Moving Obstacle Avoidance Research Articles

The obstacle avoidance motion planning problem for autonomous vehicles: A low-demanding receding horizon control scheme

The paper addresses the obstacle avoidance motion planning problem for ground vehicles operating in uncertain environments. By resorting to set-theoretic ideas, a receding horizon control algorithm is proposed for robots modelled by linear time-invariant (LTI) systems subject to input and state constraints and disturbance effects. Sequences of inner ellipsoidal approximations of the exact one-step controllable sets are pre-computed for all the possible obstacle scenarios and then on-line exploited to determine the more adequate control action to be applied to the robot in a receding horizon fashion. The resulting framework guarantees Uniformly Ultimate Boundedness and constraints fulfilment regardless of any obstacle scenario occurrence.

Learning fuzzy controllers in mobile robotics with embedded preprocessing

The automatic design of controllers for mobile robots usually requires two stages. In the first stage, sensorial data are preprocessed or transformed into high level and meaningful values of variables which are usually defined from expert knowledge. In the second stage, a machine learning technique is applied to obtain a controller that maps these high level variables to the control commands that are actually sent to the robot. This paper describes an algorithm that is able to embed the preprocessing stage into the learning stage in order to get controllers directly starting from sensorial raw data with no expert knowledge involved. Due to the high dimensionality of the sensorial data, this approach uses Quantified Fuzzy Rules (QFRs), that are able to transform low-level input variables into high-level input variables, reducing the dimensionality through summarization. The proposed learning algorithm, called Iterative Quantified Fuzzy Rule Learning (IQFRL), is based on genetic programming. IQFRL is able to learn rules with different structures, and can manage linguistic variables with multiple granularities. The algorithm has been tested with the implementation of the wall-following behavior both in several realistic simulated environments with different complexity and on a Pioneer 3-AT robot in two real environments. Results have been compared with several well-known learning algorithms combined with different data preprocessing techniques, showing that IQFRL exhibits a better and statistically significant performance. Moreover, three real world applications for which IQFRL plays a central role are also presented: path and object tracking with static and moving obstacles avoidance.

Dynamic optimal payload path planning of mobile manipulators among moving obstacles

This paper is dealt with dynamic analysis of the wheeled mobile manipulators in the presence of moving obstacles considering optimal payload criterion. General dynamic formulation of the system was derived, and the moving obstacle avoidance strategy was proposed in terms of dynamic potential functions. The problem of dynamic motion planning and payload maximization was formulated using open-loop optimal control theory. Then, the indirect solution based on Pontryagin’s minimum principle was employed to solve the problem. Using the proposed method, complete nonlinear states and control constraints were treated without any simplifications such as linearizing the dynamics equations, discretizing the robot’s workspace, or parameterizing the solution. The proposed method will be useful for the system design and in the situation where the trajectories of obstacles are predefined. Finally, capability and applicability of the proposed method were investigated by the number of simulations on a two-link mobile manipulator.

Multi-Robot nonlinear model predictive formation control: the obstacle avoidance problem

SUMMARYThis paper discusses about a proposed solution to the obstacle avoidance problem in multi-robot systems when applied to active target tracking. It is explained how a nonlinear model predictive formation control (NMPFC) previously proposed solves this problem of fixed and moving obstacle avoidance. First, an approach is presented which uses potential functions as terms of the NMPFC. These terms penalize the proximity with mates and obstacles. A strategy to avoid singularity problems with the potential functions using a modified A* path planning algorithm was then introduced. Results with simulations and experiments with real robots are presented and discussed demonstrating the efficiency of the proposed approach.

Worst-case analysis of moving obstacle avoidance systems for unmanned vehicles

SUMMARYThis paper investigates worst-case analysis of a moving obstacle avoidance algorithm for unmanned vehicles in a dynamic environment in the presence of uncertainties and variations. Automatic worst-case search algorithms are developed based on optimization techniques, and illustrated by a Pioneer robot with a moving obstacle avoidance algorithm developed using the potential field method. The uncertainties in physical parameters, sensor measurements, and even the model structure of the robot are taken into account in the worst-case analysis. The minimum distance to a moving obstacle is considered as an objective function in automatic search process. It is demonstrated that a local nonlinear optimization method may not be adequate, and global optimization techniques are necessary to provide reliable worst-case analysis. The Monte Carlo simulation is carried out to demonstrate that the proposed automatic search methods provide a significant advantage over random sampling approaches.

Autonomous Visual Navigation and Laser-Based Moving Obstacle Avoidance

Moving obstacle avoidance is a fundamental requirement for any robot operating in real environments, where pedestrians, bicycles, and cars are present. In this paper, we propose and validate a framework for avoiding moving obstacles during visual navigation with a wheeled mobile robot. Visual navigation consists of following a path, represented as an ordered set of key images, which have been acquired by an on-board camera in a teaching phase. While following such a path, our robot is able to avoid static and moving obstacles, which were not present during teaching, and which are sensed by an on-board lidar. The proposed approach takes explicitly into account obstacle velocities, estimated using an appropriate Kalman-based observer. The velocities are then used to predict the obstacle positions within a tentacle-based approach. Finally, our approach is validated in a series of real outdoor experiments, showing that when the obstacle velocities are considered, the robot behavior is safer, smoother, and faster than when it is not.

Kinodynamic Motion Planning for Autonomous Vehicles

This article proposes a computationally effective motion planning algorithm for autonomous ground vehicles operating in a semi-structured environment with a mission specified by waypoints, corridor widths and obstacles. The algorithm switches between two kinds of planners, (i) static planners and (ii) moving obstacle avoidance manoeuvre planners, depending on the mobility of any detected obstacles. While the first is broken down into a path planner and a controller, the second generates a sequence of controls without global path planning. Each subsystem is implemented as follows. The path planner produces an optimal piecewise linear path by applying a variant of cell decomposition and dynamic programming. The piecewise linear path is smoothed by Bézier curves such that the maximum curvatures of the curves are minimized. The controller calculates the highest allowable velocity profile along the path, consistent with the limits on both tangential and radial acceleration and the steering command for the vehicle to track the trajectory using a pure pursuit method. The moving obstacle avoidance manoeuvre produces a sequence of time-optimal local velocities, by minimizing the cost as determined by the safety of the current velocity against obstacles in the velocity obstacle paradigm and the deviation of the current velocity relative to the desired velocity, to satisfy the waypoint constraint. The algorithms are shown to be robust and computationally efficient, and to demonstrate a viable methodology for autonomous vehicle control in the presence of unknown obstacles.

A distributed obstacle avoidance MPC strategy for leader-follower formations

In this paper we address the obstacle avoidance motion planning problem for leader-follower vehicles configurations operating in static environments. By resorting to set-theoretic ideas, a receding horizon control algorithm is proposed for robots modelled by linear time-invariant (LTI) systems subject to input and state constraints. Terminal robust positively invariant regions and sequences of precomputed inner approximations of the one-step controllable sets are on-line exploited to compute the commands to be applied in a receding horizon fashion. Moreover, we prove that the design of both terminal sets and one-step ahead controllable regions is achieved in a distributed sense. An illustrative example is used to show the effectiveness of the proposed control strategy.

Mobile Robot Motion Using Cellular Automaton Model to Avoid Transient Obstacles

The obstacle avoidance is currently treated by methods that fall into two broad categories: global and local approach. This paper considers the obstacles whose velocity and direction cannot be easily predicted. Such obstacle is called transient obstacle. To avoid such kind of obstacle, we introduce a local path planning method for a robotics by using cellular automaton approach. The cellular automaton was combined with Dijkstra shortest path algorithm as global path planning to obtain a path for mobile robot to be able to avoid transient obstacle along the path. Using the proposed method, a scene in a typical corridor has been created. Moreover, this paper also evaluated two kinds of obstacle avoidance motion. First, the robot uses the stop and go method, which is the robot decreases its speed while encounter a transient obstacle. The second one is detour method, in which the robot makes a detour motion to avoid a transient obstacle. To coupe the drawbacks of local path planning, this paper also propose the enhancement of detour method. The simulation results show that in dynamic environment with transient obstacles, the stop and go method produces minimal collision with shortest-distance path. While, using the detour method generates minimal collision with time-minimal navigation path.

Realization of Robot-assisted Laser Scanning Measurement System based on Assembly Platform

The assembly platform of complex structures with a variety of CNC tools can achieve automated clamping, riveting, drilling, seamless calibration docking, and can also realize connecting engineering of complex assembly. In this paper, to solve the issue of digital measurement positioning technology in complex-structure assembly platform, an industrial robot-assisted laser scanning system is designed, and its kinematics model is established. At the same time, the trajectory of the robot rotation scan is planned. Finally, the measurement accuracy of the system we have established proves to meet the ac- tual demands by assembling experiment. In the assembly of complex-structure, digitized measurement positioning technology is an important standard of assembly quality. The technology can be realized by the robots equipped with vision meas- urement system, which is real-time scanning, high measuring accuracy and efficiency. In the 1960s, there were researches of combining visual measurement systems and robot in the world. In 1969, Nilsson developed the first intelligent mobile robot named shakey equipped with a camera, rangefinder, and contact sensors at Stanford SRI Institute, and its task was automatic obstacle avoidance and target motion tracking. In the late 1990s, machine vision measurement technology be- gan to develop rapidly in China. Harbin Institute of Technology achieved a fully autonomous soccer robot navigation with heterogeneous binocular active vision systems, analyzed fusion method of foot- ball robot's navigation data based on the binocular coordination, and proposed the binocular coordina- tion navigation model and target implementation strategy based on its behavior (1). National University of Defense Technology studied surgical instrument tracking and positioning technology based on bin- ocular vision (2). Currently, machine vision technology is widely used in various aspects, especially in industrial production and assembly. The machine vision measurement technology has become increas- ingly mature, but the study of improving the measurement accuracy of vision measurement system is still a main direction that we need to further study. In this paper, through analyzing the complex-structure assembly and detection system, a laser scan- ning measurement system is built by the Swedish ABB robot equipped with a CCD(Charge-coupled Device) high-speed camera. The system proves to be large measurement range, high accuracy and real- time.

Trajectory optimization of nonholonomic mobile manipulators departing to a moving target amidst moving obstacles

How to plan the optimal trajectory of nonholonomic mobile manipulators in dynamic environments is a significant and challenging task, especially in the system with a moving target. This paper presents trajectory optimization of a nonholonomic mobile manipulator in dynamic environment pursuing a moving target. Full nonlinear dynamic equations of the system considering the nonholonomic constraints of wheels are presented. Then, dynamic motion planning of the system is formulated as an optimal control problem considering moving obstacle avoidance conditions. Accordingly, a new formulation of dynamic potential function was proposed based on the dynamic distance between colliding objects. In addition, an appropriate boundary value for a moving target was defined, and the resulted boundary value problem was solved to optimize the trajectory of the system. To solve the problem, an indirect solution of optimal control was applied which leads to transform the optimal control problem into a set of coupled differential equations. To demonstrate the efficiency and applicability of the method a number of simulations and experiments was performed for a spatial nonholonomic mobile manipulator.

A Framework for Obstacles Avoidance of Humanoid Robot Using Stereo Vision

In this paper, we propose a framework for multiple moving obstacles avoidance strategy using stereo vision for humanoid robot in indoor environment. We assume that this model of humanoid robot is used as a service robot to deliver a cup to customer from starting point to destination point. We have successfully developed and introduced three main modules to recognize faces, to identify multiple moving obstacles and to initiate a maneuver. A group of people who are walking will be tracked as multiple moving obstacles. Predefined maneuver to avoid obstacles is applied to robot because the limitation of view angle from stereo camera to detect multiple obstacles. The contribution of this research is a new method for multiple moving obstacles avoidance strategy with Bayesian approach using stereo vision based on the direction and speed of obstacles. Depth estimation is used to obtain distance calculation between obstacles and the robot. We present the results of the experiment of the humanoid robot called Gatotkoco II which is used our proposed method and evaluate its performance. The proposed moving obstacles avoidance strategy was tested empirically and proved effective for humanoid robot.

Modeling and Analysis of Human Navigation with Crossing Interferer Using Inverse Optimal Control

AbstractThe walking paths of humans in everyday life exhibit specific characteristics. Our investigation takes the well-established perspective that human locomotion approximately fulfills suitable optimality principles when walking from a starting position to a designated end position. Here, we address the special task of collision avoidance with a crossing interferer. While our model of the dynamics is quite simple, we focus on the task of determining the cost function out of a parametric family, that results in a best fit between the corresponding optimal control-based navigation and given recorded data of human navigation. The resulting bilevel optimization problem combines an optimal control problem on the lower level with a data fitting problem on the upper level. Our solution strategy uses the first-order optimality conditions of the (discretized) optimal control problem to transform the bilevel problem to a standard (one-level) optimization problem.Modeling aspects concerning the walking process and especially the interfering person are discussed. Since human walking motions with a crossing interferer do not seem to be globally optimal, an MPC-like approach distinguishing between obstacle avoidance and free motion is introduced and optimization results using recorded human data are presented.

Multiple Moving Obstacles Avoidance of Service Robot using Stereo Vision

In this paper, we propose a multiple moving obstacles avoidance using stereo vision for service robots in indoor environments. We assume that this model of service robot is used to deliver a cup to the recognized customer from the starting point to the destination. The contribution of this research is a new method for multiple moving obstacle avoidance with Bayesian approach using stereo camera. We have developed and introduced 3 main modules to recognize faces, to identify multiple moving obstacles and to maneuver of robot. A group of people who is walking will be tracked as a multiple moving obstacle, and the speed, direction, and distance of the moving obstacles is estimated by a stereo camera in order that the robot can maneuver to avoid the collision. To overcome the inaccuracies of vision sensor, Bayesian approach is used for estimate the absense and direction of obstacles. We present the results of the experiment of the service robot called Srikandi III which uses our proposed method and we also evaluate its performance. Experiments shown that our proposed method working well, and Bayesian approach proved increasing the estimation perform for absence and direction of moving obstacle.

Inconsistent Routes in Moving Obstacle Avoidance Are Due to Sensitivity to Initial Conditions, not Attention

Inconsistent Routes in Moving Obstacle Avoidance Are Due to Sensitivity to Initial Conditions, not Attention

Learning of Obstacle Avoidance with Redundant Manipulator by Hierarchical SOM

This paper proposes a method of obstacle avoidance motion generation for a redundant manipulator with a Self-OrganizingMap (SOM) and reinforcement learning. To consider redundancy, two types of SOMs - a hand position map and a joint angle map - are combined. Multiple joint angles corresponding to the same hand position are memorized in the proposed map. Preserved redundant configuration information is used to generate motions based on tasks and situations, while resolving inverse kinematics problems with a redundant manipulator. The proposed map is applied to planning motion control using reinforcement learning in an unknown environment, where collision with obstacles is detected only directly by tactile sensing. The feasibility of the proposed framework was verified by simulation and experiments with an arm robot with force and a vision sensors.

Navigation Function-Based Control of Multiple Wheeled Vehicles

In this paper, a collision-free navigation method for a group of autonomous wheeled vehicles is investigated. The position and orientation information of individual vehicles is transformed to navigation variables, which are the distance left to the goal position, the angle made by the orientation of the vehicle at the goal position and the vehicle-to-target (v-to-t) vector, and the angle made by the heading direction of the vehicle and the v-to-t vector. As a Lyapunov function for deriving a smooth control law that drives all the vehicles from an initial configuration to a goal configuration, a new navigation function that incorporates the squared norm of the navigation variables, the boundaries of collision-free areas, and the angles made by the vehicle heading direction and the vehicle-to-obstacle (v-to-o) vectors is proposed. The asymptotic stability of the closed-loop system is proved. The effectiveness of the developed algorithm is illustrated through three simulations (three vehicles in a free environment, three vehicles in the presence of a static obstacle, and eight vehicles operating along a corridor) and two experiments (static and moving obstacles avoidance). The proposed algorithm has been implemented on a real forklift and the navigation of the forklift from an arbitrary initial configuration to a goal configuration while avoiding collisions has been demonstrated.

Time-varying Path Following Control for Port-Hamiltonian Systems

This paper is devoted to path following control for port-Hamiltonian systems whose desired path is time-varying. Most of the existing results on path following can only take care of time invariant paths, hence they cannot be applied to control systems whose environments change, e.g., path following control with moving obstacle avoidance or with a path crossing itself. The proposed method solves this problem by employing decoupling control of three particular directions in the phase space which allows one to assign time-varying potential functions and vector fields.

A Robust Obstacle Avoidance for Service Robot Using Bayesian Approach

The objective of this paper is to propose a robust obstacle avoidance method for service robot in indoor environment. The method for obstacles avoidance uses information about static obstacles on the landmark using edge detection. Speed and direction of people that walks as moving obstacle obtained by single camera using tracking and recognition system and distance measurement using 3 ultrasonic sensors. A new geometrical model and maneuvering method for moving obstacle avoidance introduced and combined with Bayesian approach for state estimation. The obstacle avoidance problem is formulated using decision theory, prior and posterior distribution and loss function to determine an optimal response based on inaccurate sensor data. Algorithms for moving obstacles avoidance method proposed and experiment results implemented to service robot also presented. Various experiments show that our proposed method very fast, robust and successfully implemented to service robot called Srikandi II that equipped with 4 DOF arm robot developed in our laboratory.

A Method for Path Planning Strategy and Navigation of Service Robot

AbstractThis paper presents our work on the development of Path Planning Strategy and Navigation by using ANFIS(Adaptive Neuro-Fuzzy Inference System)controller for a vision-based service robot. The robot will deliver a cup to a recognized customer and a black line as the guiding track for navigating a robot with a single camera. The contribution of this research includes a proposed architecture of ANFIS controller for vision-based service robot integrated with improved face recognition system using PCA, and the algorithm for moving obstacle avoidance. We also propose a path planning algorithm based on Dijkstra's algorithm to obtain the shortest path for robot to move from the starting point to the destination. In order to avoid moving obstacles, we have proposed an algorithm using binaural ultrasonic sensors. The service robot called Srikandi is also equipped with 4 DOF arm and a framework of face recognition system. The proposed path planning strategy and navigation was tested empirically and proved effective.