This paper considers the robotic on-orbit servicing of a client satellite. We present an adaptive robotic system to perform two on-orbit tasks: client manipulation and jammed component dislodging (e.g., a solar panel). We use adaptive control due to the uncertainty of the client dynamics and the stiffness of the jammed component. We present two methods for dislodging: a decentralized approach with multiple free-fiying collaborating agents and then transferring the same decentralized approach to a multi-robot arms system. We present simulation studies for these on-orbit servicing tasks. These studies validate the effectiveness of the presented controller despite uncertainty and unmodeled dynamics, such as nonlinear friction.

This paper addresses the landing problem of a quadrotor on an unpredictable moving vehicle, using a robust image-based visual servoing (IBVS) method. The circle-based image moments are defined to construct image dynamics, and the passivity-like property of the circle features is preserved by reprojecting to the virtual image plane. The landing control system is decoupled into translation and rotation modules due to the rotation invariance of the proposed circle features. First, by exploiting the error transformation in the image space, a robust IBVS controller can overcome the lack of both the desired depth information of target features and the velocity feedback of the target. Next, an adaptive geometric attitude controller is developed directly using rotation matrices to avoid the singularities of Euler-angles and the ambiguity of quaternions. One benefit of the proposed scheme is that it can potentially improve the camera visibility, guarantee the transient and steady-state behaviors in image space, and be efficiently implemented on the low-cost quadrotor. Finally, The stability analysis is presented using Lyapunov stability theory on cascaded systems, and the effectiveness of the proposed control strategy is demonstrated through simulations and experiments. Note to Practitioners—The motivation of this paper is to investigate a practical control strategy for the image-based landing control of underactuated quadrotors on an unpredictable moving vehicle. In most of the existing image-based landing control schemes for underactuated quadrotors, having the prior predictive model of the moving landing vehicle to provide a feed-forward compensation during the landing maneuver. However, due to the fact that the landing environment and vehicle are primarily stochastic, resulting in no predictive models are valid in practice. Therefore, this paper suggests a robust image-based landing control strategy without the model or state of the moving landing vehicle. In particular, a novel virtual circle feature, possessing the characteristic of rotation invariance, is designed for the landing of underactuated quadrotors, which decouples the landing system and simplifies the control design. Moreover, the image feature errors are directly retained within prescribed performance funnels in the image space. As a result, the transient and steady-state landing behaviors can be implicitly guaranteed in Cartesian space. The stability and convergence of the system are analyzed mathematically and the experiment using quadrotors provides promising results. In ongoing research, we are addressing the issues of collision avoidances and unknown disturbances to provide a more realistic setup for the autonomous deployment and recovery of underactuated quadrotors in GPS-denied environments.

This article addresses the vision-based landing problem of a low-cost quadrotor on an unknown moving platform. A robust landing controller is developed, which consists of the design of the low-complexity outer-loop controller and the geometric inner-loop attitude controller. First, an error transformation based on prescribed performance is designed to guarantee the landing behaviors and deal with the intermediate control signal of backstepping approaches, resulting in a low-complexity position-based visual servoing (PBVS) design. In addition, the proposed PBVS controller exhibits strong robustness against an uncertain relative dynamic system due to no incorporation of any prior knowledge of the moving platform. Next, a modified geometric attitude controller is presented by characterizing the geometric properties of rotation matrices intrinsically. Finally, the stability analysis is presented using Lyapunov stability theory, and the effectiveness of the proposed control strategy is demonstrated through numerical simulations and experiments.

This article aims to solve the problem of formation control of mobile robots based on image and provide a low-cost as well as ease-of-implementation solution for mobile robots relying merely on a monocular camera under field-of-view (FOV) constraints. A low-complexity image-based visual servo controller is proposed, which can achieve the desired relative position on the image plane and solve the FOV constraints without the feature depth and leader’s velocities information. To facilitate the control design, a state transformation is first performed to decouple the visual motion kinematics. Then, an error transformation is introduced to handle the FOV constraints, and performance specifications are incorporated in the error transformation to achieve the predefined control performance. Finally, a simple static controller is derived using only information from images, and the stability of the uncertain system with unknown control direction/coefficients under the given performance control condition is analyzed. The effectiveness and performance of the proposed visual servoing controller can be illustrated using both simulations and experiments.

The problem of consensus in networked agent systems is revisited and applied to vision-based localization. A class of new consensus dynamics is introduced first, and sufficient conditions including the persistence of excitation on the coupling matrix for reaching consensus are derived. As an application of the proposed consensus dynamics, an adaptive localization algorithm then is proposed for autonomous robots equipped with primarily visual sensors in GPS-denied environments. In the context of consensus over an undirected tree topology, the convergence of the proposed localization algorithm is proved. Finally, both numerical simulations and physical experiments are presented to show the effectiveness of the proposed localization algorithm. Our algorithm is simpler to implement and computationally cheaper compared to other localization methods. Moreover, it is immune to error accumulation and long-term stable, and the asymptotical convergence of the estimation errors can be theoretically guaranteed.

This article addresses the security of distributed secondary control of inverter-based distributed energy resources (DERs) in microgrids. The proposed cybersecure scheme utilizes the weighted mean subsequence reduced algorithm at each DER to discard the corrupted information received from neighboring DERs. This algorithm requires the connectivity of underlying communication graph to be above a specific threshold. To cope with this requirement, a methodology is proposed such that each DER is able to virtually change the quality of communication links connected to that DER to enhance the connectivity of communication graph. Two islanded microgrid test systems are simulated to validate the effectiveness of proposed cybersecure secondary control.

The problem of the leader-following formation control of nonholonomic mobile robots is addressed in this paper. A distributed formation control strategy using explicitly the coordination errors among robots is proposed without assuming that each follower robot knows the full state of the leader. First, a distributed estimation law is proposed for each follower robot to estimate the states, including the position, orientation and linear velocity of the leader. The distributed formation control law is then designed based on the estimated states of the leader and the neighborhood formation tracking error. Under some mild assumptions on the interaction graph among the leader and the follower robots and the velocity of the leader, asymptotic convergence of formation tracking errors to zero can be achieved. Finally, some numerical simulations and experiments on a group of nonholonomic mobile robots are presented to demonstrate the effectiveness of the proposed strategy.

Recent technology advances significantly push forward the development and the deployment of the concept of smart, such as smart community and smart city. Smart transportation is one of the core components in modern urbanization processes. Under this context, the connected autonomous vehicle (CAV) system presents a promising solution towards the enhanced traffic safety and mobility through state-of-the-art wireless communications and autonomous driving techniques. Being capable of collecting and transmitting real-time vehicle-specific, location-specific, and area-wide traffic information, it is believed that CAV-enabled transportation systems will revolutionize the existing understanding of network-wide traffic operations and reestablish traffic flow theory. This paper develops a new continuum dynamics model for the future CAV-enabled traffic system, realized by encapsulating mutually-coupled vehicle interactions using virtual internal and external forces. Leveraging Newton's second law of motion, our model naturally preserves the traffic volume and automatically handles both the longitudinal and lateral traffic operations due to its 2-D nature, which sets us apart from the existing macroscopic traffic flow models. Our model can also be rolled back to handle the conventional traffic of human drivers, and the experiment shows that the model describes real-world traffic behavior well. Therefore, we consider the proposed model a complement and generalization of the existing traffic theory. We also develop a smoothed particle hydrodynamics-based numerical simulation and an interactive traffic visualization framework. By posing user-specified external constraints, our system allows users to visually understand the impact of different traffic operations interactively.

In this paper, we consider a problem of formation control for two teams of robots. Synchronization inside each team, intra-group, and between the two teams, inter-group, are important in applications where robots belonging to different teams need to coordinate to perform a given task. We are motivated by the work reported in [1] in which a group of planar agents is driven into a special case of phase balancing known as splay state, i.e., a circular formation with equally spaced phases among the robots in the team. In this paper, we are interested in achieving not only splay state but also phase alignment between the agents in the two groups. We formally analyze the stability of the desired intra-group and inter-group phase configuration state. Simulation and experimental results employing a team of terrestrial robots and a team of rotorcraft aerial platforms verify the validity of the proposed coordination strategy.

Attaining autonomous flight is an important task in aerial robotics. Often flight trajectories are not only subject to unknown system dynamics, but also to specific task constraints. This paper presents a motion planning method for generating trajectories with minimal residual oscillations (swing-free) for rotorcraft carrying a suspended loads. We rely on a finite-sampling, batch reinforcement learning algorithm to train the system for a particular load. We find criteria that allow the trained agent to be transferred to a variety of models, state and action spaces and produce a number of different trajectories. Through a combination of simulations and experiments, we demonstrate that the inferred policy is robust to noise and the unmodeled dynamics of the system. The contributions of this work are 1) applying reinforcement learning to solve the problem of finding swing-free trajectories for rotorcraft, 2) designing a problem-specific feature vector for value function approximation, 3) giving sufficient conditions for successful learning transfer to different models, state and action spaces, and 4) verification of the resulting trajectories in both simulation and autonomous control of quadrotors with suspended loads.

In this paper, we present a problem where a suspended load, carried by a rotorcraft aerial robot, performs trajectory tracking. We want to accomplish this by specifying the reference trajectory for the suspended load only. The aerial robot needs to discover/learn its own trajectory which ensures that the suspended load tracks the reference trajectory. As a solution, we propose a method based on least-square policy iteration (LSPI) which is a type of reinforcement learning algorithm. The proposed method is verified through simulation and experiments.

In this paper, we address the problem of agile swing-free trajectory tracking of a quadrotor with a suspended load. This problem has great practical significance in many UAV applications. However, it has received little attention in the literature so far. Flying with a suspended load can be a very challenging and sometimes hazardous task as the suspended load significantly alters the flight characteristics of the quadrotor. In order to deal with this problem, we propose a technique based on dynamic programming which ensures swing-free trajectory tracking. We start by presenting the mathematical model of a quadrotor with suspended load dynamics and kinematics. A high-level planner is used to provide desired waypoints, and then a dynamic programming approach is used to generate the swing-free trajectory for the quadrotor carrying a suspended load. Effectiveness of this method is demonstrated by numerical simulations and experiments.

In this paper we relax the assumption of network connectivity within the sensor network and introduce mobile communication relays to the network. This addition converts the homogeneous sensor network to a heterogeneous one. Based on the communication geometry of both sensing and communication relay agents we derive communication constraints within the network that guarantee network connectivity. We then define a heterogeneous proximity graph that encodes the communication links that exist within the heterogeneous network. By specifying particular edge weights in the proximity graph, we provide a technique for biasing particular connections within the heterogeneous sensor network. Through a minimal spanning tree approach, we show how to minimize communication links within the network which allows for larger feasible motion sets of the sensing agents that guarantee the network remains connected. We also provide an algorithm that allows for adding communication links to the minimal spanning tree of the heterogeneous proximity graph to create a biconnected graph that is robust to a single node failure. We then combine a prioritized search algorithm and the communication constraints to provide a decentralized prioritized sensing control algorithm for a heterogeneous sensor network that maintains network connectivity.

This paper shows the capability of our environmental testbed to validate novel cooperative control algorithms that rely on measurements from the environment. Through the design of our mounting plates, the testbed can accommodate multiple environmental sensors as well as different configurations of robot accessories. Also, our environmental sensor suite provides the ability to integrate a variety of sensors that provide measurements of physical quantities to address many different types of cooperative control algorithms.

In this paper we present our multi-vehicle testbed that was designed for verification and validation of cooperative control algorithms involving environmental sensing. Two cooperative control algorithms: prioritized multi-sensing behavior, and a distributed adaptive algorithm for nonholonomic sensor networks are qualitatively verified using our multi-vehicle testbed. The multi-vehicle testbed allows for a straightforward transition from simulation to experimenting on actual hardware and has the flexibility to interface various types of sensors, vehicles, as well as enable indoor and outdoor experiments.

In this paper we present a formation synchronization and reconfiguration scheme based on distributed computation. In our previous work, we have introduced a formation abstraction by means of a virtual rectangular grid. The grid was used to ensure collision-free transitions between formations, coordinated by a centralized controller. This paper is an extension of that work, proposing control laws that are computed in a distributed way while still ensuring collision-free formation reconfigurations. Furthermore, we consider an extension of the group rendezvous problem. The agents are required to meet at the rendezvous point and establish consensus on formation state. Again, the grid abstraction is used to describe the formation state. Nonholonomic constraints of agent motion are taken into account explicitly. As our approach is communication-based, we also examine the effects of temporary communication loss.

This paper presents a decentralized coordination algorithm that allows a team of sensor-enabled robots to navigate a region containing non-convex obstacles and take measurements within the region that contain the highest probability of having ¿good¿ information first. This approach is motivated by scenarios where prior knowledge of the search space is known or when time constraints are present that limit the amount of area that can be searched by a robot team. Practical applications include search and rescue, target detection, and hazardous contaminations. Our cooperative control algorithm combines Voronoi partitioning, a global optimization technique, and a modified navigation function to prioritize sensor detection. The issues we address such as non-convex obstacles as well as global search are not extensively addressed in the current literature. Simulation results of the control algorithm are given and validate the prioritized sensing behavior as well as the collision avoidance property.

In this paper we present a formation reconfiguration methodology designed for controlling groups of autonomous agents in environments densely populated with obstacles. Our approach is based on abstracting the group of agents by a discrete rectangular grid. Agent and obstacle positions are mapped onto the formation grid. Then, collision free formation transition trajectories are computed using discrete event scheduling techniques that have been well-established in the manufacturing systems domain. The main contribution of this paper is a unified formation control framework that explicitly takes obstacles into account. Using discrete event system analysis tools we show that our approach guarantees convergence to the desired formation while avoiding obstacles and inter-agent collisions.

This paper presents a multi-vehicle platform and framework for robotics education and research. The system is designed as an educational tool for introducing children to engineering and robotics and is composed of hardware and software components that allow users to easily design and implement sophisticated robotic behaviors. We formally introduce the robotic game Marco Polo as a problem that mimics the pursuit-evasion game often played by children in swimming pools. Specifically, we address the question of finding a pursuit strategy under the condition of intermittent communication. Finally, we present an implementation of the Marco Polo game

This paper presents a vision-based control strategy for decentralized stabilization of unmanned vehicle (UV) formations. The key point of the algorithm is that it only requires knowledge of the leader-follower relative distance and bearing. The approach is based on an output feedback controller that uses a high-gain observer to estimate derivatives of UVs relative positions. Both data are measured using a pan-controlled camera on-board the following robot which eliminates sensitivity to information flow among vehicles. A Lyapunov stability analysis guarantees that the closed-loop system is stable and the formation error can be made arbitrarily small. A virtual environment and a vision system are used to validate the proposed methodology

In this paper, we consider the task of repositioning a formation of robots to a new shape while minimizing either the maximum distance that any robot travels, or the total distance traveled by the formation. We show that optimal solutions in SE (2) can be achieved for either metric through second-order cone programming (SOCP) techniques. For the case where the orientation of the new formation shape is fixed, we obtain optimal solutions in both R2and R3. The latter also allows for complete regulation of the formation size via constraints on the shape scale. We expect that these results will prove useful for extending the mission lives of robot formations and mobile ad-hoc networks (MANETs).

We present the modeling and analysis of distributed autonomous robots using the specification language for hybrid systems, called CHARON. Coordination between distributed autonomous robots has attracted researchers of embedded and hybrid systems, since there has been increasing demand for multiple robots working together in a dynamically changing or unknown environment to carry out missions such as search and rescue, cooperative localization, and scouting and reconnaissance. To maximize the capability of performing tasks collaboratively as a team, formation control is one of crucial parts in developing distributed autonomous robots. In this paper formation control of a team of robots is modeled using CHARON and the model is analyzed using simulation with assertion checking capability of the CHARON toolset.

We describe a framework for cooperative control of a group of nonholonomic mobile robots that allows us to build complex systems from simple controllers and estimators. The resultant modular approach is attractive because of the potential for reusability. Our approach to composition also guarantees stability and convergence in a wide range of tasks. There are two key features in our approach: 1) a paradigm for switching between simple decentralized controllers that allows for changes in formation; 2) the use of information from a single type of sensor, an omnidirectional camera, for all our controllers. We describe estimators that abstract the sensory information at different levels, enabling both decentralized and centralized cooperative control. Our results include numerical simulations and experiments using a testbed consisting of three nonholonomic robots.

This paper considers the problem of vision-based control of a nonholonomic mobile robot. We describe the design and implementation of real-time estimation and control algorithms on a car-like robot platform using a single omni-directional camera as a sensor without explicit use of odometry. We provide experimental results for each of these vision-based control objects. The algorithms are packaged as control modes and can be combined hierarchically to perform higher level tasks involving multiple robots.

We describe a framework for controlling a group of nonholonomic mobile robots equipped with range sensors. The vehicles are required to follow a prescribed trajectory while maintaining a desired formation. By using the leader-following approach, we formulate the formation control problem as a hybrid (mode switching) control system. We then develop a decision module that allows the robots to automatically switch between continuous-state control laws to achieve a desired formation shape. The stability properties of the closed-loop hybrid system are studied using the Lyapunov theory. We do not use explicit communication between robots; instead we integrate optimal estimation techniques with nonlinear controllers. Simulation and experimental results verify the validity of our approach.

We describe a framework for coordinating multiple robots in cooperative manipulation tasks in which vision is used for establishing relative position and orientation and maintaining formation. The two key contributions are a cooperative scheme for localizing the robots based on visual imagery that is more robust than decentralized localization, and a set of control algorithms that allow the robots to maintain a prescribed formation (shape and size). The ability to maintain a prescribed formation allows the robots to "trap" objects in their midst, and to "flow" the formation to a desired position. We derive the cooperative localization and control algorithms and present experimental results that illustrate the implementation and the performance of these algorithms.

This paper considers a stabilizing hybrid scheme to control a class of underactuated mechanical systems. The hybrid controller consists of a collection of state feedback controllers plus a discrete-event supervisor. When the continuous-state hits a switching boundary, a new controller is applied to the plant. Lyapunov theory is used to determine the switching boundaries and to guarantee the stability of the closed-loop hybrid system. This approach is applied to the well-known swing up and balancing control problem of the inverted pendulum.

A control structure that makes possible the integration of a kinematic controller and a neural network (NN) computed-torque controller for nonholonomic mobile robots is presented. A combined kinematic/torque control law is developed using backstepping and stability is guaranteed by Lyapunov theory. This control algorithm can be applied to the three basic nonholonomic navigation problems: tracking a reference trajectory, path following, and stabilization about a desired posture. Moreover, the NN controller proposed in this work can deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics in the vehicle. On-line NN weight tuning algorithms do no require off-line learning yet guarantee small tracking errors and bounded control signals are utilized.

This paper presents a hybrid system framework which considers simultaneously the control and decision-making issues. This reconfigurable framework can accommodate a wide range of situations, from aircraft control systems to mobile manipulators. A continuous-state plant is supervised by a discrete-event system which is based on a theory of linked finite state machines. The composite system is viewed as an iterative process where a task is carried out by changing the structure of the continuous-state plant. An algorithm for a hybrid control design is provided and illustrated through a mobile manipulator example.