Jean-Pierre de la Croix

Flipping the controls classroom around a MOOC

Bridging the theory-practice gap in controls education is a well-known challenge. In this paper, we discuss how one can bridge this gap using a flipped classroom. Based on the recent MOOC (Massive Open Online Course), Control of Mobile Robots, we flipped the classroom in a senior robotics and controls class at the Georgia Institute of Technology. The students participated in the MOOC and came to class prepared to solve controls problems on robots. Key to this experience was not only the delivery of theoretical concepts via the MOOC, but also a hardware/software platform that provided a learning environment where exploratory, practical tinkering was grounded in solid theory. This paper reports on the findings of the flipped classroom experiment, as well as discusses why this classroom format is ideal for controls courses.

Asthmon: empowering asthmatic children's self-management with a virtual pet

Asthma is a common chronic childhood disease. Children spend a majority of their time in schools, and barriers to on-site asthma management have been reported. Previous forms of clinical intervention have regarded patients as passive subjects. However, self-management plays a significant role in caring for asthmatics. We consider asthmatic children and their parents, primary caregivers, as active participants in their treatment and care. To achieve this, we created Asthmon, a portable virtual pet that measures the lung capacity, and instructs appropriate actions to take.

Deformable-medium affordances for interacting with multi-robot systems

This paper addresses the issue of human-swarm interactions by proposing a new set of affordances that make a multi-robot system amenable to human control. In particular, we propose to use clay- a deformable medium- as the “joystick” for controlling the swarm, supporting such affordances as stretching, splitting and merging, shaping, and mixing. The contribution beyond the formulation of these affordances is the coupling of an image recognition framework to decentralized control laws for the individual robots, and the developed human-swarm interaction methodology is applied to a team of mobile robots.

A control lyapunov function approach to human-swarm interactions

In this paper, we seek to establish formal guarantees for whether or not a given human-swarm interaction (HSI) is appropriate for achieving multi-robot tasks. Examples of such tasks include guiding a swarm of robots into a particular geometric configuration. In doing so, we define what it means to impose a HSI control structure on a multi-robot system. Control Lyapunov functions (CLFs) are used to prove that it is feasible for a user to achieve a particular geometric configuration with a multi-robot system under some selected HSI control structure. Several examples of multi-robot systems with unique HSI control structures are provided to illustrated the use of CLFs to establish feasibility.

Interacting with Networks of Mobile Agents

How should human operators interact with teams of mobile agents, whose movements are dictated by decentralized and localized interaction laws? This chapter connects the structure of the underlying information exchange network to how easy or hard it is for human operators to influence the behavior of the team. “Influence” is understood both in terms of controllability, which is a point-to-point property, and manipulability, which is an instantaneous influence notion. These two notions both rely on the assumption that the user can exert control over select leader agents, and we contrast this with another approach whereby the agents are modeled as particles suspended in a fluid, which can be “stirred” by the operator. The theoretical developments are coupled with multirobot experiments and human user-studies to support the practical viability and feasibility of the proposed methods.

Pancakes: A Software Framework for Distributed Robot and Sensor Network Applications

The development of control applications for multi-agent robot and sensor networks is complicated by the heterogeneous nature of the systems involved, as well as their physical capabilities (or limitations).We propose a software framework that unifies these networked systems, thus facilitating the development of multiagent control across multiple platforms and application domains. This framework addresses the need for these systems to dynamically adjust their actuating, sensing, and networking capabilities based on physical constraints, such as power levels. Furthermore, it allows for sensing and control algorithms to migrate to different platforms, which gives multi-agent control application designers the ability to adjust sensing and control as the network evolves. This paper describes the design and implementation of our software system and demonstrates its successful application on robots and sensor nodes, which dynamically modify their operational components.

A Hardware Testbed for Multi-UAV Collaborative Ground Convoy Protection in Dynamic Environments

In this paper, we will present a hardware testbed for multi-UAV systems that bridges the gap between algorithm design and field deployment. The testbed allows for UAV coordination algorithms, that have been shown to work in simulation, to be further tested in an environment where limited on-board computational resources, wireless communication constraints, environmental noise, and differences in the UAVs modeled versus actual dynamics come into effect. In particular, we will introduce an efficient assignment algorithm. This algorithm is used in a multi-UAV ground convoy protection scenario, where UAVs escort the ground convoy and are deployed to check potential threats along the way.

Fast Motion Planning for Agile Space Systems with Multiple Obstacles

In this paper, we develop a novel algorithm for spacecraft trajectory planning in an environment cluttered with many geometrically-fixed obstacles. The Spherical Expansion and Sequential Convex Programming (SE-SCP) algorithm first uses a spherical-expansion-based sampling algorithm to explore the workspace. Once a path is found from the start position to the goal position, the algorithm generates a locally optimal trajectory within the homotopy class using sequential convex programming. If the number of samples tends to infinity, then the SE-SCP trajectory converges to the globally optimal trajectory in the workspace. The SE-SCP algorithm is computationally efficient, therefore it can be used for real-time applications on resource-constrained systems. We also present results of numerical simulations and comparisons with existing algorithms.

Enceladus Vent Explorer Concept

Enceladus Vent Explorer (EVE) is a robotic mission to enter Enceladus vents. It would send two types of modules: Surface Module (SM) and Descent Module (DM). SM is a lander that lands within a few hundred meters from the entrance of an erupting vent. After a successful landing, it deploys a single or multiple DMs. First, a DM moves to a vent and descends into it. It then performs in-situ science investigations in the vent using miniaturized instruments such as microscopic imager and a microfluidics chip. Finally, it collects samples in the vent and delivers to instruments on SM for detailed analysis. Out trade study concluded that the most robust configuration of the DM would be a limbed robot that climbs down the vent using ice screws. The ice screw is a hollow metal screw used by ice climbers for making a strong anchor on ice walls. DM would rely on a power and communication link provided by SM through a tether. Should EVE be realized, it could enable not only the direct confirmation of extraterrestrial life but also the characterization of it. Comparative study of lives on different worlds would provide clues to the secret of the genesis of life.

Autonomous guidance navigation and control for agile quadrotors using polynomial trajectory planning and li adaptive control

We address the challenge to allow efficient autonomous flight in real world environments, both indoor and outdoor. We use a straight-line SE-SCP to find an initial route through the environment and minimum snap trajectory generation using piecewise polynomials. Then, we implement an adaptive robust control able to address some robustness issues for quadrotors in outdoor flight, such as mass variation and wind disturbances. Coupling these techniques we allow highspeed and aggressive autonomous flight through obstacle-dense indoor environments, as well as address outdoor disturbances.