There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

We present a novel, deeply embedded robotics middleware and programming environment. It uses a multithreaded, publish-subscribe design pattern and provides a Unix-like software interface for micro controller applications. We improve over the state of the art in deeply embedded open source systems by providing a modular and standards-oriented platform. Our system architecture is centered around a publish-subscribe object request broker on top of a POSIX application programming interface. This allows to reuse common Unix knowledge and experience, including a bash-like shell. We demonstrate with a vertical takeoff and landing (VTOL) use case that the system modularity is well suited for novel and experimental vehicle platforms. We also show how the system architecture allows a direct interface to ROS and to run individual processes either as native ROS nodes on Linux or nodes on the micro controller, maximizing interoperability. Our microcontroller-based execution environment has substantially lower latency and better hardware connectivity than a typical Robotics Linux system and is therefore well suited for fast, high rate control tasks.

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PX4: A node-based multithreaded open source robotics framework for deeply embedded platforms

The fast advances in autonomous driving technology prompt the question of suitable operational models for future autonomous vehicles. A key determinant of the viability of such operational models is the competitiveness of their cost structures. Using a comprehensive analysis of the respective cost structures, this research shows that public transportation (in its current form) will only remain economic in situations which allow substantial bundling of demand. In all other cases, shared and pooled vehicles serve the travel demand more efficiently. In contrast to the general opinion, shared fleets may not be the most efficient alternative due to higher efforts for vehicle cleaning. Moreover, a substantial share of vehicles may remain in private possession and use.

Cost-based analysis of autonomous mobility services

International Conference on Robotics and Automation (ICRA)

We evaluate performance of four different operational policies to control an automated mobility-on-demand system with sequential vehicle-sharing in one public open-source-accessible, agent-based, high fidelity simulation environment. Detailed network dynamics on a road level of precision are taken into account. The case study is conducted in a simulation scenario of Zurich city. The results indicate that automated vehicles’ shared mobility systems can provide approximately six times higher occupancy rates than conventional private cars, but that their costs – in Switzerland – are considerably higher than those of subsidized public transport or private cars in the short term. However, these services are predicted to be considerably cheaper than the full costs of owning and using a private car, which makes the long-term adoption of automated taxi services likely. Simulations demonstrate that choice of fleet operational policy determining customer-vehicle assignment and repositioning of empty vehicles (rebalancing) heavily influences system performance, e.g., wait times and cost. This aspect must thus be regarded as an important factor in the interpretation of existing and future simulation studies.

Fleet operational policies for automated mobility: A simulation assessment for Zurich

In an autonomous mobility-on-demand (AMoD) system, customers are transported by autonomously driving vehicles in an on-demand fashion. Although these AMoD systems will soon be introduced to cities, their quantitative analysis from a fleet operational and city planning viewpoint remains challenging due to the lack of dedicated analysis tools. In this paper, we introduce AMoDeus, an open-source software package for the accurate and quantitative analysis of autonomous mobility-on-demand systems. AMoDeus uses an agent-based transportation simulation framework to simulate arbitrarily configured AMoD systems with static or dynamic demand. It includes standard benchmark algorithms, fleet efficiency and service level analysis methods and a dedicated graphical viewer that allows in-depth insights into the system. Together with AMoDeus, we publish a typical simulation scenario based on taxi traces recorded in San Francisco. It can be used to test novel fleet control algorithms or as a basis to model more complex transportation research scenarios.

AMoDeus, a Simulation-Based Testbed for Autonomous Mobility-on-Demand Systems

This paper presents Skye, a novel blimp design. Skye is a helium-filled sphere of diameter 2.7m with a strong inelastic outer hull and an impermeable elastic inner hull. Four tetrahedrally-arranged actuation units (AU) are mounted on the hull for locomotion, with each AU having a thruster which can be rotated around a radial axis through the sphere center. This design provides redundant control in the six degrees of freedom of motion, and Skye is able to move omnidirectionally and to rotate around any axis. A multi-camera module is also mounted on the hull for capture of aerial imagery or live video stream according to an `eyeball' concept - the camera module is not itself actuated, but the whole blimp is rotated in order to obtain a desired camera view. Skye is safe for use near people - the double hull minimizes the likelihood of rupture on an unwanted collision; the propellers are covered by grills to prevent accidental contact; and the blimp is near neutral buoyancy so that it makes only a light impact on contact and can be readily nudged away. The system is portable and deployable by a single operator - the electronics, AUs, and camera unit are mounted externally and are detachable from the hull during transport; operator control is via an intuitive touchpad interface. The motivating application is in entertainment robotics. Skye has a varied motion vocabulary such as swooping and bobbing, plus internal LEDs for visual effect. Computer vision enables interaction with an audience. Experimental results show dexterous maneuvers in indoor and outdoor environments, and non-dangerous impacts between the blimp and humans.

Design and control of a spherical omnidirectional blimp

This paper presents a model predictive control (MPC) approach to optimize routes for Ride-sharing Autonomous Mobility-on-Demand (RAMoD) systems, whereby self-driving vehicles provide coordinated on-demand mobility, possibly allowing multiple customers to share a ride. Specifically, we first devise a time-expanded network flow model for RAMoD. Second, leveraging this model, we design a real-time MPC algorithm to optimize the routes of both empty and customer-carrying vehicles, with the goal of optimizing social welfare, namely, a weighted combination of customers’ travel time and vehicles’ mileage. Finally, we present a real-world case study for the city of San Francisco, CA, by using the micro-scopic traffic simulator MATSim. The simulation results show that a RAMoD system can significantly improve social welfare with respect to a single-occupancy Autonomous Mobility-on-Demand (AMoD) system, and that the predictive structure of the proposed MPC controller allows it to outperform existing reactive ride-sharing coordination algorithms for RAMoD.

Model Predictive Control of Ride-sharing Autonomous Mobility-on-Demand Systems

In unit-capacity mobility-on-demand systems, the vehicles transport only one travel party at a time, whereas in ride-sharing mobility-on-demand systems, a vehicle may transport different travel parties at the same time, e.g., if paths are partially overlapping. One potential benefit of ride sharing is increased system efficiency. However, it is not clear what the trade-offs are between the efficiency gains and the reduction in quality of service. To quantify those trade-offs, an open-source simulation environment is introduced, which is capable of evaluating a large class of operational policies for ride-sharing mobility-on-demand systems. The impact of ride sharing on efficiency and service level is assessed for several benchmark operational policies from the literature and for different transportation scenarios: first a dense urban scenario, then a line-shaped, rural one. Based on the results of these case studies, we find that the efficiency gains in ride sharing are relatively small and potentially hard to justify against quality of service concerns such as reduced convenience, loss of privacy, and higher total travel and drive times. Furthermore, in the assessed scenarios, the relatively low occupancy of the vehicles suggests that smaller vehicles with 4–6 seats, able to handle occasional ride sharing, may be preferable to larger and more expensive vehicles such as minibuses.

Claudio Ruch

Papers

Fleet operational policies for automated mobility: A simulation assessment for Zurich

AMoDeus, a Simulation-Based Testbed for Autonomous Mobility-on-Demand Systems

Design and control of a spherical omnidirectional blimp

Model Predictive Control of Ride-sharing Autonomous Mobility-on-Demand Systems

Quantifying the Efficiency of Ride Sharing