Showing papers on "Obstacle avoidance published in 2002"
TL;DR: In this paper, a randomized path planning architecture for dynamical systems in the presence of fixed and moving obstacles is proposed, which can be applied to vehicles whose dynamics are described either by ordinary differential equations or by higher-level, hybrid representations.
Abstract: Planning the path of an autonomous, agile vehicle in a dynamic environment is a very complex problem, especially when the vehicle is required to use its full maneuvering capabilities. Recent efforts aimed at using randomized algorithms for planning the path of kinematic and dynamic vehicles have demonstrated considerable potential for implementation on future autonomous platforms. This paper builds upon these efforts by proposing a randomized path planning architecture for dynamical systems in the presence of fixed and moving obstacles. This architecture addresses the dynamic constraints on the vehicle's motion, and it provides at the same time a consistent decoupling between low-level control and motion planning. The path planning algorithm retains the convergence properties of its kinematic counterparts. System safety is also addressed in the face of finite computation times by analyzing the behavior of the algorithm when the available onboard computation resources are limited, and the planning must be performed in real time. The proposed algorithm can be applied to vehicles whose dynamics are described either by ordinary differential equations or by higher-level, hybrid representations. Simulation examples involving a ground robot and a small autonomous helicopter are presented and discussed.
742 citations
TL;DR: Describes the technologies of cooperative driving with automated vehicles and intervehicle communications in the Demo 2000 cooperative driving, which was held in November 2000, on a test track with five automated vehicles.
Abstract: Describes the technologies of cooperative driving with automated vehicles and intervehicle communications in the Demo 2000 cooperative driving. Cooperative driving, aiming at the compatibility of safety and efficiency of road traffic, means that automated vehicles drive by forming a flexible platoon over a couple of lanes with a short intervehicle distance while performing lane changing, merging, and leaving the platoon. The vehicles for the demonstration are equipped with automated lateral and longitudinal control functions with localization data by the differential global positioning system (DGPS) and the intervehicle communication function with 5.8-GHz dedicated short range communication (DSRC) designed for the dedicated use in the demonstration. In order to show the feasibility and potential of the technologies, the demonstration was held in November 2000, on a test track with five automated vehicles. The scenario included stop and go, platooning, merging, and obstacle avoidance.
405 citations
TL;DR: The elastic strip framework presented in this paper enables the execution of a previously planned motion in a dynamic environment for robots with many degrees of freedom, and encompasses methods to suspend task behavior when its execution becomes inconsistent with other constraints imposed on the motion.
Abstract: Robotic applications are expanding into dynamic, unstructured, and populated environments. Mechanisms specifically designed to address the challenges arising in these environments, such as humanoid robots, exhibit high kinematic complexity. This creates the need for new algorithmic approaches to motion generation, capable of performing task execution and real-time obstacle avoidance in high-dimensional configuration spaces. The elastic strip framework presented in this paper enables the execution of a previously planned motion in a dynamic environment for robots with many degrees of freedom. To modify a motion in reaction to changes in the environment, real-time obstacle avoidance is combined with desired posture behavior. The modification of a motion can be performed in a task-consistent manner, leaving task execution unaffected by obstacle avoidance and posture behavior. The elastic strip framework also encompasses methods to suspend task behavior when its execution becomes inconsistent with other const...
330 citations
TL;DR: An approach to path planning for humanoid robots that computes dynamically-stable, collision-free trajectories from full-body posture goals that generally applies to any robot subject to balance constraints (legged or not).
Abstract: We present an approach to path planning for humanoid robots that computes dynamically-stable, collision-free trajectories from full-body posture goals. Given a geometric model of the environment and a statically-stable desired posture, we search the configuration space of the robot for a collision-free path that simultaneously satisfies dynamic balance constraints. We adapt existing randomized path planning techniques by imposing balance constraints on incremental search motions in order to maintain the overall dynamic stability of the final path. A dynamics filtering function that constrains the ZMP (zero moment point) trajectory is used as a post-processing step to transform statically-stable, collision-free paths into dynamically-stable, collision-free trajectories for the entire body. Although we have focused our experiments on biped robots with a humanoid shape, the method generally applies to any robot subject to balance constraints (legged or not). The algorithm is presented along with computed examples using both simulated and real humanoid robots.
302 citations
TL;DR: A new IR sensor based on the light intensity back-scattered from objects and able to measure distances of up to 1 m is described and the expected errors in distance estimates are analysed and modelled.
260 citations
10 Dec 2002
TL;DR: The existence of an optimal path class satisfying the UAV kinematic constraints and vector calculus are exploited to reduce this class of optimal path-planning problems for unmanned air vehicles to a parameter optimization problem.
Abstract: We consider a class of 2D optimal path-planning problems for unmanned air vehicles (UAVs) with kinematic and tactical constraints. The existence of an optimal path class satisfying the UAV kinematic constraints and vector calculus are exploited to reduce this class of optimal path-planning problems to a parameter optimization problem. Illustrative tactical constraints arising in target touring and obstacle avoidance problems are considered. A necessary condition for optimal path planning in the presence of tactical constraints is characterized.. An efficient numerical algorithm using simulated dynamics is developed to enforce the optimality criterion. The proposed optimal path planner can handle multiple tactical constraints. An illustrative numerical simulation demonstrates the efficacy of our approach.
178 citations
TL;DR: In this article, a reactive shared controller is proposed to assist wheelchair users in semi-autonomous navigation of a wheelchair in unknown and dynamic environments, where the user and the vehicle share the control of the wheelchair.
Abstract: This paper describes new results with a Reactive Shared-Control system that enables a semi-autonomous navigation of a wheelchair in unknown and dynamic environments. The purpose of the reactive shared controller is to assist wheelchair users providing an easier and safer navigation. It is designed as a fuzzy-logic controller and follows a behaviour-based architecture. The implemented behaviours are three: intelligent obstacle avoidance, collision detection and contour following. Intelligent obstacle avoidance blends user commands, from voice or joystick, with an obstacle avoidance behaviour. Therefore, the user and the vehicle share the control of the wheelchair. The reactive shared control was tested on the RobChair powered wheelchair prototype [6] equipped with a set of ranging sensors. Experimental results are presented demonstrating the effectiveness of the controller.
163 citations
10 Dec 2002
TL;DR: A new approach is presented that integrates path planning with sensor-based collision avoidance that simultaneously considers the robot's pose and velocities during the planning process and can reliably control mobile robots moving at high speeds.
Abstract: Whenever robots are installed in populated environments, they need appropriate techniques to avoid collisions with unexpected obstacles. Over the past years several reactive techniques have been developed that use heuristic evaluation functions to choose appropriate actions whenever a robot encounters an unforeseen obstacle. Whereas the majority of these approaches determines only the next steering command, some additionally consider sequences of possible poses. However, they generally do not consider sequences of actions in the velocity space. Accordingly, these methods are not able to slow down the robot early enough before it has to enter a narrow passage. In this paper we present a new approach that integrates path planning with sensor-based collision avoidance. Our algorithm simultaneously considers the robot's pose and velocities during the planning process. We employ different strategies to deal with the huge state space that has to be explored. Our method has been implemented and tested on real robots and in simulation runs. Extensive experiments demonstrate that our technique can reliably control mobile robots moving at high speeds.
150 citations
TL;DR: The work presented in this paper deals with the problem of the navigation of a mobile robot either in unknown indoor environment or in a partially known one, and a hybrid method is used in order to exploit the advantages of global and local navigation strategies.
142 citations
07 Nov 2002
TL;DR: A foot-mounted ZMP sensor design based on force sensing resistors (FSR) is presented and experimental ZMP data obtained from the biped walking robots Mari-1 and Mari-2 and human subjects are compared in the context of reference gait generation for biped Walking robots.
Abstract: The bipedal robot structure is highly suitable for working in human environments due to its advantages in obstacle avoidance and its ability to be employed as a human substitute. However, the complex dynamics involved make biped robot control a challenging task. The zero moment point (ZMP) trajectory in the robot foot support area is a significant criterion for the stability of the walk. In many studies, ZMP coordinates are computed using a model of the robot and information from the joint encoders. A more direct approach is to use measurement data from sensors mounted in the robot feet. In this paper, a foot-mounted ZMP sensor design based on force sensing resistors (FSR) is presented and experimental ZMP data obtained from the biped walking robots Mari-1 and Mari-2 and human subjects are compared in the context of reference gait generation for biped walking robots.
131 citations
10 Dec 2002
TL;DR: In this paper, an on-board controller performs obstacle avoidance while the operator uses the manipulandum of a haptic probe to designate the desired speed and rate of turn for teleoperating a mobile robot using shared autonomy.
Abstract: We address the problem of teleoperating a mobile robot using shared autonomy: an on-board controller performs obstacle avoidance while the operator uses the manipulandum of a haptic probe to designate the desired speed and rate of turn. Sensors on the robot are used to measure obstacle range information. We describe a strategy to convert such range information into forces, which are reflected to the operator's hand, via the haptic probe. This haptic information provides feedback to the operator in addition to imagery from a front-facing camera mounted on the mobile robot. Extensive experiments with a user population show that the added haptic feedback significantly improves operator performance in several ways (reduced collisions, increased minimum distance between the robot and obstacles) without a significant increase in navigation time.
01 Jan 2002
TL;DR: A novel limit-cycle navigation method is proposed for a fast mobile robot using the limit- cycle characteristics of a 2nd-order nonlinear function that enables a robot to maneuver smoothly towards any desired destination.
Abstract: A mobile robot should be designed to navigate with collision avoidance capability in the real world, flexibly coping with the changing environment. In this paper, a novel limit-cycle navigation method is proposed for a fast mobile robot using the limit-cycle characteristics of a 2nd-order nonlinear function. It can be applied to the robot operating in a dynamically changing environment, such as in a robot soccer system. By adjusting the radius of the motion circle and the direction of obstacle avoidance, the navigation method proposed enables a robot to maneuver smoothly towards any desired destination. Simulations and real experiments using a robot soccer system demonstrate the merits and practical applicability of the proposed method. 11 12 13 14 15 16 17 © 2002 Published by Elsevier Science B.V. 18
07 Aug 2002
TL;DR: General transition criteria and methods are presented, permitting the suspension and resumption of task execution to ensure other desired motion behavior, such as obstacle avoidance.
Abstract: Applications in mobile manipulation require sophisticated motion execution skills to address issues like redundancy resolution, reactive obstacle avoidance, and transitioning between different motion behaviors. The elastic strip framework is an approach to reactive motion generation providing an integrated solution to these problems. Novel techniques within the elastic strip framework are presented, allowing task-consistent obstacle avoidance and task-consistent motion behavior. General transition criteria and methods are presented, permitting the suspension and resumption of task execution to ensure other desired motion behavior, such as obstacle avoidance. Task execution has to be suspended when kinematic constraints or changes in the environment render task-consistent motion behavior infeasible. Task execution is resumed as soon as it is consistent with other desired motion behaviors.
07 Aug 2002
TL;DR: An algorithm that learns collections of typical trajectories that characterize a person's motion patterns, related to specific locations that they might be interested in approaching and specific trajectories they might follow in doing so is proposed.
Abstract: We propose a method for learning models of people's motion behaviors in an indoor environment. As people move through their environments, they do not move randomly. Instead, they often engage in typical motion patterns, related to specific locations that they might be interested in approaching and specific trajectories that they might follow in doing so. Knowledge about such patterns may enable a mobile robot to develop improved people following and obstacle avoidance skills. This paper proposes an algorithm that learns collections of typical trajectories that characterize a person's motion patterns. Data, recorded by mobile robots equipped with laser range finders, is clustered into different types of motion using the popular expectation maximization algorithm, while simultaneously learning multiple motion patterns. Experimental results, obtained using data collected in a domestic residence and in an office building, illustrate that highly predictive models of human motion patterns can be learned.
10 Dec 2002
TL;DR: This paper considers the problem of a robot navigating in a crowded or congested environment and proposes a hierarchical representation of POMDPs to attempt to predict the motion trajectory of humans and obstacles.
Abstract: This paper considers the problem of a robot navigating in a crowded or congested environment. A robot operating in such an environment can get easily blocked by moving humans and other objects. To deal with this problem it is proposed to attempt to predict the motion trajectory of humans and obstacles. Two kinds of prediction are considered: short-term and long-term. The short-term prediction refers to the one-step ahead prediction and the long-term to the prediction of the final destination point of the obstacle's movement. The robot movement is controlled by a partially observable Markov decision process (POMDP). POMDPs are utilized because of their ability to model information about the robot's location and sensory information in a probabilistic manner. The solution of a POMDP is computationally expensive and thus a hierarchical representation of POMDPs is used.
TL;DR: This work found the optimal trajectory for obstacle avoidance by minimizing the mean-squared error at the end of the movement while keeping the probability of collision with the obstacle below a fixed limit.
Abstract: Task optimization in the presence of signal-dependent noise (TOPS) has been proposed as a general framework for planning goal-directed movements. Within this framework, the motor command is assumed to be corrupted by signal-dependent noise, which leads to a distribution of possible movements. A task can then be equated with optimizing some function of the statistics of this distribution. We found the optimal trajectory for obstacle avoidance by minimizing the mean-squared error at the end of the movement while keeping the probability of collision with the obstacle below a fixed limit. The optimal paths accurately predicted the empirical trajectories. This demonstrates that controlling the statistics of movements in the presence of signal-dependent noise may be a fundamental and unifying principle of goal-directed movements.
07 Aug 2002
TL;DR: In this paper, an approach to obstacle avoidance and local path planning for polygonal robots is presented, which decomposes the task into a model stage and a planning stage using a reduced dynamic window.
Abstract: In this paper we present an approach to obstacle avoidance and local path planning for polygonal robots. It decomposes the task into a model stage and a planning stage. The model stage accounts for robot shape and dynamics using a reduced dynamic window. The planning stage produces collision-free local paths with a velocity profile. We present an analytical solution to the distance to collision problem for polygonal robots, avoiding thus the use of look-up tables. The approach has been tested in simulation and on two non-holonomic rectangular robots where a cycle time of 10 Hz was reached under full CPU load. During a long-term experiment over 5 km travel distance, the method demonstrated its practicability.
07 Aug 2002
TL;DR: The dynamical systems theory is used here as a theoretical language and tool to design a distributed control architecture that generates navigation in formation, integrated with obstacle avoidance, for a team of three autonomous robots.
Abstract: The dynamical systems theory is used here as a theoretical language and tool to design a distributed control architecture that generates navigation in formation, integrated with obstacle avoidance, for a team of three autonomous robots. In this approach the level of modeling is at the level of behaviors. A "dynamics" of behavior is defined over a state-space of behavioral variables. The environment is also modeled in these terms by representing task constraints as attractors (i.e., asymptotically stable states) or repellers (i.e., unstable states) of behavioral dynamics. For each robot attractors and repellers are combined into a vector field that governs the behavior. The resulting dynamical systems that generate the behavior of the robots are nonlinear. Computer simulations support the validity of our dynamic model architectures.
10 Dec 2002
TL;DR: The paper deals with kinematic control algorithms for on-line obstacle avoidance which allow a kinematically redundant manipulator to move in an unstructured environment without colliding with obstacles and proposes an approximate solution which is computationally more efficient and allows for many simultaneously active obstacles without any problems.
Abstract: The paper deals with kinematic control algorithms for on-line obstacle avoidance which allow a kinematically redundant manipulator to move in an unstructured environment without colliding with obstacles. The presented approach is based on the redundancy resolution at the velocity level. The primary task is determined by the end-effector trajectories and for obstacle avoidance the internal motion of the manipulator is used. The obstacle avoiding motion is defined in one-dimensional operational space and, hence, the system has less singularities making implementation easier. Instead of the exact pseudoinverse solution we propose an approximate one which is computationally more efficient and allows us to consider many simultaneously active obstacles without any problems. The fast cycle times of the numerical implementation enable use of the algorithm in real-time control. For illustration, some simulation results of a highly redundant planar manipulator moving in an unstructured and time-varying environment and experimental results of a four link planar manipulator are given.
10 Dec 2002
TL;DR: An algorithm is proposed that learns collections of typical trajectories that characterize a person's motion patterns that enable a mobile robot to develop improved people following and obstacle avoidance skills.
Abstract: We propose a method for learning models of people's motion behaviors in indoor environments. As people move through their environments, they do not move randomly. Instead, they often engage in typical motion patterns, related to specific locations that they might be interested in approaching and specific trajectories that they might follow in doing so. Knowledge about such patterns may enable a mobile robot to develop improved people following and obstacle avoidance skills. This paper proposes an algorithm that learns collections of typical trajectories that characterize a person's motion patterns. Data, recorded by mobile robots equipped with laser-range finders, is clustered into different types of motion using the popular expectation maximization algorithm, while simultaneously learning multiple motion patterns. Experimental results, obtained using data collected in a domestic residence and in an office building, illustrate that highly predictive models of human motion patterns can be learned.
15 Jul 2002
TL;DR: This paper describes a method for goal-directed, collision-free navigation in unpredictable environments that employs a behavior-based hybrid architecture with asynchronously operating behavioral modules that differs from existing hybrid architectures in two important ways.
Abstract: Research in the planning and control of mobile robots has received much attention in the past two decades. Two basic approaches have emerged from these research efforts: deliberative vs.\ reactive. These two approaches can be distinguished by their different usage of sensed data and global knowledge, speed of response, reasoning capability, and complexity of computation. Their strengths are complementary and their weaknesses can be mitigated by combining the two approaches in a hybrid architecture. This paper describes a method for goal-directed, collision-free navigation in unpredictable environments that employs a behavior-based hybrid architecture with asynchronously operating behavioral modules. It differs from existing hybrid architectures in two important ways: (1) the planning module produces a sequence of checkpoints instead of a conventional complete path, and (2) in addition to obstacle avoidance, the reactive module also performs target reaching under the control of a self-organizing neural network. The neural network is trained to perform fine, smooth motor control that moves the robot through the checkpoints. These two aspects facilitate a tight integration between high-level planning and low-level control, which permits real-time performance and easy path modification even when the robot is en route to the goal position.
TL;DR: A planning methodology for nonholonomic mobile platforms with manipulators in the presence of obstacles is developed that employs smooth and continuous functions such as polynomials that yields admissible input trajectories that drive both the manipulator and the platform to a desired configuration.
Abstract: A planning methodology for nonholonomic mobile platforms with manipulators in the presence of obstacles is developed that employs smooth and continuous functions such as polynomials. The method yields admissible input trajectories that drive both the manipulator and the platform to a desired configuration and is based on mapping the nonholonomic constraint to a space where it can be satisfied trivially. In addition, the method allows for direct control over the platform orientation. Cartesian space obstacles are also mapped into this space in which they can be avoided by increasing the order of the polynomials employed in planning trajectories. The additional parameters required are computed systematically, while the computational burden increases linearly with the number of obstacles and the system elements taken into account. Illustrative examples demonstrate the planning methodology in obstacle-free and obstructed environments.
27 Oct 2002
TL;DR: A cooperative sweeping strategy of complete coverage path planning for multiple cleaning robots in a time-varying and unstructured environment is proposed using biologically inspired neural networks to achieve a common sweeping goal effectively.
Abstract: In this paper, a cooperative sweeping strategy of complete coverage path planning for multiple cleaning robots in a time-varying and unstructured environment is proposed using biologically inspired neural networks. Cleaning tasks require a special kind of trajectory being able to cover every unoccupied area in specified cleaning environments, which is an essential issue for cleaning robots and many other robotic applications. Multiple robots can improve the work capacity, share the cleaning tasks, and reduce the time to complete sweeping tasks. In the proposed model, the dynamics of each neuron in the topologically organized neural network is characterized by a shunting neural equation. Each cleaning robot treats the other robots as moving obstacles. Multiple cleaning robots can cooperate to achieve a common sweeping goal effectively. The robot path is autonomously generated from the dynamic activity landscape of the neural network, the previous robot location and the other robot locations. The proposed model algorithm is computationally efficient. The feasibility is validated by simulation studies on three cases of two cooperating cleaning robots.
TL;DR: An iterative solution of the Inverse Geometric Model is used, which requires no matrix inversion and iterates directly on the joint position, being thus suitable for on-line application and also preserving repeatability.
10 Dec 2002
TL;DR: This paper discusses techniques to predict the dynamic vehicle response to various natural obstacles and opens up a new area of obstacle negotiation for UGVs, where the vehicle moves over certain obstacles, rather than avoiding them, thereby resulting in more effective achievement of objectives.
Abstract: We discuss techniques to predict the dynamic vehicle response to various natural obstacles. This method can then be used to adjust the vehicle dynamics to optimize performance (e.g. speed) while ensuring that the vehicle is not damaged. This capability opens up a new area of obstacle negotiation for UGVs, where the vehicle moves over certain obstacles, rather than avoiding them, thereby resulting in more effective achievement of objectives. Robust obstacle negotiation and vehicle dynamics prediction requires several key technologies that are discussed in this paper. We detect and segment (label) obstacles using a novel 3D obstacle algorithm. The material of each labelled obstacle (rock, vegetation, etc) is then determined using a texture or color classification scheme. Terrain load-bearing surface models are then constructed using vertical springs to model the compressibility and traversability of each obstacle in front of the vehicle. The terrain model is then combined with the vehicle suspension model to yield an estimate of the maximum safe velocity, and predict the vehicle dynamics as the vehicle follows a path. This end-to-end obstacle negotiation system is envisioned to be useful in optimized path planning and vehicle navigation in terrain conditions cluttered with vegetation, bushes, rocks, etc. Results on natural terrain with various natural materials are presented.
TL;DR: Single obstacle trials demonstrated that take-off distance and toe-elevation are gait parameters, which are controlled in successful obstacle clearance, and double obstacle trials revealed that presence and position of a second obstacle in the travel path influences trail limb take-offs for both first and second obstacles.
07 Nov 2002
TL;DR: A discrete coding of the input space using a neural network structure is presented as opposed to the commonly used continuous internal representation of the environment, which enables a faster and more efficient convergence of the reinforcement learning process.
Abstract: One of the basic issues in the navigation of autonomous mobile robots is the obstacle avoidance task that is commonly achieved using a reactive control paradigm where a local mapping from perceived states to actions is acquired. A control strategy with learning capabilities in an unknown environment can be obtained using reinforcement learning where the learning agent is given only sparse reward information. This credit assignment problem includes both temporal and structural aspects. While the temporal credit assignment problem is solved using core elements of the reinforcement learning agent, solution of the structural credit assignment problem requires an appropriate internal state space representation of the environment. In this paper, a discrete coding of the input space using a neural network structure is presented as opposed to the commonly used continuous internal representation. This enables a faster and more efficient convergence of the reinforcement learning process.
07 Aug 2002
TL;DR: A stereo-based obstacle avoidance system for mobile vehicles that can detect both positive and "negative" obstacles in its path and results on indoor environments with planar supporting surfaces that show the algorithms to be both fast and robust.
Abstract: We present a stereo-based obstacle avoidance system for mobile vehicles. The system operates in three steps. First, it models the surface geometry of the supporting surface and removes the supporting surface from the scene. Next, it segments the remaining stereo disparities into connected components in image and disparity space. Finally, it projects the resulting connected components onto the supporting surface and plans a path around them. One interesting aspect of this system is that it can detect both positive and "negative" obstacles (e.g. stairways) in its path. The algorithms we have developed have been implemented on a mobile robot equipped with a real-time stereo system. We present experimental results on indoor environments with planar supporting surfaces that show the algorithms to be both fast and robust.
29 Jan 2002
TL;DR: The fuzzy logic based strategy described in the paper employs an arbiter which assigns a robot to shoot or pass the ball, and dynamic role switching and formation control are crucial for a successful game.
Abstract: Robot soccer is a challenging platform for multi-agent research, involving topics such as real-time image processing and control, robot path planning, obstacle avoidance and machine learning. The robot soccer game presents an uncertain and dynamic environment for cooperating agents. Dynamic role switching and formation control are crucial for a successful game. The fuzzy logic based strategy described in the paper employs an arbiter which assigns a robot to shoot or pass the ball.
01 Aug 2002
TL;DR: In closed-loop simulations in a highly realistic virtual environment, four independent, purely reactive mechanisms based on optimized receptive fields for attitude control, course stabilization, obstacle avoidance and altitude control are sufficient for a fully autonomous and robust flight stabilization with all six degrees of freedom.
Abstract: Most flying insects extract information about their spatial orientation and self-motion from visual cues such as global patterns of light intensity or optic flow. We present an insect-inspired neuronal filter model and show how optimal receptive fields for the detection of flight-relevant input patterns can be derived directly from the local receptor signals during typical flight behavior. Using a least squares principle, the receptive fields are optimally adapted to all behaviorally relevant, invariant properties of the agent and the environment. In closed-loop simulations in a highly realistic virtual environment we show that four independent, purely reactive mechanisms based on optimized receptive fields for attitude control, course stabilization, obstacle avoidance and altitude control, are sufficient for a fully autonomous and robust flight stabilization with all six degrees of freedom.