Automatic design of fuzzy controllers for car-like autonomous robots

This paper describes the design and implementation of a fuzzy control system for a car-like autonomous vehicle. The problem addressed is the diagonal parking in a constrained space, a typical problem in motion control of nonholonomic robots. The architecture proposed for the fuzzy controller is a hierarchical scheme which combines seven modules working in series and in parallel. The rules of each module employ the adequate fuzzy operators for its task (making a decision or generating a smoothly varying control output), and they have been obtained from heuristic knowledge and numerical data (with geometric information) depending on the module requirements (some of them are constrained to provide paths of near-minimal lengths). The computer-aided design tools of the environment Xfuzzy 3.0 (developed by some of the authors) have been employed to automate the different design stages: 1) translation of heuristic knowledge into fuzzy rules; 2) extraction of fuzzy rules from numerical data and their tuning to give paths of near-minimal lengths; 3) offline verification of the control system behavior; and 4) its synthesis to be implemented in a true robot and be verified on line. Real experiments with the autonomous vehicle ROMEO 4R (designed and built at the Escuela Superior de Ingenieros, University of Seville, Seville, Spain) demonstrate the efficiency of the described controller and of the methodology followed in its design.     

A Control Architecture for Time‐Optimal Landing of a Quadrotor Onto a Moving Platform

We address the problem of autonomous landing of a quadrotor onto a heaving (moving vertically) platform in this paper. A control architecture that consists of a motion estimation module, a trajectory generation module and a tracking control module is proposed. The motion estimation module estimates the absolute motion of the platform and the quadrotor with the measurements from an on‐board accelerometer and vision measurements. Based on these estimates, the trajectory generation module generates a time‐optimal reference trajectory. With the reference trajectory and motion estimation, the tracking control module synthesizes a control command that enables robust tracking of the reference trajectory. Experimental results and comparison with a state‐of‐the‐art landing controller demonstrate the effectiveness of the proposed control architecture.     

自主车的局部路径规划

局部路径规划是自主车的一项关键技术,它的品质 密切关系到整个自主车系统的性能．本文提出了将自主式多智能体的任务和反应性行为模型 嵌入到离散事件系统框架中作局部路径规划的方法,此方法克服了势场法（包括早期的虚力 场法）的缺陷——即由于把所有信息压缩为单个合力而损失部分有价值的局部障碍物分布信 息,从而提高了局部路径规划的可靠性．     

A parallel processing architecture for sensor-based control of intelligent mobile robots

Parallel processing plays an important role in sensor-based control of intelligent mobile robots. This paper describes the design and implementation of a parallel processing architecture used for real-time, sensor-based control of mobile robots. This architecture takes the form of a network of sensing and control nodes, based on a novel module that we call Locally Intelligent Control Agent (LICA). It is a hybrid control architecture containing low-level feedback control loops and high-level decision making components. All the sensing, planning, and control tasks for intelligent control of a mobile robot are distributed across such a network, and operate in parallel. It has been used successfully in many experiments to perform planning and navigation tasks in real-time. Such a generic architecture can be readily applied to many diverse applications.     

Obstacle avoidance with translational and efficient rotational motion control considering movable gaps and footprint for autonomous mobile robot

This paper presents a sensor-based real-time obstacle avoidance method for an autonomous omnidirectional mobile robot based on simultaneous control of translational and efficient rotational motion considering movable gaps and the footprint. Autonomous mobile service robots that have been developed in recent years have arms that work and execute tasks. Depending on the task using moving parts, the shape of the robot (i.e., the footprint) changes. In this study, to improve the safety and possibility of reaching a goal even through a narrow gap with unknown obstacles, a sensor-based real-time obstacle avoidance method with simultaneous control of translational and efficient rotational motion (without unnecessary rotational motion) based on the evaluation of movable gaps and the footprint is proposed. To take account of the anisotropy footprint of the robot, multiple-circle robot model is proposed. In this paper, a novel control method based on fuzzy set theory is presented. To verify the effectiveness of the proposed method, several simulations and experiments are carried out.

     

Path following of car-like vehicles using dynamic inversion

This paper focuses on a special path following task arising from the needs of vision-based autonomous guidance: a given front point of a car-like vehicle that is within the look-ahead range of a stereo vision system must follow a prespecified Cartesian path. A solution to this path following problem is provided by a new feedforward/feedback control strategy where the feedforward is determined by a dynamic generator based on exact dynamic inversion over the nominal vehicle model and the feedback is mainly issued by correcting terms proportional to the tangential and normal errors determined with respect to the vehicle’s ideal trajectory. A convergence analysis of the resulting dynamic inversion based controller is established versus a vehicle’s uncertain model defined via equation errors. Simulation examples highlighting the controller’s performances are included.     

Neuro-fuzzy and model-based motion control for mobile manipulator among dynamic obstacles

This paper focuses on autonomous motion control of a nonholonomic platform with a robotic arm, which is called mobile manipulator. It serves in transportation of loads in imperfectly known industrial environments with unknown dynamic obstacles. A union of both procedures is used to solve the general problems of collision-free motion. The problem of collision-free motion for mobile manipulators has been approached from two directions, Planning and Reactive Control. The dynamic path planning can be used to solve the problem of locomotion of mobile platform, and reactive approaches can be employed to solve the motion planning of the arm. The execution can generate the commands for the servo-systems of the robot so as to follow a given nominal trajectory while reacting in real-time to unexpected events. The execution can be designed as an Adaptive Fuzzy Neural Controller. In real world systems, sensor-based motion control becomes essential to deal with model uncertainties and unexpected obstacles.     

Active sonar-based bottom-following for unmanned underwater vehicles

The problem of high-precision bottom-following in the proximity of the seabed for open-frame unmanned underwater vehicles (UUVs) is addressed in this paper. The suggested approach consists of the integration of a guidance and control system with an active multi-hypothesis extended Kalman filter, able to estimate the motion of the vehicle with respect to the bottom profile. The guidance module is based on the definition of a suitable Lyapunov function associated with the bottom-following task, while the motion controller is a conventional autopilot, performing autoheading, autodepth, and autospeed. The motion of the vehicle is estimated from range and bearing measurements supplied by a high-frequency pencil-beam profiling sonar. Moreover, a general-purpose sensor-based guidance and control system for advanced UUVs, able to manage active sensing-based guidance and motion estimation modules, is presented. An application of the proposed architecture to execute high-precision bottom-following using Romeo, a prototype UUV, developed by the Robotics Dept. of the Istituto Automazione Navale, is described. Experimental results of tests, conducted in a high-diving pool with the vehicle equipped with a sonar profiler, are presented.     

Autonomous Vehicle Parking Using Hybrid Artificial Intelligent Approach

This paper devotes to design and implement a hybrid artificial intelligent control scheme for a car-like vehicle to perform the task of optimal parking. The parallel parking control scheme addresses three issues: trajectory planner, decisional kernel, and trajectory tracking control. Design of the control scheme consists of several techniques: genetic algorithm, Petri net, and fuzzy logic control. The genetic algorithm is used to determine the feasible parking locations. The Petri net is used to replace the traditional decision flow chart and plan alternative parking routes especially in global space. The parking routine can be re-performed if the initially assigned route is interfered or when the targeted parking space has been occupied. The fuzzy logic controller is used to drive the vehicle along with the optimal parking route. The proposed scheme is put into several scenarios to test and verify its applicability and to manifest its distinguished features.     

智能海洋机器人技术进展

智能海洋机器人是可以在复杂海洋环境中执行各种任务的智能化无人平台, 包括智能水下机器人和智能水面机器人. 基于实践和在相关技术难题上的经验, 提出了一些关键技术问题的解决方案. 在智能水下机器人方面, 探讨了体系结构、运动控制、智能规划与决策和系统仿真等关键技术. 在智能水面机器人方面, 探讨了快速性和智能化问题. 我国在智能海洋机器人技术研究方面已经取得了较大的进步, 但距离全面应用还有很大差距.     

Receding-Horizon Trajectory Planning for Under-Actuated Autonomous Vehicles Based on Collaborative Neurodynamic Optimization

This paper addresses a major issue in planning the trajectories of under-actuated autonomous vehicles based on neurodynamic optimization. A receding-horizon vehicle trajectory planning task is formulated as a sequential global optimization problem with weighted quadratic navigation functions and obstacle avoidance constraints based on given vehicle goal configurations. The feasibility of the formulated optimization problem is guaranteed under derived conditions. The optimization problem is sequentially solved via collaborative neurodynamic optimization in a neurodynamics-driven trajectory planning method/procedure. Simulation results with under-actuated unmanned wheeled vehicles and autonomous surface vehicles are elaborated to substantiate the efficacy of the neurodynamics-driven trajectory planning method.      

3D Local Trajectory Planner for UAV

In this paper a local trajectory planner is described and applied. This planner works in three dimensional environment populated with static and passive movable obstacles. The main contribution of this paper is a proposal of a new method of autonomous navigation. Novelty of this approach relies on splitting the motion planning problem into two stages: a decision mode and a trace mode. In the decision mode, vehicle selects its current direction of motion on the basis of the current value of a performance index. In the trace mode vehicle traces boundary and edges of obstacles using its on-board sensors. Depending on vehicle"s environment, the two modes follow one another many times. Another new idea is a negative velocity feedback. The feedback stabilizes velocity of the vehicle around a value considered safe in a given environment. The planner, although autonomous, may be adjusted by higher order system (strategic behavior) by changing its parameters. It is not computationally intensive and therefore can be used in real-time applications.     

Autonomous driving at the handling limit using residual reinforcement learning

While driving a vehicle safely at its handling limit is essential in autonomous vehicles in Level 5 autonomy, it is a very challenging task for current conventional methods. Therefore, this study proposes a novel controller of trajectory planning and motion control for autonomous driving through manifold corners at the handling limit to improve the speed and shorten the lap time of the vehicle. The proposed controller innovatively combines the advantages of conventional model-based control algorithm, model-free reinforcement learning algorithm, and prior expert knowledge, to improve the training efficiency for autonomous driving in extreme conditions. The reward shaping of this algorithm refers to the procedure and experience of race training of professional drivers in real time. After training on track maps that exhibit different levels of difficulty, the proposed controller implemented a superior strategy compared to the original reference trajectory, and can to other tougher maps based on the basic driving knowledge learned from the simpler map, which verifies its superiority and extensibility. We believe this technology can be further applied to daily life to expand the application scenarios and maneuvering envelopes of autonomous vehicles.     

Deliberation and reactivity in autonomous mobile robots

This paper discusses issues related to the design of the control architectures for an autonomous mobile robot capable of performing tasks efficiently and intelligently, i.e. in a manner adapted to its environment, to its own state and to the execution status of its task. We present our developments and experimentations on mobile robot navigation and show how it is necessary to produce representations at several levels of abstraction, that are used by adequate processes for obstacle detection, target recognition, robot localization, and motion planning and control. We also show that deliberation is necessary for the robot in order to anticipate events, take efficient decisions, and react adequately to asynchronous events. We also discuss the organization of the system, i.e. the design of the control architecture.     

Trajectory Planning and Control for Airport Snow Sweeping by Autonomous Formations of Ploughs

This article presents a control approach that enables an autonomous operation of fleets of unmanned snow ploughs at large airports. The proposed method is suited for the special demands of tasks of the airport snow shovelling. The robots have to keep a compact formation of variable shapes during moving into the locations of their deployment and for the autonomous sweeping of runways surfaces. These tasks are solved in two independent modes of the airport snow shoveling. The moving and the sweeping modes provide a full-scale solution of the trajectory planning and coordination of vehicles applicable in the specific airport environment. Nevertheless, they are suited for any multi-robot application that requires complex manoeuvres of compact formations in dynamic environment. The approach encapsulates the dynamic trajectory planning and the control of the entire formation into one merged optimization process via a novel Model Predictive Control (MPC) based methodology. The obtained solution of the optimization includes a complete plan for the formation. It respects the overall structure of the workspace and actual control inputs for each vehicle to ensure collision avoidance and coordination of team members. The presented method enables to autonomously design arbitrary manoeuvres, like reverse driving or turning of compact formations of car-like robots, which frequently occur in the airport sweeping application. Examples of such scenarios verifying the performance of the approach are shown in simulations and hardware experiments in this article. Furthermore, the requirements that guarantee a convergence of the group to a desired state are formulated for the formation acting in the sweeping and moving modes.     

A Fuzzy-Logic-Based Approach for Mobile Robot Path Tracking

One important problem in autonomous robot navigation is the effective following of an unknown path traced in the environment in compliance with the kinematic limits of the vehicle, i.e., bounded linear and angular velocities and accelerations. In this case, the motion planning must be implemented in real-time and must be robust with respect to the geometric characteristics of the unknown path, namely curvature and sharpness. To achieve good tracking capability, this paper proposes a path following approach based on a fuzzy-logic set of rules which emulates the human driving behavior. The input to the fuzzy system is represented by approximate information concerning the next bend ahead the vehicle; the corresponding output is the cruise velocity that the vehicle needs to attain in order to safely drive on the path. To validate the proposed algorithm two completely different experiments have been run: in the first experiment, the vehicle has to perform a lane-following task acquiring lane information in real-time using an onboard camera; in the second, the motion of the vehicle is obtained assigning in real-time a given time law. The obtained results show the effectiveness of the proposed method     

A new continuous-curvature line/path-tracking method for car-like vehicles

In this paper, we first investigate the problem of finding an algorithm for the movement of a car-like vehicle to track a given directed straight line. Car-like vehicles' motions possess 2 d.o.f., speed v and path curvature κ. We compute the 'derivative of path curvature', λ = dκ/ds, rather than the path curvature κ itself, to enforce continuous-curvature vehicle motions. Specifically, we compute λ as a linear function of the current path curvature, orientation and positional difference of the vehicle against the given line. We call this function λ the steering function. The uniform asymptotic stability of the feedback rule is proved through linearization and a Lyapunov function. Next, this basic line-tracking result is applied to the problem of path tracking where a path consists of directed straight lines. The main advantages and features of the results are summarized as follows. (i) Since the derivative dκ/ds of path curvature is obtained, the path curvature is always continuous while a vehicle converges to the line. (ii) By assuming the critical damping condition, the steering function contains one parameter σ that controls the smoothness (or equivalently, sharpness) of vehicle motions. (iii) This theory can be applied to a vehicle with any wheel architecture, i.e. this theory is vehicle independent. (iv) The theory can easily be extended to the more general problem of tracking a path consisting of any number of directed lines with a new principle of neutral switching. (v) The simplicity and machine independence of this theory makes implementation of this theory on vehicles easy. We present numerous simulation results of line/path-tracking motions. These results verify the effectiveness of this continuous-curvature motion control method. Some successful results obtained on the autonomous robot Yamabico at the Naval Postgraduate School are also included.     

Autonomous Navigation of UAV in Foliage Environment

This paper presents a navigation system that enables small-scale unmanned aerial vehicles to navigate autonomously using a 2D laser range finder in foliage environment without GPS. The navigation framework consists of real-time dual layer control, navigation state estimation and online path planning. In particular, the inner loop of a quadrotor is stabilized using a commercial autopilot while the outer loop control is implemented using robust perfect tracking. The navigation state estimation consists of real-time onboard motion estimation and trajectory smoothing using the GraphSLAM technique. The onboard real-time motion estimation is achieved by a Kalman filter, fusing the planar velocity measurement from matching the consecutive scans of a laser range finder and the acceleration measurement of an inertial measurement unit. The trajectory histories from the real-time autonomous navigation together with the observed features are fed into a sliding-window based pose-graph optimization framework. The online path planning module finds an obstacle-free trajectory based the local measurement of the laser range finder. The performance of the proposed navigation system is demonstrated successfully on the autonomous navigation of a small-scale UAV in foliage environment.     

Hybrid intelligent vision-based car-like vehicle backing systems design

This paper is integrated with discrete wavelet transformation (DWT), self-organizing map (SOM) neural network and fuzzy logic control methods to approach the intelligent vision-based car-like vehicle backing system. All the presented procedures are implemented as software simulations and are synthesized as hardware to describe their superiorities in our experimental platform. In a word, this study has been implemented as a car-like vehicle consists of a field programmable gate array (FPGA) chip, a CMOS image sensor, a microprocessor, a flash memory module, and servo motors. From our experimental results, this paper not only illustrates the results with computer simulation, but also demonstrates the practical car parking achievements in a real environment.     

Target following with motion prediction for unmanned surface vehicle operating in cluttered environments

The capability of following a moving target in an environment with obstacles is required as a basic and necessary function for realizing an autonomous unmanned surface vehicle (USV). Many target following scenarios involve a follower and target vehicles that may have different maneuvering capabilities. Moreover, the follower vehicle may not have prior information about the intended motion of the target boat. This paper presents a trajectory planning and tracking approach for following a differentially constrained target vehicle operating in an obstacle field. The developed approach includes a novel algorithm for computing a desired pose and surge speed in the vicinity of the target boat, jointly defined as a motion goal, and tightly integrates it with trajectory planning and tracking components of the entire system. The trajectory planner generates a dynamically feasible, collision-free trajectory to allow the USV to safely reach the computed motion goal. Trajectory planning needs to be sufficiently fast and yet produce dynamically feasible and short trajectories due to the moving target. This required speeding up the planning by searching for trajectories through a hybrid, pose-position state space using a multi-resolution control action set. The search in the velocity space is decoupled from the search for a trajectory in the pose space. Therefore, the underlying trajectory tracking controller computes desired surge speed for each segment of the trajectory and ensures that the USV maintains it. We have carried out simulation as well as experimental studies to demonstrate the effectiveness of the developed approach.