Showing papers on "Obstacle avoidance published in 2009"

Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation

Abstract: In the Motion Planning research field, heuristic methods have demonstrated to outperform classical approaches gaining popularity in the last 35 years. Several ideas have been proposed to overcome the complex nature of this NP-Complete problem. Ant Colony Optimization algorithms are heuristic methods that have been successfully used to deal with this kind of problems. This paper presents a novel proposal to solve the problem of path planning for mobile robots based on Simple Ant Colony Optimization Meta-Heuristic (SACO-MH). The new method was named SACOdm, where d stands for distance and m for memory. In SACOdm, the decision making process is influenced by the existing distance between the source and target nodes; moreover the ants can remember the visited nodes. The new added features give a speed up around 10 in many cases. The selection of the optimal path relies in the criterion of a Fuzzy Inference System, which is adjusted using a Simple Tuning Algorithm. The path planner application has two operating modes, one is for virtual environments, and the second one works with a real mobile robot using wireless communication. Both operating modes are global planners for plain terrain and support static and dynamic obstacle avoidance.

Biologically-inspired dynamical systems for movement generation: Automatic real-time goal adaptation and obstacle avoidance

Abstract: Dynamical systems can generate movement trajectories that are robust against perturbations. This article presents an improved modification of the original dynamic movement primitive (DMP) framework by Ijspeert et al [1], [2]. The new equations can generalize movements to new targets without singularities and large accelerations. Furthermore, the new equations can represent a movement in 3D task space without depending on the choice of coordinate system (invariance under invertible affine transformations). Our modified DMP is motivated from biological data (spinal-cord stimulation in frogs) and human behavioral experiments. We further extend the formalism to obstacle avoidance by exploiting the robustness against perturbations: an additional term is added to the differential equations to make the robot steer around an obstacle. This additional term empirically describes human obstacle avoidance. We demonstrate the feasibility of our approach using the Sarcos Slave robot arm: after learning a single placing movement, the robot placed a cup between two arbitrarily given positions and avoided approaching obstacles.

Vision-based control of near-obstacle flight

Abstract: This paper presents a novel control strategy, which we call optiPilot, for autonomous flight in the vicinity of obstacles. Most existing autopilots rely on a complete 6-degree-of-freedom state estimation using a GPS and an Inertial Measurement Unit (IMU) and are unable to detect and avoid obstacles. This is a limitation for missions such as surveillance and environment monitoring that may require near-obstacle flight in urban areas or mountainous environments. OptiPilot instead uses optic flow to estimate proximity of obstacles and avoid them. Our approach takes advantage of the fact that, for most platforms in translational flight (as opposed to near-hover flight), the translatory motion is essentially aligned with the aircraft main axis. This property allows us to directly interpret optic flow measurements as proximity indications. We take inspiration from neural and behavioural strategies of flying insects to propose a simple mapping of optic flow measurements into control signals that requires only a lightweight and power-efficient sensor suite and minimal processing power. In this paper, we first describe results obtained in simulation before presenting the implementation of optiPilot on a real flying platform equipped only with lightweight and inexpensive optic computer mouse sensors, MEMS rate gyroscopes and a pressure-based airspeed sensor. We show that the proposed control strategy not only allows collision-free flight in the vicinity of obstacles, but is also able to stabilise both attitude and altitude over flat terrain. These results shed new light on flight control by suggesting that the complex sensors and processing required for 6 degree-of-freedom state estimation may not be necessary for autonomous flight and pave the way toward the integration of autonomy into current and upcoming gram-scale flying platforms.

Vision-based guidance and control of a hovering vehicle in unknown, GPS-denied environments

Abstract: This paper describes the system architecture and core algorithms for a quadrotor helicopter that uses vision data to navigate an unknown, indoor, GPS-denied environment. Without external sensing, an estimation system that relies only on integrating inertial data will have rapidly drifting position estimates. Micro aerial vehicles (MAVs) are stringently weight-constrained, leaving little margin for additional sensors beyond the mission payload. The approach taken in this paper is to introduce an architecture that exploits a common mission payload, namely a video camera, as a dual-use sensor to aid in navigation. Several core algorithms, including a fast environment mapper and a novel heuristic for obstacle avoidance, are also presented. Finally, drift-free hover and obstacle avoidance flight tests in a controlled environment are presented and analyzed.

Combined Automatic Lane-Keeping and Driver's Steering Through a 2-DOF Control Strategy

Abstract: In this paper, we address the problem of combining automatic lane-keeping and driver's steering for either obstacle avoidance or lane-change maneuvers for passing purposes or any other desired maneuvers, through a closed-loop control strategy. The automatic lane-keeping control loop is never opened, and no on/off switching strategy is used. During the driver's maneuver, the vehicle lateral dynamics are controlled by the driver himself through the vehicle steering system. When there is no driver's steering action, the vehicle center of gravity tracks the center of the traveling lane thanks to the automatic lane-keeping system. At the beginning (end) of the maneuver, the lane-keeping task is released (resumed) safely and smoothly. The performance of the proposed closed-loop structure is shown both by means of simulations and through experimental results obtained along Italian highways.

Neural Network Control of Mobile Robot Formations Using RISE Feedback

Abstract: In this paper, an asymptotically stable (AS) combined kinematic/torque control law is developed for leader-follower-based formation control using backstepping in order to accommodate the complete dynamics of the robots and the formation, and a neural network (NN) is introduced along with robust integral of the sign of the error feedback to approximate the dynamics of the follower as well as its leader using online weight tuning. It is shown using Lyapunov theory that the errors for the entire formation are AS and that the NN weights are bounded as opposed to uniformly ultimately bounded stability which is typical with most NN controllers. Additionally, the stability of the formation in the presence of obstacles is examined using Lyapunov methods, and by treating other robots in the formation as obstacles, collisions within the formation do not occur. The asymptotic stability of the follower robots as well as the entire formation during an obstacle avoidance maneuver is demonstrated using Lyapunov methods, and numerical results are provided to verify the theoretical conjectures.

A survey of collision avoidance approaches for unmanned aerial vehicles

Abstract: The ability to integrate unmanned and manned aircraft into airspace is a critical capability that will enable growth in wide varieties of applications. Collision avoidance is a key enabler for the integration of manned and unmanned missions in civil and military operation theaters. Large efforts have been done to address collision avoidance problem to both manned and unmanned aircraft. However, there has been little comparative discussion of the proposed approaches. This paper presents a survey of the collision avoidance approaches those deployed for aircraft, especially for unmanned aerial vehicles. The collision avoidance concept is introduced together with proposing generic functions carried by collision avoidance systems. The design factors of the sense and avoid system, which are used to categorize methods, are explained deeply. Based on the design factors, several typical approaches are categorized.

Obstacle avoidance of autonomous vehicles based on model predictive control

Abstract: This paper presents an obstacle avoidance scheme for autonomous vehicles as an active safety procedure in unknown environments. Safe trajectories are generated using the non-linear model predictive framework, in which the simplified dynamics of the vehicle are used to predict the state of the vehicle over the look-ahead horizon. To compensate for the slight dissimilarity between the simplified model and the actual vehicle, a separate controller is designed to track the generated trajectory. The longitudinal dynamics of the vehicle are controlled using the inverse dynamics of the vehicle powertrain model, and the lateral dynamics are controlled using a linear quadratic regulator. In the non-linear model predictive framework, to obtain safe trajectories, local obstacle information is incorporated into the performance index using a parallax-based method. Simulation results on a full non-linear vehicle model show that the proposed combination of model-predictive-control-based trajectory generation and...

Integration of active and passive compliance control for safe human-robot coexistence

Abstract: In this paper we discuss the integration of active and passive approaches to robotic safety in an overall scheme for real-time manipulator control. The active control approach is based on the use of a supervisory visual system, which detects the presence and position of humans in the vicinity of the robot arm, and generates motion references. The passive control approach uses variable joint impedance which combines with velocity control to guarantee safety in worst-case conditions, i.e. unforeseen impacts. The implementation of these techniques in a 3-dof, variable impedance arm is described, and the effectiveness of their functional integration is demonstrated through experiments.

Nonlinear Model Predictive Formation Flight

Abstract: This correspondence paper presents the validation of a formation flight control technique with obstacle avoidance capability based on nonlinear model predictive algorithms. Control architectures for multi-agent systems employed in this correspondence paper can be categorized as centralized, sequential decentralized, and fully decentralized methods. Centralized methods generally have better performance than decentralized methods. However, it is well known that the performance of the centralized methods for formation flight degrades when there exists communication failure among the vehicles, and they require more computation time than the decentralized method. This correspondence paper evaluates the control performance and the computation time reduction of the sequential decentralized and fully decentralized methods in comparison with the centralized method and shows that the fully decentralized method can be made effective against short term communication failure. The control inputs for formation flight are computed by nonlinear model predictive control (NMPC). The control input saturation and state constraints are incorporated as inequality constraints using Karush Kuhn Tucker conditions in the NMPC framework, and the collision avoidance can be considered in real time. The proposed schemes are validated by numerical simulations, which include the process and measurement noise for more realistic situations.

Multi-robot exploration and fire searching

Abstract: Exploration of an unknown environment is a fundamental concern in mobile robotics. This paper presents an approach for cooperative multi-robot exploration, fire searching and mapping in an unknown environment. The proposed approach aims to minimize the overall exploration time, making it possible to localize fire sources in an efficient way. In order to achieve this goal, the robots should cooperate in an effective way, so they can individually and simultaneously explore different areas of the environment while they identify fire sources. The proposed approach employs a decentralized frontier based exploration method which evaluates the cost-gain ratio to navigate to target way-points. The target way-points are obtained by an A* search variant algorithm. The potential field method is used to control the robots motion while avoiding obstacles. When a robot detects a fire, it estimates the flame's position by triangulation. The communication between the robots is done in a decentralized control way where they share the necessary data to generate the map of the environment and to perform cooperative actions in a behavioral decision making way. This paper presents simulation and experimental results of the proposed exploration and fire search method and concludes with a discussion of the obtained results and future improvements. 1

Consensus for formation control of nonholonomic mobile robots

Abstract: In this article we present novel formation control laws based on artificial potential fields and consensus algorithms for a group of unicycles enabling arbitrary formation patterns for these nonholonomic vehicles. Given connected and balanced graphs we are able to prove stability of the rendezvous controller by applying the LaSalle-Krasovskii invariance principle. Further, we introduce obstacle avoidance, enabling a reactive behavior of the robotic group in unknown environments. The effectiveness of the proposed controllers is shown using computer simulations and finally, a classification w.r.t. existing solutions is done.

Safe Navigation of a Mobile Robot Considering Visibility of Environment

Abstract: We present one approach to achieve safe navigation in an indoor dynamic environment. So far, there have been various useful collision avoidance algorithms and path planning schemes. However, those algorithms possess fundamental limitations in that the robot can avoid only ldquovisiblerdquo ones among surrounded obstacles. In a real environment, it is not possible to detect all the dynamic obstacles around the robot. There are many occluded regions due to the limited field of view. In order to avoid collisions, it is desirable to exploit visibility information. This paper proposes a safe navigation scheme to reduce collision risk considering occluded dynamic obstacles. The robot's motion is controlled by the hybrid control scheme. The possibility of collision is dually reflected to path planning and speed control. The proposed scheme clearly indicates the structural procedure on how to model and to exploit the risk of navigation. The proposed scheme is experimentally tested in a real office building. The experimental results show that the robot moves along the safe path to obtain sufficient field of view. In addition, safe speed constraints are applied in motion control. It is experimentally verified that a robot safely navigates in dynamic indoor environment by adopting the proposed scheme.

Robust Appearance Based Visual Route Following for Navigation in Large-scale Outdoor Environments

Abstract: In this paper we present a navigation algorithm that enables mobile robots to retrace routes previously taught under the control of human operators in outdoor environments. Possible applications include robot couriers, autonomous vehicles, tour guides and robotic patrols. The appearance-based approach presented in the paper is provably convergent, computationally inexpensive compared with map-based approaches and requires only odometry and a monocular omnidirectional vision sensor. A sequence of reference images is recorded during the human-guided route-teaching phase. Before starting the autonomous phase, the robot needs to be positioned at the beginning of the route. During the autonomous phase, the measurement image is compared with reference images using image cross-correlation performed in the Fourier domain to recover the difference in relative orientation. Route following is achieved by compensating for this orientation difference. Over 18 km of experiments performed under varying conditions demonstrate the algorithm's robustness to lighting variations and partial occlusion. Obstacle avoidance is not included in the current system.

Real-time obstacle avoidance for multiple mobile robots

Abstract: An efficient, simple, and practical real time path planning method for multiple mobile robots in dynamic environments is introduced. Harmonic potential functions are utilized along with the panel method known in fluid mechanics. First, a complement to the traditional panel method is introduced to generate a more effective harmonic potential field for obstacle avoidance in dynamically changing environments. Second, a group of mobile robots working in an environment containing stationary and moving obstacles is considered. Each robot is assigned to move from its current position to a goal position. The group is not forced to maintain a formation during the motion. Every robot considers the other robots of the group as moving obstacles and hence the physical dimensions of the robots are also taken into account. The path of each robot is planned based on the changing position of the other robots and the position of stationary and moving obstacles. Finally, the effectiveness of the scheme is shown by modeling an arbitrary number of mobile robots and the theory is validated by several computer simulations and hardware experiments.

A three-layered architecture for real time path planning and obstacle avoidance for surveillance USVs operating in harbour fields

Abstract: The use of unmanned vehicles in the field of underwater and marine applications is increasing significantly in recent years. Autonomous vehicles (like AUVs and gliders) or teleoperated ones (like ROVs) are currently employed for executing a number of different underwater tasks, like inspecting submerged pipes, executing maintenance interventions on underwater gas- or oil-platforms, collecting environmental or oceanographic data, performing surveys on sites of archeological interest. In parallel with the development of underwater vehicles, unmanned surface vehicles (USVs), are they also witnessing an increasing interest from the robotic community, especially with the goal of performing surveillance applications, like patrolling and maintaining safeguarded against intruders harbours or other “crucial” sites. The potential benefits offered by automated vessels equipped with sensors such as cameras or sonars are quite evident, since they could be used to quickly identify the level of menace of unknown radar track without exposing any human operators to possible threats. However USVs, unlike in the underwater case, have to face the problem of avoiding other vessels which in most cases are manned ones. This is a crucial point especially in that kind of application, where the automated vessel has to move quickly towards a possible menace while at the same time avoiding all the other boats normally operating in the harbour area. Unfortunately, at the current state of art, a reliable methodology to avoid the other vessels and the availability of effective and accurate obstacle detection sensors is still missing. This paper focus its attention on the case of USV used for security applications within a harbour, devising a solution that can be real-time implemented for the obstacle avoidance problem under critical situations where the vehicle as to reach its target as fast as possible while guaranteeing the safety of the other vessels. The presented solution is based on a three layered hierarchical architecture: the first layer computes a global path taking into account static obstacles known a priori, the second layer modifies this path in a locally optimal way (under certain assumptions) exploiting kinematic data of the moving obstacles, while the last layer reactively avoids obstacles for which such data is not available. The paper will be therefore organized as follows: in the first section an introduction and state of art are presented, in the successive section the work will discuss the first layer and the methods for the static obstacles avoidance, while in the third the paper will focus on the moving obstacles and the proposed avoidance algorithm, while also presenting many different detailed simulation results regarding the performances achievable by the overall architecture. Finally a concluding section will also indicate some still open problems and future work directions to be developed.

Vibrotactile feedback to aid blind users of mobile guides

Abstract: In this work, we report on a solution for providing support to the blind using mobile museum guides by exploiting the haptic channel as a complement to the audio/vocal one. The overall goal is to improve the autonomy and social integration of blind visitors. We followed an iterative approach in which the proposed system went through various user evaluations and further refinements. The final solution includes vibrotactile feedback enhancement for orientation and obstacle avoidance obtained through the use of unobtrusive actuators applied to two of the user's fingers combined with an electronic compass and obstacle detector sensors connected wirelessly to the mobile guide. Our study indicates that vibrotactile feedback is particularly useful to provide frequent unobtrusive indications of useful dynamic information, such as the level of proximity of an obstacle or the distance from the right orientation.

Spatial orientation of attention and obstacle avoidance following concussion

Abstract: Re-injury to the brain during recovery from an initial concussion leads to increased probability of permanent brain damage or death. Recovery from concussion has been proposed to be ongoing even up to a month post-injury. The goal of the current study was to investigate the relationship between the visuospatial orientation of attention and obstacle avoidance during gait in individuals that have recently suffered a concussion (mTBI) over a month post-injury. MTBI subjects and matched control subjects performed the attentional network test (ANT), designed to isolate several different components of attention. Obstacle crossing during gait with and without a concurrent attention dividing task was also performed. Reaction times from the ANT and obstacle clearance measurements were the main dependent variables. We observed that concussed individuals had statistically more obstacle contacts than controls. The ability to orient attention in space was also statistically deficient immediately after a concussion, and this was correlated with lower obstacle clearances of the leading foot. Similar correlations could also be found between both leading and trailing foot avoidance and spatial orientation of attention in participants with concussion when attention was divided during obstacle crossing, and these relationships gradually weakened as recovery progressed. By contrast, spatial attention and obstacle clearance were not significantly correlated in control subjects. These findings indicate that patients with mTBI who display greater spatial attention deficits cross over the obstacle with a lower clearance than patients with less or without spatial attention deficits, leading to an increased probability of obstacle contact.

Learning Anticipation via Spiking Networks: Application to Navigation Control

Abstract: In this paper, we introduce a network of spiking neurons devoted to navigation control. Three different examples, dealing with stimuli of increasing complexity, are investigated. In the first one, obstacle avoidance in a simulated robot is achieved through a network of spiking neurons. In the second example, a second layer is designed aiming to provide the robot with a target approaching system, making it able to move towards visual targets. Finally, a network of spiking neurons for navigation based on visual cues is introduced. In all cases, the robot was assumed to rely on some a priori known responses to low-level sensors (i.e., to contact sensors in the case of obstacles, to proximity target sensors in the case of visual targets, or to the visual target for navigation with visual cues). Based on their knowledge, the robot has to learn the response to high-level stimuli (i.e., range finder sensors or visual input). The biologically plausible paradigm of spike-timing-dependent plasticity (STDP) is included in the network to make the system able to learn high-level responses that guide navigation through a simple unstructured environment. The learning procedure is based on classical conditioning.

Multi-UAV path planning in obstacle rich environments using Rapidly-exploring Random Trees

Abstract: This paper presents path planning algorithms using Rapidly-exploring Random Trees (RRTs) to generate paths for multiple unmanned air vehicles (UAVs) in real time, from given starting locations to goal locations in the presence of static, pop-up and dynamic obstacles. Generating non-conflicting paths in obstacle rich environments for a group of UAVs within a given short time window is a challenging task. The difficulty further increases because the turn radius constraints of the UAVs have to be comparable with the corridors where they intend to fly. Hence we first generate a path quickly using RRT by taking the kinematic constraints of the UAVs into account. Then in order to generate a low cost path we develop an anytime algorithm that yields paths whose quality improves as flight proceeds. When the UAV detects a dynamic obstacle, the path planner avoids it based on a set of criteria. In order to track generated paths, a guidance law based on pursuit and line-of-sight is developed. Simulation studies are carried out to show the performance of the proposed algorithm.

Pseudospectral Motion Planning for Autonomous Vehicles

Abstract: A COMMON task for autonomous vehicles is motion planning. Discipline-based design of motion planning algorithms have led to the development and evolution of different techniques to solve specific problems. For instance, the artificial–potential–function technique [1] is a popular method for the motion planning of unmanned ground vehicles (UGV) and robotic manipulators [1–3]. Although this technique has been used for over 30 years, it suffers from the possibility of the vehicle not achieving its goal and difficulties in accommodating various environmental constraints [4]. To overcome such issues, recent developments in motion planning algorithms place heavy emphasis on so-called sampling-based planning techniques such as probabilistic road maps, rapidly exploring random trees, and expansive space trees to name a few [5– 9]. These methods use probabilistic means of connecting the initial configuration to thefinal configuration thereby enabling an improved capacity to achieve the goal and a capability to generate initial feasible paths. In all these techniques, the initial feasible paths do not automatically incorporate vehicle dynamics; hence, path-following control techniques are needed to satisfy the physics of themotion [9]. This is one reason why nonholonomic constraints play such a crucial role in constrained control techniques that are designed to serve pathfollowing systems. There is no doubt that optimal control theory is the most natural framework for solving motion planning problems; however, solving optimal control problems has historically been considered difficult due to the twin curses of dimensionality and complexity. These difficulties are exacerbated in the presence of state constraints; hence, an obstacle-cluttered environment becomes a substantially more difficult problem under the framework of optimal control theory [10,11]. Nonetheless, it is possible to solve simplified motion planning problems wherein the cost function is quadratic or the environment is obstacle free. Because the motion planning techniques developed in robotics applications are unsuitable for flight vehicles, such as launch and reentry, for example, aerospace problems have motivated the development of efficient optimal control algorithms. In aerospace applications, satisfaction of the dynamical constraints is exceedingly important; hence, trajectory and control become fundamentally intertwined. On the other hand, aerospace problems do not have the vast number of path constraints that are common in an obstacle-cluttered environment. In recent years, paradigm-changing advancements have taken place in computational optimal control that challenge conventional wisdom. For instance, onboard the International Space Station, Bedrossian et al. [12] discovered and implemented a revolutionary momentum-dumping approach that they call a zero-propellant maneuver. This discovery was made possible by an application of pseudospectral methods to solve a real-life challenging optimal control problem [13]. Other applications of such advancements are discussed in [14,15] and include the ground test of a revolutionary attitude control concept for the NPSAT1 spacecraft. Motivated by these advancements, we apply pseudospectral methods to develop motion planning algorithms for autonomous vehicles characterized by nonlinear dynamical constraints, an obstacle-cluttered environment, and a need to generate solutions in real time. We consider the problem of generating optimal trajectories for generic autonomous vehicles. Shapes of arbitrary number, size, and configuration are modeled in the form of path constraints in the resulting optimal control problem. The method is tested under various obstacle environments on different platforms such as sea surface vehicles, ground vehicles, and aerial vehicles. The optimality of the computed trajectories is verified by way of the necessary conditions. We show that it is possible to do motion planning for different problems under the unified framework of optimal control and pseudospectral methods [16–19].

Autonomous Forest Vehicles: Historic, envisioned, and state-of-the-art

Abstract: The feasibility of using autonomous forest vehicles (which can be regarded as logical developments in the ongoing automation of forest machines), the systems that could be applied in them, their potential advantages and limitations (in the foreseeable future) are considered in this paper. The goals were to analyze: 1) the factors influencing the degree of automation in logging; 2) the technical principles that can be applied to autonomous forest machines, and 3) the feasibility of developing an autonomous path-tracking forest vehicle. A type of vehicle that is believed to have considerable commercial potential is an autonomous forwarder. The degree of automation is influenced by increased productivity, the machine operator as a bottle-neck, cost reduction, and environmental aspects. Technical principles that can be applied to autonomous forest vehicles are satellite navigation, wheel odometry, laser scanner, and radar. A new path-tracking algorithm has been developed to reduce deviations from the desired path by utilizing the driver’s steering commands. The presented system has demonstrated both possibilities and difficulties associated with autonomous forest machines. A field study has shown that it is quite possible for them to learn and track a path previously demonstrated by an operator with an accuracy of 0.1 m on flat ground and also to detect and avoid unexpected obstacles. Although the forest machine safely avoids obstacles, the study shows that further research in the field of obstacle avoidance is needed to optimize performance and ensure safe operation in a real forest environment.

Entrapment/escorting and patrolling missions in multi-robot cluster space control

Abstract: The tasks of entrapping/escorting and patrolling around an autonomous target are presented making use of the multi-robot cluster space control approach. The cluster space control technique promotes simplified specification and monitoring of the motion of mobile multi-robot systems of limited size. Previous work has established the conceptual foundation of this approach and has experimentally verified and validated its use for 2-robot, 3-robot and 4-robot systems, with varying implementations ranging from automated trajectory control to human-in-the-loop piloting. In this publication, we show that the problem of entrapping/escorting/patrolling is trivial to define and manage from a cluster space perspective. Using a 3-robot experimental testbed, results are shown for the given tasks. We also revise the definition of the cluster space framework for a three-robot formation and incorporate a robot-level obstacle avoidance functionality.