Nathan Michael

Trajectory generation and control for precise aggressive maneuvers with quadrotors

We study the problem of designing dynamically feasible trajectories and controllers that drive a quadrotor to a desired state in state space. We focus on the development of a family of trajectories defined as a sequence of segments, each with a controller parameterized by a goal state or region in state space. Each controller is developed from the dynamic model of the robot and then iteratively refined through successive experimental trials in an automated fashion to account for errors in the dynamic model and noise in the actuators and sensors. We show that this approach permits the development of trajectories and controllers enabling such aggressive maneuvers as flying through narrow, vertical gaps and perching on inverted surfaces with high precision and repeatability.

The GRASP Multiple Micro-UAV Testbed

In the last five years, advances in materials, electronics, sensors, and batteries have fueled a growth in the development of microunmanned aerial vehicles (MAVs) that are between 0.1 and 0.5 m in length and 0.1-0.5 kg in mass [1]. A few groups have built and analyzed MAVs in the 10-cm range [2], [3]. One of the smallest MAV is the Picoftyer with a 60-mmpropellor diameter and a mass of 3.3 g [4]. Platforms in the 50-cm range are more prevalent with several groups having built and flown systems of this size [5]-[7]. In fact, there are severalcommercially available radiocontrolled (PvC) helicopters and research-grade helicopters in this size range [8].

Cooperative manipulation and transportation with aerial robots

In this paper we consider the problem of controlling multiple robots manipulating and transporting a payload in three dimensions via cables. We develop robot configurations that ensure static equilibrium of the payload at a desired pose while respecting constraints on the tension and provide analysis of payload stability for these configurations. We demonstrate our methods on a team of aerial robots via simulation and experimentation.

Autonomous multi-floor indoor navigation with a computationally constrained MAV

In this paper, we consider the problem of autonomous navigation with a micro aerial vehicle (MAV) in indoor environments. In particular, we are interested in autonomous navigation in buildings with multiple floors. To ensure that the robot is fully autonomous, we require all computation to occur on the robot without need for external infrastructure, communication, or human interaction beyond high-level commands. Therefore, we pursue a system design and methodology that enables autonomous navigation with real-time performance on a mobile processor using only onboard sensors. Specifically, we address multi-floor mapping with loop closure, localization, planning, and autonomous control, including adaptation to aerodynamic effects during traversal through spaces with low vertical clearance or strong external disturbances. We present experimental results with ground truth comparisons and performance analysis.

Cooperative Grasping and Transport Using Multiple Quadrotors

In this paper, we consider the problem of controlling multiple quadrotor robots that cooperatively grasp and transport a payload in three dimensions.We model the quadrotors both individually and as a group rigidly attached to a payload. We propose individual robot control laws defined with respect to the payload that stabilize the payload along three-dimensional trajectories. We detail the design of a gripping mechanism attached to each quadrotor that permits autonomous grasping of the payload. An experimental study with teams of quadrotors cooperatively grasping, stabilizing, and transporting payloads along desired three-dimensional trajectories is presented with performance analysis over many trials for different payload configurations.

Opportunities and challenges with autonomous micro aerial vehicles

We survey the recent work on micro unmanned aerial vehicles (UAVs), a fast-growing field in robotics, outlining the opportunities for research and applications, along with the scientific and technological challenges. Micro-UAVs can operate in three-dimensional environments, explore and map multi-story buildings, manipulate and transport objects, and even perform such tasks as assembly. While fixed-base industrial robots were the main focus in the first two decades of robotics, and mobile robots enabled most of the significant advances during the next two decades, it is likely that UAVs, and particularly micro-UAVs, will provide a major impetus for the next phase of education, research, and development.

Collaborative mapping of an earthquake-damaged building via ground and aerial robots

We report recent results from field experiments conducted with a team of ground and aerial robots engaged in the collaborative mapping of an earthquake-damaged building. The goal of the experimental exercise is the generation of three-dimensional maps that capture the layout of a multifloor environment. The experiments took place in the top three floors of a structurally compromised building at Tohoku University in Sendai, Japan that was damaged during the 2011 Tohoku earthquake. We provide details of the approach to the collaborative mapping and report results from the experiments in the form of maps generated by the individual robots and as a team. We conclude by discussing observations from the experiments and future research topics.

Vision-Based, Distributed Control Laws for Motion Coordination of Nonholonomic Robots

In this paper, we study the problem of distributed motion coordination among a group of nonholonomic ground robots. We develop vision-based control laws for parallel and balanced circular formations using a consensus approach. The proposed control laws are distributed in the sense that they require information only from neighboring robots. Furthermore, the control laws are coordinate-free and do not rely on measurement or communication of heading information among neighbors but instead require measurements of bearing, optical flow, and time to collision, all of which can be measured using visual sensors. Collision-avoidance capabilities are added to the team members, and the effectiveness of the control laws are demonstrated on a group of mobile robots.

Distributed multi-robot task assignment and formation control

Distributed task assignment for multiple agents raises fundamental and novel problems in control theory and robotics. A new challenge is the development of distributed algorithms that dynamically assign tasks to multiple agents, not relying on a priori assignment information. We address this challenge using market-based coordination protocols where the agents are able to bid for task assignment with the assumption that every agent has knowledge of the maximum number of agents that any given task can accommodate. We show that our approach always achieves the desired assignment of agents to tasks after exploring at most a polynomial number of assignments, dramatically reducing the combinatorial nature of discrete assignment problems. We verify our algorithm through both simulation and experimentation on a team of non-holonomic robots performing distributed formation stabilization and group splitting and merging.

Planning and control for cooperative manipulation and transportation with aerial robots

We consider the planning and control of multiple aerial robots manipulating and transporting a payload in three dimensions via cables. Individual robot control laws and motion plans enable the control of the payload (position and orientation) along a desired trajectory. We address the fact that robot configurations may admit multiple payload equilibrium solutions by developing constraints for the robot configuration that guarantee the existence of a unique payload pose. Further, we formulate individual robot control laws that enforce these constraints and enable the design of non-trivial payload motion plans. Finally, we propose two quality measures for motion plan design that minimize individual robot motion and maximize payload stability along the trajectory. The methods proposed in the work are evaluated through simulation and experimentation with a team of three quadrotors.