Abstract: An algorithm is presented for wheeled mobile robot trajectory generation that achieves a high degree of generality and efficiency. The generality derives from numerical linearization and inversion of forward models of propulsion, suspension, and motion for any type of vehicle. Efficiency is achieved by using fast numerical optimization techniques and effective initial guesses for the vehicle controls parameters. This approach can accommodate such effects as rough terrain, vehicle dynamics, models of wheel-terrain interaction, and other effects of interest. It can accommodate boundary and internal constraints while optimizing an objective function that might, for example, involve such criteria as obstacle avoidance, cost, risk, time, or energy consumption in any combination. The algorithm is efficient enough to use in real time due to its use of nonlinear programming techniques that involve searching the space of parameterized vehicle controls. Applications of the presented methods are demonstrated for planetary rovers.

Abstract: Navigation problems of unmanned air vehicles (UAVs) flying in a formation in a free and an obstacle-laden environment are investigated in this brief. When static obstacles popup during the flight, the UAVs are required to steer around them and also avoid collisions between each other. In order to achieve these goals, a new dual-mode control strategy is proposed: a "safe mode" is defined as an operation in an obstacle-free environment and a "danger mode" is activated when there is a chance of collision or when there are obstacles in the path. Safe mode achieves global optimization because the dynamics of all the UAVs participating in the formation are taken into account in the controller formulation. In the danger mode, a novel algorithm using a modified Grossberg neural network (GNN) is proposed for obstacle/collision avoidance. This decentralized algorithm in 2-D uses the geometry of the flight space to generate optimal/suboptimal trajectories. Extension of the proposed scheme for obstacle avoidance in a 3-D environment is shown. In order to handle practical vehicle constraints, a model predictive control-based tracking controller is used to track the references generated. Numerical results are provided to motivate this approach and to demonstrate its potential.

Abstract: This paper presents a motion control method for mobile robots in partially unknown environments populated with moving obstacles. The proposed method is based on the integration of focused D* search algorithm and dynamic window local obstacle avoidance algorithm with some adaptations that provide efficient avoidance of moving obstacles. The moving obstacles are modelled as moving cells in the occupancy grid map and their motion is predicted by applying a procedure similar to the dynamic window approach. The collision points of the robot predicted trajectory and moving cells predicted trajectories form the new active obstacles in the environment, which should be avoided. The algorithms are implemented and verified using a Pioneer 3DX mobile robot equipped with laser range finder.

Abstract: Classical sensor-based approaches tend to constrain all the degrees of freedom of a robot during the execution of a task. In this paper, a new solution is proposed. The key idea is to divide the global full-constraining task into several subtasks, which can be applied or inactivated to take into account potential constraints of the environment. Far from any constraint, the robot moves according to the full task. When it comes closer to a configuration to avoid, a higher level controller removes one or several subtasks, and activates them again when the constraint is avoided. The last controller ensures the convergence at the global level by introducing some look-ahead capabilities when a local minimum is reached. The robot accomplishes the global task by automatically sequencing sensor-based tasks, obstacle avoidance, and short deliberative phases. In this paper, a complete solution to implement this idea is proposed, along with several experiments that prove the validity of this approach

Abstract: The following topics are dealt with: intelligent control; spacecraft guidance; process control; PID control; dextrous manipulation; mobile robot; signal processing; UAV flight control; nonlinear control; embedded systems; obstacle avoidance; virtual reality; time-delay system and adaptive control.

Abstract: In this paper we try to develop an algorithm for visual obstacle avoidance of autonomous mobile robot. The input of the algorithm is an image sequence grabbed by an embedded camera on the B21r robot in motion. Then, the optical flow information is extracted from the image sequence in order to be used in the navigation algorithm. The optical flow provides very important information about the robot environment, like: the obstacles disposition, the robot heading, the time to collision and the depth. The strategy consists in balancing the amount of left and right side flow to avoid obstacles, this technique allows robot navigation without any collision with obstacles. The robustness of the algorithm will be showed by some

Abstract: This paper proposes an algorithm that drives a unicycle type robot to a desired path, including obstacle avoidance capabilities. The path-following control design relies on Lyapunov theory, backstepping techniques and deals explicitly with vehicle dynamics. Furthermore, it overcomes the initial condition constraints present in a number of path-following control strategies described in the literature. This is done by controlling explicitly the rate of progression of a “virtual target” to be tracked along the path; thus bypassing the problems that arise when the position of the path target point is simply defined as the closest point on the path. The obstacle avoidance part uses the Deformable Virtual Zone (DVZ) principle. This principle defines a safety zone around the vehicle in which the presence of an obstacle induces an “intrusion of information” that drives the vehicle reaction. The overall algorithm is combined with a guidance solution that embeds the path-following requirements in a desired intrusion information function, which steers the vehicle to the desired path while the DVZ ensures minimal contact with the obstacle, implicitly bypassing it. Simulation and experimental results illustrate the performance of the control system proposed.

Abstract: Immersive virtual environments are a promising research tool for the study of perception and action, on the assumption that visual--motor behavior in virtual and real environments is essentially similar. We investigated this issue for locomotor behavior and tested the generality of Fajen and Warren's [2003] steering dynamics model. Participants walked to a stationary goal while avoiding a stationary obstacle in matched physical and virtual environments. There were small, but reliable, differences in locomotor paths, with a larger maximum deviation (Δ = 0.16 m), larger obstacle clearance (Δ = 0.16 m), and slower walking speed (Δ = 0.13 m/s) in the virtual environment. Separate model fits closely captured the mean virtual and physical paths (R2 > 0.98). Simulations implied that the path differences are not because of walking speed or a 50p distance compression in virtual environments, but might be a result of greater uncertainty about the egocentric location of virtual obstacles. On the other hand, paths had similar shapes in the two environments with no difference in median curvature and could be modeled with a single set of parameter values (R2 > 0.95). Fajen and Warren's original parameters successfully generalized to new virtual and physical object configurations (R2 > 0.95). These results justify the use of virtual environments to study locomotor behavior.

Abstract: Visual servoing consists of steering a robot from an initial to a desired location by exploiting the information provided by visual sensors. This paper deals with the problem of realizing visual servoing for robot manipulators taking into account constraints such as visibility, workspace (that is obstacle avoidance), and joint constraints, while minimizing a cost function such as spanned image area, trajectory length, and curvature. To solve this problem, a new path-planning scheme is proposed. First, a robust object reconstruction is computed from visual measurements which allows one to obtain feasible image trajectories. Second, the rotation path is parameterized through an extension of the Euler parameters that yields an equivalent expression of the rotation matrix as a quadratic function of unconstrained variables, hence, largely simplifying standard parameterizations which involve transcendental functions. Then, polynomials of arbitrary degree are used to complete the parametrization and formulate the desired constraints and costs as a general optimization problem. The optimal trajectory is followed by tracking the image trajectory with an IBVS controller combined with repulsive potential fields in order to fulfill the constraints in real conditions.

Abstract: We develop a switched cooperative control scheme, to coordinate groups of ground and aerial vehicles for the purpose of locating a moving target in a given area. We do so by stabilizing the ground group into a guarding formation using a navigation function, and then steering the aerial group along a trajectory that uniformly scans the enclosed regions. The novelty of the approach lays in combining decentralized flocking algorithms with navigation functions for obstacle avoidance, convergence to designated position, and direction control.

Abstract: This paper describes a vision-based navigation and guidance design for UAVs for a combined mission of waypoint tracking and collision avoidance with unforeseen obstacles using a single 2-D passive vision sensor. An extended Kalman fllter (EKF) is applied to estimate a relative position of obstacles from vision-based measurements. The stochastic z-test value is used to solve a correspondence problem between the measurements and the estimates that have been already obtained by then. A collision cone approach is used as a collision criteria in order to examine if there is any obstacle that is critical to the vehicle. A guidance strategy for collision avoidance is designed based on a minimum-efiort guidance (MEG) method for multiple target tracking. The vision-based navigation and guidance designs suggested in this paper are integrated with realtime image processing algorithm and the entire vision-based control system are evaluated in the closed-loop 6 DoF ∞ight simulation.

Abstract: plane This paper explores both of these robustness issues using results from a micro air vehicle simulation model developed at the NASA Langley Research Center In particular, a hierarchy of dynamic models, ranging from a random walk model to a high-fidelity nonlinear micro air vehicle model, is employed in the Kalman filter for a simulated micro air vehicle trajectory with varying levels of measurement noise It is demonstrated that the visionbased measurement updates in the filter are capable of compensating for significant modeling errors and filter initialization errors As would be expected, superior overall results are achieved using higher-fidelity dynamic modelsintheKalman filterTheworkpresentedinthispaperrepresentsthe firststeptowardtheultimateobjectiveof incorporating vision-based state estimation into the design of autonomous flight control systems for micro air vehicles operating in urban environments

Abstract: The exponential growth of the interest and investigations in UAVs is strongly pushing the emergence of autonomous MFRs. This article presented some developments in the ASL-MFR project. A new design methodology was introduced and applied to a quadrotor and a coaxial helicopter enhancing appreciably the robots characteristics by allowing 100% thrust margin and 30 min autonomy (respectively, 40% and 20 min for CoaX). An original concept of hybrid active and passive control is introduced for CoaX. A simulation software permitting rapid MFR reconfiguration and various testing conditions was shown. Finally, a simulation of an obstacle avoidance controller was presented. The numerous developments presented in this article reinforce our conviction in the emergence of autonomous MFRs.

Abstract: Box jellyfish, cubomedusae, possess an impressive total of 24 eyes of four morphologically different types. Two of these eye types, called the upper and lower lens eyes, are camera-type eyes with spherical fish-like lenses. Compared with other cnidarians, cubomedusae also have an elaborate behavioral repertoire, which seems to be predominantly visually guided. Still, positive phototaxis is the only behavior described so far that is likely to be correlated with the eyes. We have explored the obstacle avoidance response of the Caribbean species Tripedalia cystophora and the Australian species Chiropsella bronzie in a flow chamber. Our results show that obstacle avoidance is visually guided. Avoidance behavior is triggered when the obstacle takes up a certain angle in the visual field. The results do not allow conclusions on whether color vision is involved but the strength of the response had a tendency to follow the intensity contrast between the obstacle and the surroundings (chamber walls). In the flow chamber Tripedalia cystophora displayed a stronger obstacle avoidance response than Chiropsella bronzie since they had less contact with the obstacles. This seems to follow differences in their habitats.

Abstract: This paper presents a rule-based intelligent guidance strategy for autonomous pursuit of mobile targets by unmanned aerial vehicles (UAVs) in an area with threats, obstacles, and restricted regions. The probabilistic threat exposure map (PTEM) is used as the mathematical formulation of the area of operation for the guidance strategy to make intelligent decisions based on a set of defined rules. The rules are developed for three objectives in the order of priority as: 1) avoid obstacles/restricted regions; 2) maintain the target proximity; 3) minimize UAV threat exposure level. A least-square estimation and kinematic relations are used to estimate/predict the target states based on noisy position measurements. The work presented herein addresses the same problem as in a previous work by the authors, and aims at improving the computational efficiency without compromising the performance. Simulation results of several pursuit scenarios demonstrate the full capabilities of the strategy and the improvement over the previous work

Abstract: The aim of this research was to describe age-related changes in locomotor adjustments during obstructed gait and expand and build from the current body of literature describing single obstacle avoidance strategies by including trials in which the subjects stepped over two identical obstacles placed in series. We observed young adults (YA: N = 8; aged 23.1 +/- 2.0 years) and older adults (OA: N = 8; aged 76.1 +/- 4.3 years) as they walked along a 5 m long instrumented pathway (GAITRite) and stepped over one or two obstacles that were scaled to their lower leg length. Infrared markers, tracked using the Optotrak motion analysis system (60 Hz; Northern Digital Inc, Canada), were fixed to subjects' trunk and feet, and several anatomical landmarks were digitized for each segment (e.g. toes). Data analyses included lead and trail toe clearance values, take-off and landing distance, step time, length, width and velocity, and three-dimensional trunk angles. Both age groups were able to successfully complete the obstacle avoidance task, and the presence of a second obstacle did not affect clearance strategies of either OA or YA. OA crossed the obstacles with a reduced step velocity and stepped closer to the trailing edge, although take-off distances were not different between the age groups. Additionally, OA used similar ranges of trunk motion as YA when crossing the obstacle, but did so while using smaller step lengths and step widths compared to YA, effectively, using a narrower base of support. Together, these findings suggest that older adults adopted a more cautious crossing strategy in that they reduced their crossing step velocity. However, other aspects of the avoidance strategy used by the older adults, specifically the shortened landing distances and the use of similar ranges of trunk motion within a narrowed BOS, could potentially put them at risk for tripping or imbalance when stepping over an obstacle.

Abstract: Autonomously developing organisms face several challenges when learning reaching movements. First, motor control is learned unsupervised or self-supervised. Second, knowledge of sensorimotor contingencies is acquired in contexts in which action consequences unfold in time. Third, motor redundancies must be resolved. To solve all 3 of these problems, the authors propose a sensorimotor, unsupervised, redundancy-resolving control architecture (SURE_REACH), based on the ideomotor principle. Given a 3-degrees-of-freedom arm in a 2-dimensional environment, SURE_REACH encodes 2 spatial arm representations with neural population codes: a hand end-point coordinate space and an angular arm posture space. A posture memory solves the inverse kinematics problem by associating hand end-point neurons with neurons in posture space. An inverse sensorimotor model associates posture neurons with each other action-dependently. Together, population encoding, redundant posture memory, and the inverse sensorimotor model enable SURE_REACH to learn and represent sensorimotor grounded distance measures and to use dynamic programming to reach goals efficiently. The architecture not only solves the redundancy problem but also increases goal reaching flexibility, accounting for additional task constraints or realizing obstacle avoidance. While the spatial population codes resemble neurophysiological structures, the simulations confirm the flexibility and plausibility of the model by mimicking previously published data in arm-reaching tasks.

Abstract: Unmanned aerial vehicles (UAVs) are playing increasingly prominent roles in defense programs and strategy around the world. Technology advancements have enabled the development of large UAVs (e.g., Global Hawk, Predator) and the creation of smaller, increasingly capable UAVs. The focus of this Chapter is on smaller fixed-wing miniature aerial vehicles (MAVs), which range in size from % to 2 m in wingspan. As recent conflicts have demonstrated, there are numerous military applications for MAVs including reconnaissance, surveillance, battle damage assessment, and communications relays.

Abstract: This paper demonstrates a reliable navigation of a mobile robot in outdoor environment. We fuse differential GPS and odometry data using the framework of extended Kalman filter to localize a mobile robot. And also, we propose an algorithm to detect curbs through the laser range finder. An important feature of road environment is the existence of curbs. The mobile robot builds the map of the curbs of roads and the map is used for tracking and localization. The navigation system for the mobile robot consists of a mobile robot and a control station. The mobile robot sends the image data from a camera to the control station. The control station receives and displays the image data and the teleoperator commands the mobile robot based on the image data. Since the image data does not contain enough data for reliable navigation, a hybrid strategy for reliable mobile robot in outdoor environment is suggested. When the mobile robot is faced with unexpected obstacles or the situation that, if it follows the command, it can happen to collide, it sends a warning message to the teleoperator and changes the mode from teleoperated to autonomous to avoid the obstacles by itself. After avoiding the obstacles or the collision situation, the mode of the mobile robot is returned to teleoperated mode. We have been able to confirm that the appropriate change of navigation mode can help the teleoperator perform reliable navigation in outdoor environment through experiments in the road.

Abstract: In this study we investigate two approachees for aggregation behavior in swarm robotics systems: Evolutionary methods and probabilistic control. In first part, aggregation behavior is chosen as a case, where performance and scalability of aggregation behaviors of perceptron controllers that are evolved for a simulated swarm robotic system are systematically studied with different parameter settings. Using a cluster of computers to run simulations in parallel, four experiments are conducted varying some of the parameters. Rules of thumb are derived, which can be of guidance to the use of evolutionary methods to generate other swarm robotic behaviors as well. In the second part a systematic analysis of probabilistic aggregation strategies in swarm robotic systems is presented. A generic aggregation behavior is proposed as a combination of four basic behaviors: obstacle avoidance, approach, repel, and wait. The latter three basic behaviors are combined using a three-state finite state machine with two probabilistic transitions among them. Two different metrics were used to compare performance of strategies. Through systematic experiments, how the aggregation performance, as measured by these two metrics, change 1) with transition probabilities, 2) with number of simulation steps, and 3) with arena size, is studied. We then discuss these two approaches for the aggregation problem.

Abstract: Miniature aerial vehicles (MAVs) have attracted major research interest during the last decade. Recent advances in low power processors, miniature sensors and control theory have contributed to system miniaturization and creation of new application fields.

Abstract: Fully autonomous control of ultra-light indoor airplanes has not yet been achieved because of the strong limitations on the kind of sensors that can be embedded making it difficult to obtain good estimations of altitude. We propose to revisit altitude control by considering it as an obstacle avoidance problem and introduce a novel control scheme where the ground and ceiling is avoided based on translatory optic flow, in a way similar to existing vision-based wall avoidance strategies. We show that this strategy is successful at controlling a simulated microflyer without any explicit altitude estimation and using only simple sensors and processing that have already been embedded in an existing 10-gram microflyer. This result is thus a significant step toward autonomous control of indoor flying robots.

Abstract: Previous research suggests that motor equivalence is achieved through reliance on effector-independent spatiotemporal forms. Here the authors report a series of experiments investigating the role of such forms in the production of movement sequences. Participants were asked to complete series of arm movements in time with a metronome and, on some trials, with an obstacle between 1 or more of the target pairs. In moves following an obstacle, participants only gradually reduced the peak heights of their manual jumping movements. This hand path priming effect, scaled with obstacle height, was preserved when participants cleared the obstacle with 1 hand and continued with the other, and it was modulated by future task demands. The results are consistent with the hypothesis that the control of movement sequences relies on abstract spatiotemporal forms. The data also support the view that motor programming is largely achieved by changing just those features that distinguish the next movement to be made from the movement that was just made.

Abstract: The U.S. Army Research Laboratory (ARL), as part of a mission and customer funded exploratory program, has developed a new low-frequency, ultra-wideband (UWB) synthetic aperture radar (SAR) for forward imaging to support the Army's vision of an autonomous navigation system for robotic ground vehicles. These unmanned vehicles, equipped with an array of imaging sensors, will be tasked to help detect man-made obstacles such as concealed targets, enemy minefields, and booby traps, as well as other natural obstacles such as ditches, and bodies of water. The ability of UWB radar technology to help detect concealed objects has been documented in the past and could provide an important obstacle avoidance capability for autonomous navigation systems, which would improve the speed and maneuverability of these vehicles and consequently increase the survivability of the U. S. forces on the battlefield. One of the primary features of the radar is the ability to collect and process data at combat pace in an affordable, compact, and lightweight package. To achieve this, the radar is based on the synchronous impulse reconstruction (SIRE) technique where several relatively slow and inexpensive analog-to-digital (A/D) converters are used to sample the wide bandwidth of the radar signals. We conducted an experiment this winter at Aberdeen Proving Ground (APG) to support the phenomenological studies of the backscatter from positive and negative obstacles for autonomous robotic vehicle navigation, as well as the detection of concealed targets of interest to the Army. In this paper, we briefly describe the UWB SIRE radar and the test setup in the experiment. We will also describe the signal processing and the forward imaging techniques used in the experiment. Finally, we will present imagery of man-made obstacles such as barriers, concertina wires, and mines.

Abstract: This paper discusses the importance, the complexity and the challenges of mapping mobile robot's unknown and dynamic environment, besides the role of sensors and the problems inherited in map build...

Abstract: Designing Nonlinear Model Predictive Control (NMPC) laws for controlling multiple autonomous surface vessels in arbitrary formations in environments containing obstacles are reported in this paper. Two leader-follower decentralized geometrical control schemes that are required for defining a unique two-dimensional formation are considered. A three-degree-of-freedom dynamic model of surface vessels has been used for the controller design. The realtime optimization abilities of the NMPC method has been used to improve the response of the unactuated DOF of the vessels and to directly incorporate the local obstacle avoidance into the formation control eliminating the need for an external local obstacle avoidance algorithm. The effectiveness of the developed control law, even in the presence of model uncertainty and external disturbances is demonstrated via computer simulations.

Abstract: An obstacle avoidance method for mobile apparatus includes the steps of acquiring, by a mobile apparatus, information as to relative movement of an obstacle with respect to the mobile apparatus, calculating a travel path and a travel direction of the obstacle based on the information during its relative movement, setting a non-intrusion area having a configuration which is longer in the travel direction of the obstacle than in a direction perpendicular to the travel direction, and performing such travel control on the mobile apparatus as to avoid the non-intrusion area, by which obstacle avoidance operation is fulfilled. Thus, an obstacle avoidance method as well as an obstacle-avoidable mobile apparatus are provided which are capable of not only reducing collisions against obstacles and repetitions of avoidance operation, but also preventing impartment of uneasiness and oppression to persons in the obstacle avoidance operation for mobile apparatuses such as robots.