News & Events

22.09.2022

בקרת גודש עבור רשת תחבורה אווירית אוטונומית מאסיבית בסביבה עירונית בגובה נמוך - Congestion Control for Large Scale Low-Altitude City Air

Abstract :Recently we witnessed an increasing trend for autonomous aerial vehicles in the city’s sky to deliver goods, transport passengers, attend medical support and more. These vehicles perform high mobility for point-to-point missions, but to do so, they should cruise across the sky with minimum interference. As these trends grow, more vehicles will demand these safe routes as more potential collisions require resolution.

Here we give some insights to a preliminary novel concept of a low-altitude air transport system (LAAT) that supplies the infrastructure for the aerial vehicles to move in the urban area. Inspired by the well-researched ground transportation methods, the suggested LAAT system is based on the natural relation between density and accumulation of the vehicles in a given area – as described in the macroscopic fundamental diagram (MFD), and then using a flow control method called perimeter control to regulate the inter-regional flow from adjacent regions.

A micro-simulation of a LAAT vehicle was designed, including mission definition and collision avoidance mechanism, and then it was implemented in an aggregated simulation to show that an MFD-like relation exists in a given region of LAAT system similarly to ground traffic MFD. Then, an adaptive perimeter controller is designed and tested in several multi-regional simulations. The controller performance was measured in the meaning of network congestion avoidance in different scenarios, including different number of connected regions, different regional and inter-regional flow demand (attraction) and tested on the plant model – either original non-linear model or simplified partially-linearized model.

The simulations show that the aerial vehicles flow indeed perform a MFD relation, and the extracted MFD was used to design a perimeter controller. The simulations show that the controller successfully prevents congestion on the partially-linearized model of the multi-regional scenarios and extends the stability of the system to harsh demand in the original non-linear model of the multi-regional scenarios.

 

Afterwards, robustness research was performed on the designed perimeter controller to evaluate the dependency of the solutions improvement on several key parameters under expected real-life implementation difficulties such as controller-plant channel delay and control communication constraints.

 

13.06.2022

Technion Robotics Seminar (TRS) - Ronen Brafman

Ronen Brafman – Ben-Gurion University

When: 08.06.2022 at 15:00

Where: zoom

Abstract: While one-trick robots are abundant in industry,

constructing an autonomous robot that can perform multiple

tasks flexibly, such as a service robot, is a difficult task, we are

yet to reach. A large number of robotics researchers have been working hard to design

and implement algorithms for diverse robotic skills. But these skills are

hard to put together: A lot of software engineering and thought goes

into each script that operates them. In this talk I will present a research

program seeking to change this situation, providing

plug’s play autonomy for robotic via what we call the autonomous operating system  (AOS).

This system uses a language we designed for documenting robotic skill code to provide

the AOS with the information needed to decide when and how tol execute each skill.

No additional software-engineering effort is required beyond this specification.

I will describe our initial version of this system and our future plans.

The talk is non-technical and requires little background.

 

You can see the seminar here

 

24.04.2022

TRS - Prof. Amanda Prorok (University of Cambridge) - Learning-Based Methods for Cooperative Multi-Robot Systems

When: Wednesday, April 27th at 15:00

Where: Zoom

Abstract: In this talk, I first discuss how we leverage machine learning methods to generate cooperative policies for multi-robot systems. I describe how we use Graph Neural Networks (GNNs) to learn effective communication strategies for decentralized coordination. I then show how our GNN-based policy is able to achieve near-optimal performance across a variety of problems, at a fraction of the real-time computational cost. Finally, I present some pioneering real-robot experiments that demonstrate the transfer of our methods to the physical world.

12.04.2022

TRS - Prof. Pulkit Agrawal (MIT) - Coming of Age of Robot Learning

When: Wednesday, April 13th at 15:00

Where: Zoom

Talk Abstract The questions of dexterity, agility, and learning from a few demonstrations have intrigued robotics researchers. In this talk, I will explore answers and solutions to these questions via the following case studies: (i) a dexterous manipulation system capable of re-orienting novel objects. (ii) a quadruped robot that is substantially more agile than its counterparts (runs, spins) on challenging natural terrains. (iii) framework for learning task-sensitive perceptual representations for planning and out-of-distribution generalization. While a lot of recent progress in robotics is driven by perception, we show that learned controllers can help address problems that were previously thought to be hard. I will discuss our findings, the insights we gained, and the road ahead.

5.04.2022

TRS - Prof. Timothy Barfoot (University of Toronto) Where Can Machine Learning Help Robotic State Estimation?

When: Wednesday, April 6th at 15:00

Where: Zoom

Abstract: Classic state estimation tools (e.g., determining position/velocity of a robot from noisy sensor data) have been in use since the 1960s, perhaps the most famous technique being the Kalman filter.  For difficult-to-model nonlinear systems with rich sensing (e.g., almost any real-world robot), clever adaptations are needed to the classic tools.  In this talk, I will first briefly summarize an idea that has become standard practice in our group over the last several years:  continuous-time trajectory estimation (and its connection to sparse Gaussian process regression).  I will then discuss two new frameworks we have been pursuing lately:  exactly sparse Gaussian variationally inference (ESGVI) and Koopman state estimation (KoopSE).   ESGVI seeks to minimize the Kullback-Leibler divergence between a Gaussian state estimate and the full Bayesian posterior; however, the framework also easily allows for parameter learning through Expectation Maximization and we’ve used this to learn simple parameters such as constant system matrices and covariances, but also to model rich sensors using Deep Neural Networks and learn the weights from data.  KoopSE takes a different approach by lifting a nonlinear system into a high-dimensional Reproducing Kernel Hilbert Space where we can treat it as linear and apply classic estimation tools; it also allows for the system to be learned from training data quite efficiently.  I will give simple intuitive explanations of the mathematics and show some examples of things working in practice.

28.03.2022

TASP PhD Seminar - Omer Nir - Reactive Planning for Dynamic Legged Maneuvers Using Motion Primitive Library

Work towards PhD degree under the supervision of  Prof. Amir Degani

When: 14.4.2022 at 11:00 

Where: Zoom

The seminar will be held in hybrid format: Water institute auditorium

Abstract: In recent years, there has been considerable advancement in the fields of planning and control of legged robots, but legged robots are still extremely limited. Most research effort has been directed toward bipedal walking and quadrupedal walking and running, and not so much at bipedal running. Dynamic-legged locomotion is difficult to plan and execute, it requires robust control strategies and fast planning algorithms. In this work, we describe a strategy for traversing uneven complex terrain without the need to perform explicit foot placement. This will be achieved using a funnel library approach in combination with an online receding horizon planner. Thus, enabling the robot to take full advantage of its dynamics and the potential reaction forces presented by the environment. We demonstrate, in simulation, multiple scenarios, from traversing a low-friction obstacle, all the way to inclined surfaces and chute climbing with intermittent low-friction patches. We present an efficient approach for generating transitions from running to climbing without the need for a long planning horizon. We investigate the SLIP, ASLIP, and the actuated SLIP models and several in-stride control approaches.

20.03.2022

TRS - Prof. Derek Long (King's College London and Scientific Advisor, Schlumberger) - From Plans to Performance

When: 23.3.2022 at 15:00

Where: zoom

Abstract: In 2016, Schlumberger started to deploy automated drilling, using plan-based control, across multiple rigs in several basins. Five years later, the technology has drilled more than half-a-million feet under fully autonomous control, with an estimated 17% efficiency improvement over human operations. A significant challenge in this deployment has been the range of rigs on which the system has been deployed, each with different degrees of access to control systems, different sensors and all of them in different geophysical settings. In this talk I will outline the planning technology and the benefits it has brought in the achievement of this success, the approaches we have adopted to interfacing between planning and execution, the scope of some of the other uses we have found for the same approach and, depending on time, some of the planning-related problems we are currently investigating.

21.02.2022

TASP MSc Seminar - Nitzan Madar - Leveraging Experience in Multi-Agent Path Finding

Work towards MSc degree under the supervision of Dr. Oren Salzman

When: 7.3.2022 at 9:30

Where: zoom

Abstract: In Lifelong Multi-Agent Path Finding (L-MAPF) a team of agents performs a stream of tasks consisting of multiple locations to be visited by the agents on a shared graph while avoiding collisions with one another. L-MAPF is typically tackled by partitioning it into multiple consecutive, and hence similar, “one-shot” MAPF queries with a single task assigned to each agent, as in the Rolling-Horizon Collision Resolution (RHCR) algorithm. Therefore, a solution to one query informs the next query, which leads to similarity with respect to the agents’ start and goal positions, and how collisions between the agents need to be resolved from one query to the next. Thus, experience from solving one MAPF query can potentially be used to speedup solving the next one and reduce runtime in L-MAPF overall. Despite this intuition, current L-MAPF planners solve consecutive MAPF queries from scratch. In this paper, we introduce a new RHCR-inspired approach called exRHCR, which exploits experience in its constituent MAPF queries. In particular, exRHCR employs a new extension of Priority-Based Search (PBS), a state-of-the-art MAPF solver.
Our extension, called exPBS, allows to warm-start the search with the priorities between agents used by PBS in the previous MAPF instances. We demonstrate empirically that exRHCR solves L-MAPF up to 25% faster than RHCR, and allows to increase throughput for given task streams by as much as 3%-16% by increasing the number of agents we can cope with for a given time budget.

You can see the seminar here