliable measurements, localization failures, and computing resource
management. The proposed robust localization system utilizes a Bayesian-based
probabilistic approach to evaluate multimodal measurements, ensuring the use of
credible data for accurate and reliable localization, even in harsh racing
conditions. To tackle potential localization failures during intense racing, we
present a resilient navigation system. This system enables the race car to
continue track-following by leveraging direct perception information in
planning and execution, ensuring continuous performance despite localization
disruptions. Efficient computing resource management is critical to avoid
overload and system failure. We optimize computing resources using an efficient
LiDAR-based state estimation method. Leveraging CUDA programming and GPU
acceleration, we perform nearest points search and covariance computation
efficiently, overcoming CPU bottlenecks. Real-world and simulation tests
validate the system's performance and resilience. The proposed approach
successfully recovers from failures, effectively preventing accidents and
ensuring race car safety.Comment: arXiv admin note: text overlap with arXiv:2207.1223
Design, Field Evaluation, and Traffic Analysis of a Competitive Autonomous Driving Model in a Congested Environment
Recently, numerous studies have investigated cooperative traffic systems
using the communication among vehicle-to-everything (V2X). Unfortunately, when
multiple autonomous vehicles are deployed while exposed to communication
failure, there might be a conflict of ideal conditions between various
autonomous vehicles leading to adversarial situation on the roads. In South
Korea, virtual and real-world urban autonomous multi-vehicle races were held in
March and November of 2021, respectively. During the competition, multiple
vehicles were involved simultaneously, which required maneuvers such as
overtaking low-speed vehicles, negotiating intersections, and obeying traffic
laws. In this study, we introduce a fully autonomous driving software stack to
deploy a competitive driving model, which enabled us to win the urban
autonomous multi-vehicle races. We evaluate module-based systems such as
navigation, perception, and planning in real and virtual environments.
Additionally, an analysis of traffic is performed after collecting multiple
vehicle position data over communication to gain additional insight into a
multi-agent autonomous driving scenario. Finally, we propose a method for
analyzing traffic in order to compare the spatial distribution of multiple
autonomous vehicles. We study the similarity distribution between each team's
driving log data to determine the impact of competitive autonomous driving on
the traffic environment
Robotic Herding of a Flock of Birds Using an Unmanned Aerial Vehicle
In this paper, we derive an algorithm for enabling a single robotic unmanned aerial vehicle to herd a flock of birds away from a designated volume of space, such as the air space around an airport. The herding algorithm, referred to as the m-waypoint algorithm, is designed using a dynamic model of bird flocking based on Reynolds’ rules. We derive bounds on its performance using a combination of reduced-order modeling of the flock's motion, heuristics, and rigorous analysis. A unique contribution of the paper is the experimental demonstration of several facets of the herding algorithm on flocks of live birds reacting to a robotic pursuer. The experiments allow us to estimate several parameters of the flocking model, and especially the interaction between the pursuer and the flock. The herding algorithm is also demonstrated using numerical simulations
A Versatile Door Opening System with Mobile Manipulator through Adaptive Position-Force Control and Reinforcement Learning
The ability of robots to navigate through doors is crucial for their
effective operation in indoor environments. Consequently, extensive research
has been conducted to develop robots capable of opening specific doors.
However, the diverse combinations of door handles and opening directions
necessitate a more versatile door opening system for robots to successfully
operate in real-world environments. In this paper, we propose a mobile
manipulator system that can autonomously open various doors without prior
knowledge. By using convolutional neural networks, point cloud extraction
techniques, and external force measurements during exploratory motion, we
obtained information regarding handle types, poses, and door characteristics.
Through two different approaches, adaptive position-force control and deep
reinforcement learning, we successfully opened doors without precise trajectory
or excessive external force. The adaptive position-force control method
involves moving the end-effector in the direction of the door opening while
responding compliantly to external forces, ensuring safety and manipulator
workspace. Meanwhile, the deep reinforcement learning policy minimizes applied
forces and eliminates unnecessary movements, enabling stable operation across
doors with different poses and widths. The RL-based approach outperforms the
adaptive position-force control method in terms of compensating for external
forces, ensuring smooth motion, and achieving efficient speed. It reduces the
maximum force required by 3.27 times and improves motion smoothness by 1.82
times. However, the non-learning-based adaptive position-force control method
demonstrates more versatility in opening a wider range of doors, encompassing
revolute doors with four distinct opening directions and varying widths
Indoor UAV Control Using Multi-Camera Visual Feedback
This paper presents the control of an indoor unmanned aerial vehicle (UAV) using multi-camera visual feedback. For the autonomous flight of the indoor UAV, instead of using onboard sensor information, visual feedback concept is employed by the development of an indoor flight test-bed. The indoor test-bed consists of four major components: the multi-camera system, ground computer, onboard color marker set, and quad-rotor UAV. Since the onboard markers are attached to the pre-defined location, position and attitude of the UAV can be estimated by marker detection algorithm and triangulation method. Additionally, this study introduces a filter algorithm to obtain the full 6-degree of freedom (DOF) pose estimation including velocities and angular rates. The filter algorithm also enhances the performance of the vision system by making up for the weakness of low cost cameras such as poor resolution and large noise. Moreover, for the pose estimation of multiple vehicles, data association algorithm using the geometric relation between cameras is proposed in this paper. The control system is designed based on the classical proportional-integral-derivative (PID) control, which uses the position, velocity and attitude from the vision system and the angular rate from the rate gyro sensor. This paper concludes with both ground and flight test results illustrating the performance and properties of the proposed indoor flight test-bed and the control system using the multi-camera visual feedbackclos