Abstraction and Control for Groups of Robots

This paper addresses the general problem of controlling a large number of robots required to move as a group. We propose an abstraction based on the definition of a map from the configuration space Q of the robots to a lower dimensional manifold A, whose dimension is independent of the number of robots. In this paper, we focus on planar fully actuated robots. We require that the manifold has a product structure A = G x S, where G is a Lie group, which captures the position and orientation of the ensemble in the chosen world coordinate frame, and S is a shape manifold, which is an intrinsic characterization of the team describing the “shape” as the area spanned by the robots. We design decoupled controllers for the group and shape variables. We derive controllers for individual robots that guarantee the desired behavior on A. These controllers can be realized by feedback that depends only on the current state of the robot and the state of the manifold A. This has the practical advantage of reducing the communication and sensing that is required and limiting the complexity of individual robot controllers, even for large numbers of robots

Extended Preintegration for Relative State Estimation of Leader-Follower Platform

Relative state estimation using exteroceptive sensors suffers from limitations of the field of view (FOV) and false detection, that the proprioceptive sensor (IMU) data are usually engaged to compensate. Recently ego-motion constraint obtained by Inertial measurement unit (IMU) preintegration has been extensively used in simultaneous localization and mapping (SLAM) to alleviate the computation burden. This paper introduces an extended preintegration incorporating the IMU preintegration of two platforms to formulate the motion constraint of relative state. One merit of this analytic constraint is that it can be seamlessly integrated into the unified graph optimization framework to implement the relative state estimation in a high-performance real-time tracking thread, another point is a full smoother design with this precise constraint to optimize the 3D coordinate and refine the state for the refinement thread. We compare extensively in simulations the proposed algorithms with two existing approaches to confirm our outperformance. In the real virtual reality (VR) application design with the proposed estimator, we properly realize the visual tracking of the six degrees of freedom (6DoF) controller suitable for almost all scenarios, including the challenging environment with missing features, light mutation, dynamic scenes, etc. The demo video is at https://www.youtube.com/watch?v=0idb9Ls2iAM. For the benefit of the community, we make the source code public

Continuous-Time Range-Only Pose Estimation

Range-only (RO) localization involves determining the position of a mobile robot by measuring the distance to specific anchors. RO localization is challenging since the measurements are low-dimensional and a single range sensor does not have enough information to estimate the full pose of the robot. As such, range sensors are typically coupled with other sensing modalities such as wheel encoders or inertial measurement units (IMUs) to estimate the full pose. In this work, we propose a continuous-time Gaussian process (GP)- based trajectory estimation method to estimate the full pose of a robot using only range measurements from multiple range sensors. Results from simulation and real experiments show that our proposed method, using off-the-shelf range sensors, is able to achieve comparable performance and in some cases outperform alternative state-of-the-art sensor-fusion methods that use additional sensing modalities

Coordination of Multirobot Teams and Groups in Constrained Environments: Models, Abstractions, and Control Policies

Robots can augment and even replace humans in dangerous environments, such as search and rescue and reconnaissance missions, yet robots used in these situations are largely tele-operated. In most cases, the robots\u27 performance depends on the operator\u27s ability to control and coordinate the robots, resulting in increased response time and poor situational awareness, and hindering multirobot cooperation. Many factors impede extended autonomy in these situations, including the unique nature of individual tasks, the number of robots needed, the complexity of coordinating heterogeneous robot teams, and the need to operate safely. These factors can be partly addressed by having many inexpensive robots and by control policies that provide guarantees on convergence and safety. In this thesis, we address the problem of synthesizing control policies for navigating teams of robots in constrained environments while providing guarantees on convergence and safety. The approach is as follows. We first model the configuration space of the group (a space in which the robots cannot violate the constraints) as a set of polytopes. For a group with a common goal configuration, we reduce complexity by constructing a configuration space for an abstracted group state. We then construct a discrete representation of the configuration space, on which we search for a path to the goal. Based on this path, we synthesize feedback controllers, decentralized affine controllers for kinematic systems and nonlinear feedback controllers for dynamical systems, on the polytopes, sequentially composing controllers to drive the system to the goal. We demonstrate the use of this method in urban environments and on groups of dynamical systems such as quadrotors. We reduce the complexity of multirobot coordination by using an informed graph search to simultaneously build the configuration space and find a path in its discrete representation to the goal. Furthermore, by using an abstraction on groups of robots we dissociate complexity from the number of robots in the group. Although the controllers are designed for navigation in known environments, they are indeed more versatile, as we demonstrate in a concluding simulation of six robots in a partially unknown environment with evolving communication links, object manipulation, and stigmergic interactions

Study of robotics systems applications to the space station program

Applications of robotics systems to potential uses of the Space Station as an assembly facility, and secondarily as a servicing facility, are considered. A typical robotics system mission is described along with the pertinent application guidelines and Space Station environmental assumptions utilized in developing the robotic task scenarios. A functional description of a supervised dual-robot space structure construction system is given, and four key areas of robotic technology are defined, described, and assessed. Alternate technologies for implementing the more routine space technology support subsystems that will be required to support the Space Station robotic systems in assembly and servicing tasks are briefly discussed. The environmental conditions impacting on the robotic configuration design and operation are reviewed

Survey on Motion Planning for Multirotor Aerial Vehicles in Plan-based Control Paradigm

In general, optimal motion planning can be performed both locally and globally. In such a planning, the choice in favour of either local or global planning technique mainly depends on whether the environmental conditions are dynamic or static. Hence, the most adequate choice is to use local planning or local planning alongside global planning. When designing optimal motion planning both local and global, the key metrics to bear in mind are execution time, asymptotic optimality, and quick reaction to dynamic obstacles. Such planning approaches can address the aforesaid target metrics more efficiently compared to other approaches such as path planning followed by smoothing. Thus, the foremost objective of this study is to analyse related literature in order to understand how the motion planning, especially trajectory planning, problem is formulated, when being applied for generating optimal trajectories in real-time for Multirotor Aerial Vehicles, impacts the listed metrics. As a result of the research, the trajectory planning problem was broken down into a set of subproblems, and the lists of methods for addressing each of the problems were identified and described in detail. Subsequently, the most prominent results from 2010 to 2022 were summarized and presented in the form of a timeline

Advancements in Adversarially-Resilient Consensus and Safety-Critical Control for Multi-Agent Networks

The capabilities of and demand for complex autonomous multi-agent systems, including networks of unmanned aerial vehicles and mobile robots, are rapidly increasing in both research and industry settings. As the size and complexity of these systems increase, dealing with faults and failures becomes a crucial element that must be accounted for when performing control design. In addition, the last decade has witnessed an ever-accelerating proliferation of adversarial attacks on cyber-physical systems across the globe. In response to these challenges, recent years have seen an increased focus on resilience of multi-agent systems to faults and adversarial attacks. Broadly speaking, resilience refers to the ability of a system to accomplish control or performance objectives despite the presence of faults or attacks. Ensuring the resilience of cyber-physical systems is an interdisciplinary endeavor that can be tackled using a variety of methodologies. This dissertation approaches the resilience of such systems from a control-theoretic viewpoint and presents several novel advancements in resilient control methodologies. First, advancements in resilient consensus techniques are presented that allow normally-behaving agents to achieve state agreement in the presence of adversarial misinformation. Second, graph theoretic tools for constructing and analyzing the resilience of multi-agent networks are derived. Third, a method for resilient broadcasting vector-valued information from a set of leaders to a set of followers in the presence of adversarial misinformation is presented, and these results are applied to the problem of propagating entire knowledge of time-varying Bezier-curve-based trajectories from leaders to followers. Finally, novel results are presented for guaranteeing safety preservation of heterogeneous control-affine multi-agent systems with sampled-data dynamics in the presence of adversarial agents.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168102/1/usevitch_1.pd

Towards Better Navigation: Optimizing Algorithms for Mapping, Localization and Planning

Navigation is the problem of going from one place to another. It is an important problem in the areas of autonomous driving, mobile robotics and robotic manipulation. When navigation algorithms realize their potential, autonomous driving will lead to a new era of mobility, enabling greater independence for the disabled and greater convenience for all. It will reduce car ownership, increase fuel efficiency and reduce traffic jams. The navigation problem needs to be solved efficiently. It is well understood that slow reaction time in driving can be fatal. For self-driving cars to be safe, the navigation algorithms have to be optimized without compromising accuracy. In this thesis, we focus on optimizing algorithms in the navigation domain. Navigation is often addressed in three parts: mapping, localization and planning. Mapping is the problem of building a representation (a map) of the environment from noisy observations. Mapping is the slowest step in the navigation pipeline because the set of possible maps grows exponentially with the size of the map. This makes optimizing mapping crucial for optimizing navigation. We focus on grid based mapping algorithms that divide the space into grid cells. The state of the art (SOTA) grid based mapping algorithms have either of two limitations. They either make a limiting assumption of each grid-cell being independent of its neighboring cells or they use slow sampling based methods like Gibbs sampling and Metropolis Hastings. Although the sampling based methods are guaranteed to converge, they are slow in practice because they do not fully utilize the relationships among random variables. We avoid the independent cell assumption and use modern inference methods like Belief Propagation and Dual Decomposition, instead of sampling based methods. These modern methods not only converge up to two times faster but also lead to more accurate maps. Localization, another part of navigation, is the problem of finding the robot's position with respect to the environment or the map. It is usually carried out under two restrictive assumptions: (1) there is only one robot in the environment (2) the map (or its estimate) is known. We relax the former assumption by recognizing the fact that robots can cooperatively map the environment faster. We propose a polynomial root-finding based mutual localization algorithm that uses observations from two robots. Our algorithm depends upon only a few fiducial markers instead of external landmarks, used by methods like Bundler, which makes it faster. The final step of navigation, called planning, is the problem of taking actions based on the map, the robot's position and desired destination. Reinforcement learning (RL) is a popular algorithm for planning, when the effect of actions on the environment are not easily modeled. A special class of RL algorithms, called Goal-conditioned RL, is applied to cases when the goal location can change for every trial. The SOTA algorithms in Goal-conditioned RL specify the goal location in multiple, redundant ways. Due to this redundant information, algorithms like Hindsight Experience Replay re-samples rewards which makes them slow. We propose a deep extension to Floyd-Warshall Reinforcement Learning which removes of this redundant information thus avoiding rewards re-sampling. The resultant algorithm requires only half the reward samples as required by the baselines.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/150011/1/dhiman_1.pd