Fast autonomous motion in cluttered and unknown environments, such as forests, is highly dependent on low-latency obstacle avoidance strategies. In this context, this paper presents a motion planning strategy that relies on lattices for the fast computation of local paths that both avoid obstacles and follow a vector field that encodes the global robot task. Lattices are constructed in the sensor space and represent a set of search trees that can be quickly pruned in function of the detected obstacles. The remaining lattice trees are used to optimize a vector field-dependent functional, thus generating the best free local path that tracks the field. To illustrate the proposed approach, we present simulation and real-world experiments of a planar robot moving in a cluttered, forest-like environment.

This paper presents a tangle-free frontier based exploration algorithm for planar mobile robots equipped with limited length and anchored tethers. After planning a path to the closest point in the frontier between free and unknown space, the robot computes an estimate of the future length of its tether and decides, by comparing the anticipated length with the minimum possible tether length, whether the path should be followed or not. If the anticipated tether is longer than the minimum tether by a function of the expected radius of the obstacles, a path planner with homotopic constraints is used to plan a path that brings the robot tether to the same homotopy class of the shortest tether. This behavior will not only limit the tether length but also will prevent tether entangling on the obstacles of the environment. We evaluate our method in different simulated environments and illustrate the approach with an actual tethered robot.

This paper presents the robust Adaptive unscented Kalman filter (RAUKF) for attitude estimation. Since the proposed algorithm represents attitude as a unit quaternion, all basic tools used, including the standard UKF, are adapted to the unit quaternion algebra. Additionally, the algorithm adopts an outlier detector algorithm to identify abrupt changes in the UKF innovation and an adaptive strategy based on covariance matching to tune the measurement covariance matrix online. Adaptation and outlier detection make the proposed algorithm robust to fast and slow perturbations such as magnetic field interference and linear accelerations. Experimental results with a manipulator robot suggest that our method overcomes other algorithms found in the literature.

This paper presents the model identification and the velocity control of an autonomous car. The control system was designed so that the car is controlled at low speeds, where the main applications for the vehicle's autonomous operations include parking and urban adaptive cruise control. A longitudinal model of the car was used in the control loop to compensate the nonlinear behavior of its dynamics. Since the determination of the vehicle's model is a difficult step in the design of model-based controllers, the main contribution of this paper is the use of an empirically determined model to this end. In this paper, the structure of the model was conceived from the car's physics equations, but its parameters were estimated using data-based identification techniques. An important contribution of this paper is the fact that, although the model is strictly linear, we can change its parameters as a function of the operation point of the vehicle to represent the engine's and the transmission's nonlinear behaviors. Moreover, in this paper, we propose a way to include changes in the longitudinal dynamics caused by the automatic gear shifting. The validation of the proposed controller was conducted by computer simulations and real-world experiments.

This paper addresses the problem of coordinating the motion of multiple fixed-wing Unmanned Aerial Vehicles (UAVs) following closed intersecting curves. We require that each UAV avoid collisions with its teammates without changing its predefined, periodic path. Also, each robot must keep a minimum speed to avoid stall and a maximum speed determined by its physical constraints. The centralized solution presented in this paper is modeled as a Mixed Integer Linear Programming (MILP) problem. The solution to this problem, which maximizes safeness (in the sense of collision avoidance), determines, for each UAV, the start time and the velocity profile over the curve.

The focus of this study is on the design of feedback control laws for swarms of robots that are based on models from fluid dynamics. We apply an incompressible fluid model to solve a pattern generation task. Possible applications of an efficient solution to this task are surveillance and the cordoning off of hazardous areas. More specifically, we use the smoothed-particle hydrodynamics (SPH) technique to devise decentralized controllers that force the robots to behave in a similar manner to fluid particles. Our approach deals with static and dynamic obstacles. Considerations such as finite size and nonholonomic constraints are also addressed. In the absence of obstacles, we prove the stability and convergence of controllers that are based on the SPH method. Computer simulations and actual robot experiments are shown to validate the proposed approach.

Different robotic tasks can be solved by controlling a robot to circulate along curves. These include, for example, border inspection and surveillance, multirobot manipulation, and pattern generation. In a previous, work we have proposed a vector field approach for robot convergence and circulation along time-varying curves embedded in N-dimensional spaces. In the present work we instantiate this approach for three-dimensional spaces and, for the first time, show the efficacy of this method to control actual robots. Besides new theoretical analysis when constant speed control is applied, we present experimental results with aerial (quadrotors) and ground (differential-driven) robot.

This paper presents a methodology for computation of artificial vector fields that allows a robot to converge to and circulate around generic curves specified in$n$-dimensional spaces. These vector fields may be directly applied to solve several robot-navigation problems such as border monitoring, surveillance, target tracking, and multirobot pattern generation, with special application to fixed-wing aerial robots, which must keep a positive forward velocity and cannot converge to a single point. Unlike previous solutions found in the literature, the approach is based on fully continuous vector fields and is generalized to time-varying curves defined in$n$-dimensional spaces. We provide mathematical proofs and present simulation and experimental results that illustrate the applicability of the proposed approach. We also present a methodology for construction of the target curve based on a given set of its samples.

We address the problem of pattern generation in obstacle-filled environments by a swarm of mobile robots. Decentralized controllers are devised by using the Smoothed Particle Hydrodynamics (SPH) method. The swarm is modelled as an incompressible fluid subjected to external forces. Actual robot issues such as finite size and nonholonomic constraints are also addressed. Collision avoidance guarantees are discussed. Finally, in the absence of obstacles, we prove for the first time stability and convergence of controllers based on the SPH.

Several recent works have combined discrete and continuous motion planning methods for robot navigation and control. The basic idea of some of these works is to plan a path, by determining a sequence of neighboring discrete regions of the configuration space, and to assign a vector field that drives the robots through these regions. This paper addresses the problem of efficiently computing vector fields over a sequence of consecutive triangles. Differently from previous numerical approaches, which were not able to compute fully continuous fields in triangulated spaces, this paper presents an algorithm that is able to compute guaranteed continuous vector fields over a sequence of adjacent triangles.

This paper presents experimental results on the localization of a mobile robot equipped with relative frequent and absolute infrequent sensors. The relative sensors used are two: a wheel based odometry and a visual based odometry. The absolute sensor is a vision based landmark detector that computes the pose of the robot relative to a pre-mapped visual beacon. This would be a simple sensor fusion problem, which could be solved using standard recursive estimators, if we would not have considered two extra characteristics of the beacon detector: (1) since we assume a monocular vision system and a planar visual mark, the localization problem presents up to four possible solutions; and (2) the frequency that the robot meets a visual mark is very low (0.01 Hz or less). To consider these characteristics, we propose the use of a particle filter with a very precise prediction step (obtained by combining the two odometry sensors available) and a correction step that considers the multi-modal characteristic of the data. Besides, the sensor fusion algorithm, the paper also describes the development of the visual sensors used in the localization process.

We propose and implement a hybrid navigational strategy for mobile robots for data collection tasks in sparsely deployed sensor networks. Our approach presents an alternative to the routing algorithms used in wireless sensor networks which focus on the minimization of energy consumption of each node which is mainly due to data transmission. The method has two layers: i) Reactive Layer in which the data collected from a sensor node is modelled as a potential function whose gradient attracts the robot and ii) Planning Layer, that dictates the action the robot must take to collect sensor data. Results from both simulation and real world experiments showing the performance of the proposed methodology are shown, compared and discussed.

This paper describes the derivation of dynamical models for mobile robots using system-identification methods and shows how such models can be used in the control of autonomous devices. Unlike traditional approaches that are based on the system physics alone, in this paper, an autoregressive model structure is derived from the physics of the process, and its parameters are estimated using real input and output data. From the resulting model, efficient control can be obtained for centralized and remotely controlled robots (typically externally observed) with little or no local "intelligence," thus overcoming many of the severe shortcomings presented by such systems. System dead time is one example of a serious problem that can be efficaciously minimized by using the model as an online predictor. Several experimental results using small differential-driven robots are included to demonstrate the applicability and robustness of the suggested procedure in actual real-world scenarios

This paper addresses the problem of efficiently computing robot navigation functions. Navigation functions are potential functions free of spurious local minima that present an exact solution to the robot motion planning and control problem. Although some methodologies were found in the literature, none of them are easy to implement and generalize for complex shaped workspaces and robots. We discuss some of the difficulties encountered in the current methodologies and propose a novel approach using a Finite Element method for potential field computation.

This paper addresses the problem of collecting sensoring information from wireless sensor networks using mobile robots. We consider that sensors may interact with each other only to maintain the network topology. Because transmitting sensoring data over the network to a remote base is a very power consuming operation, we deploy mobile robots through the sensor field in order to collect such information. We present a multi-target, dynamical, potential field strategy where a robot is attracted to the sensors that have less remaining data storage capacity. Moreover, the robot is also attracted to other sensors in its path without compromising its principal mission. Our controllers guarantee the robot will never get stuck in undesirable local minima. Simulated examples with a single robot collecting data in a sensor field illustrate the elegancy of the methodology.

We address the problem of controlling a team of robots subject to constraints on relative positions. We adopt the general framework of leader-follower control in which a network of controllers is used to control the position and orientation of the team and its shape. We propose two improvements to this scheme. First, we introduce cooperative leader-following where the motion of a robot is determined not only by its leader, but also by other robots including their followers. Second, we allow constraints that are induced by limitations on ranges of sensors and wireless network cards. Our approach is based on potential field controllers for each robot and the on-line modification of these controllers to accommodate motion constraints induced by other robots in the group. We present experimental results with a team of three car-like robots equipped with omnidirectional cameras and 802.11b network cards.

Multi-agent systems (AMS) are developed using a variety of architectures. These range from peer-to-peer to blackboard-like communication, from insecure distributed systems to complex security schemes, from those MAS using databases to those using knowledge based information systems. Although a system's architecture often has to be designed according to its specific application domain, we present a reliable MAS architecture suitable for most purposes in the e-business domain. This framework combines the distributed nature of a multi-agent system with the security and encryption facilities provided by Web servers, separating data from knowledge and encouraging the use of distributed data structures in a concurrent, scalable, transaction-safe and remote-event generator. Notes on agent reputation assessment are also included. We also endow our agents with learning capabilities for two different aspects: reinforcement learning for bidding and a concept learning technique for building user profiles. Reinforcement learning allows for agent adaptive bidding whereas concept learning is of key importance to agent service bundling. The main features of the proposed architecture are agent communication through a common area, remote user access through servlets with authentication and encryption, data/knowledge separation and agent reputation assessment using customer satisfaction values. The functionality of all the proposed multi-agent system features is illustrated through an agent-based travel services application (MASTAS).

A semi-autonomous robot for autonomous installation and removal of aircraft warning spheres on overhead wires of electric power transmission lines is described. Hybrid systems methodology was used to model the basic modes of operation of the robot, which includes both autonomous and tele-operation possibilities. Several sensors and actuators including a custom made gripper are used to accomplish the demanded tasks. The robot was built and tested in real operational situations and proved to be both efficient and robust. It will enable power distribution companies to replace current highly dangerous operations which are normally performed by a human operator placed in a helicopter hovering a few meters away of hotlines. Broader use of this robot in several other common tasks such as line inspection and cleaning, cable changing and object transportation are discussed.

Addresses the problem of coordinating multiple mobile robots in a tightly coupled task by means of implicit communication. This approach allows the development of controllers that do not depend on any explicit data flow between the robots, thus relying only on local sensor information. A box-carrying task is used to validate the proposed methodology both in simulation and in real-world experiments. Results show that implicit communication can be used together or replacing explicit communication for the cooperative box carrying task, making the system more robust to faulty communication environments.

Recent investigation on the cooperation of multiple robotic agents with centralized control, has ushered in small and less sophisticated robots that are observed by a global vision system. This paper presents a modeling methodology that enables accurate prediction of such small robot's position and orientation from data measured using the global vision system. Results of several experiments show that the predictions of the model can be used to compensate time delays and improve the transient response of the robots.

Until recently the control and navigation of mobile robots was mainly performed based on exteroceptive sensoring. Recent investigation on the cooperation of multiple robotic agents with centralized control, has ushered in small and less sophisticated robots that are observed by a global vision system. This paper presents a modeling methodology that enables accurate prediction of such small robot's position and orientation from data measured using the global vision system. Results of several experiments show that the model is both accurate and robust. Another relevant aspect of the work is the ability to determine the limitations likely to be found in centralized control of agents with little or no local "intelligence".