Top PDF Dynamic Collision-avoidance Planning of Mobile Robot Based on Velocity Change Space

Dynamic Collision-avoidance Planning of Mobile Robot Based on Velocity Change Space

Dynamic Collision-avoidance Planning of Mobile Robot Based on Velocity Change Space

[16] Yamamoto M, Shimada M, Mohri A. On-line navigation of mo- bile robot under the existence of dynamically moving multiple obstacles[C]//IEEE International Symposium on Assembly and Task Planning. Piscataway, NJ, USA: IEEE, 2001: 13-18. 作者简介:

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Path Planning and Obstacle Avoidance for Boe Bot Mobile Robot-

Path Planning and Obstacle Avoidance for Boe Bot Mobile Robot-

For segmenting continuous-tone gray-scale images, we also proposed an extended discrete-time oscillator network with a degradation (posterization) system [12]. In this system, couplings between neighboring oscillators can change every discrete time. The updating of coupling strengths is based on the dynamics of a posterization system that is modified from a discrete-time system [13]. We demonstrated that the extended dynamic image-segmentation system worked well for continuous-tone gray- scale images [12].

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Decentralized Collision Avoidance

Decentralized Collision Avoidance

Navigating a mobile robot in dynamic environment is a considerably difficult task as there is sufficient amount of planning required with respect to the path and the velocity profile computed. Path planning is defined as calculating a trajectory for the robot from the start to the end position without colliding with any object on its way. Simultaneously a reasonable velocity profile needs to be maintained with respect to each robot so that the robot does not hit another robot while traversing towards its target position. Thereby Fiorni and Shiller [17] came up with a geometric and dynamic approach for robots to avoid collisions. The VO method set the platform for various algorithms for dynamic motion planning due to its advantage of giving a dependable geometric representation for allowing robots to shift and move away from obstacles. However, the method is defined for a specific time frame since in dynamic environments the constraints would keep evolving at different intervals. Hence the solution for each window of time may not be same. The biggest turning point with respect to this method is that the constraints of the robots in consideration is only the position and the velocity that makes it a simplified process and also provides lot of avenues for further exploration for dynamic motion planning especially for a large number of robots.
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A New Hybrid Method in Global Dynamic Path Planning of Mobile Robot

A New Hybrid Method in Global Dynamic Path Planning of Mobile Robot

Aim at this problem the dynamic window method is used in this paper. It has a good obstacle avoidance capability in the dynamic environment [6]. But this method does not satisfy the global optimal path planning. The node-based A* algorithm is suitable for global path planning. It has the advantages of simple data, small calculation compared to the biological inspired GA, PSO, etc [7] [9]. But when the environmental space increases, it needs large storage space, low efficiency, and poor real-time decision-making [11]. So the path curvature of the traditional A* algorithm is discontinuous. It will cause the movement parameters jump at the turning point. These are not conducive to the robot control problems [1]. Aim at these problems the algorithm is improved. It makes the path smoother on the basis of the overall optimization of the planning path [12]. so a global dynamic path planning method is proposed that it is combining improved A* algorithm and dynamic window method in the paper. It is proposed the evaluation function taking into account the global optimal path. And the real-time path planning is carried out by using dynamic window method based on the evaluation function. It would make the path smoother on the basis of ensuring the overall optimization of the planning path. At the same time it improves the local obstacle avoidance.
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Simulation of Path Planning of Mobile Robot in Dynamic Environment

Simulation of Path Planning of Mobile Robot in Dynamic Environment

Path planning of a robot [1] is a problem in which a problem space or a problem domain with a number of obstacles is given and the aim is to find a collision free path which a robot can follow in order to reach its destination from the start position. Here in this paper environment is represented by ordered grids, each of which represents a location in the mobile robot movement space. Along with this we would be able to place dynamic no. of static and movable obstacles at run time. The boundary of obstacles is formed by their actual boundary plus minimum safety distance considering the size of the mobile robot, which makes it possible to treat the mobile robot a point in the environment. The path [2] which the robot will follow is desired to be optimal in terms of distance and time taken by the robot to reach the destination. Here in this problem we have used Genetic Algorithm for path planning which is a search algorithm based on the mechanics of natural selection and natural genetics. Potential solutions of a problem are chromosomes, which form a population of possible solutions which compete with each other on the basis of fitness function. A selection mechanism based on the fitness is applied to the population and the individuals strive for the survival.
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Velocity profile compliance for a wheeled mobile robot

Velocity profile compliance for a wheeled mobile robot

Commonly, many researchers are concerned about the energy consumption on path planning for a wheeled mobile robot [1], [6]. They come out with many methods and algorithm which are very useful for the manufacturer. Some of them also concerned the energy saving on the battery consumption [1], [2], [4], [7]. From these researches, it is very important for us also to take our responsibility to concern about the energy consumption for wheeled mobile robots that can be vary from a velocity profile.

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Dynamic Obstacle Avoidance in Multi-Robot Motion Planning Using Prediction Principle in Real Environment

Dynamic Obstacle Avoidance in Multi-Robot Motion Planning Using Prediction Principle in Real Environment

The rest of the paper is organized as follows. Section II considers a formulation of the problem presuming static and dynamic obstacles separately, and then combines them together to construct a general objective function for the problem. The prediction approach has been incorporated here. The principle of PSO is outlined in section III. Sec- tion IV provides the algorithm to solve both the static and dynamic obstacle avoidance problem by using PSO. Com- puter simulation over simulated platforms is given in sec- tion V. Online experimental details with snapshots as well as the overview of the Khepera Robot used in the experi- ment are discussed in section VI. Performance analysis with respect to two standard metrics is undertaken in sec- tion VII. Inferences drawn from the paper are given in sec- tion VIII with a tinge of references at the end.
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Dynamic collision avoidance using local cooperative airplanes decisions

Dynamic collision avoidance using local cooperative airplanes decisions

(b) Collision detection In ATC, two airplanes are consid- ered in conflict whenever a violation of the protection zone is detected. For the sake of clarity, in the follow- ing an ATC loss of separation will be refereed as a col- lision, and is considered as a collision by CAAMAS. In order to take into account the fact that the verti- cal distance is not on the same scale as the horizontal distance, the collision criticality is calculated on the horizontal plan and the vertical plan. Thus, we have two values for d min,i,j,k : d min,i,j,k,h and d min,i,j,k,v , and two values for time t min,i,j,k (or an interval): t min,i,j,k,h and
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Programmable velocity profile for wheeled mobile robot

Programmable velocity profile for wheeled mobile robot

ii. The performance analysis of the velocity profile is taken regardless the actual characteristics of robotics wheelchair. Due to the limitation of resources, a mini-model of robotic wheelchair has been developed to provide simulation and test run during experimental setup. Hence, the parameters such as terrain condition, torque of motors, and stability of the wheelchair were not taken into consideration.

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Space debris collision avoidance using a three-filter sequence

Space debris collision avoidance using a three-filter sequence

The only limitation of this filter sequence theory arises from space debris with poor orbit predictability, which can occur when there are unannounced manoeuvres or a large effect of drag subject to unpredictable changes in atmospheric scaleheight. In general, this is when the orbit determination becomes non-linear or non- deterministic, in which case it would be necessary to use much more complicated methods. In this paper, we compute the ephemerides of each piece of space debris with the dynamical systems developed by the naXys team, which include the Earth’s gravitational potential, the luni-solar and planetary gravitational perturbations and the solar radiation pressure effect (Valk, Lemaˆıtre & Anselmo 2008; Valk, Lemaˆıtre & Deleflie 2009a; Valk et al. 2009b; Lemaˆıtre, Delsate & Valk 2009; Delsate et al. 2010; Hubaux et al. 2012; Casanova & Lemaˆıtre 2014).
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The Remotely Reconfigurable Intelligence of the Space Based Mobile Robot

The Remotely Reconfigurable Intelligence of the Space Based Mobile Robot

the process of hardware reconfiguration. The earlier mentioned principles of space-based Proposed concept provides simple and automatic robotic systems design allow to implement a wide range hardware reconfiguration. The concept includes: of intellectual features necessary to work in partially C Automatic remote reconfiguration without operator According to the principle of situational control, each participation class of system states which is considered possible in the C Reconfiguration without physical exchange of process of research corresponds certain control solution

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Implementation of Robot Operating System in Beaglebone Black based Mobile Robot for Obstacle Avoidance Application

Implementation of Robot Operating System in Beaglebone Black based Mobile Robot for Obstacle Avoidance Application

Abstract— The Robot Operating System (ROS) is a collection of tools, libraries, and conventions that focus on simplifying the task of creating a complex and advanced robotics system. Its standard framework can be shared with another robotics system that has a similar platform and suitable for being introduced as an educational tool in robotics. However, the problems found out in the current robot platform available in the market are expensive and encapsulated. The development of an open source robot platform is encouraged. Therefore, this research is carried out to design and develop an ROS based obstacle avoidance system for existing differential-wheeled mobile robot. The ROS was installed under Ubuntu 14.04 on a Beaglebone Black embedded computer system. Then, the ROS was implemented together with the obstacle avoidance system to establish the communication between program nodes. The mobile robot was then designed and developed to examine the obstacle avoidance application. The debugging process was carried out to check the obstacle avoidance system application based on the communication between nodes. This process is important in examining the message publishing and subscribing from all nodes. The obstacle avoidance mobile robot has been successfully tested where the communication between nodes was running without any problem.
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A Bio-Inspired Model for Visual Collision Avoidance on a Hexapod Walking Robot

A Bio-Inspired Model for Visual Collision Avoidance on a Hexapod Walking Robot

avoidance, autonomous robots nowadays normally rely on active sensors (e.g. laser range finders [ 13 ]) or extensive computations (e.g. Lucas-Kanade optic flow computation [ 10 ]), insects are able to do so with minimal energetic and computational expenditure by relying mainly on visual information. We imple- mented a bio-inspired model of collision avoidance in simulations of the hexa- pod walking robot HECTOR solely based on the processing of optic flow by correlation-type elementary motion detectors (EMDs). EMDs have previously been accounted for playing a key role in the processing of visual motion infor- mation in insects [ 3 ]. As could be shown, although the responses of EMDs to visual motion are entangled with the textural properties of the environment [ 5 ], the relative nearness information obtained from optic flow estimation via EMDs is sufficient to direct HECTOR to a goal location in cluttered environments with- out colliding with obstacles. This holds true either for artificially generated envi- ronments as well as for a reconstructed natural environment, which substantially differ in their textural pattern properties. Moreover, by employing behavioural strategies such as (a) an active-gaze strategy and (b) active head stabilisation – both also found in insects – the influence of rotational optic flow components which potentially obfuscate the estimation of relative nearness information from optic flow is reduced. Hence, on the physical robot a prototype for mechanical gaze-stabilisation has been implemented and is currently compared to a software implementation.
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Vision Based Obstacle Avoidance For Mobile Robot Using Optical Flow Process

Vision Based Obstacle Avoidance For Mobile Robot Using Optical Flow Process

Abstract: The paper is discuss on develop and implement a vision based obstacle avoidance for mobile robot using optical flow process. There are four stages in this project which are image pre-processing, optical flow process, filtering, object stance measuring and obstacle avoidance. The optical flow process are an image resizing, set parameters, convert color to grayscale, Horn-Schunk method and change grayscale image to binary number. Next process is a filtering done by smoothing filter then image center will be defined. The maximum distance object from a camera has been set as 20 cm. Therefore, the decisions of the robot to move whether left or right are based on the direction of optical flow. This avoidance algorithm allows the mobile robot to avoid the obstacles which are in different shape either square or rectangular. A friendly graphical user interface (GUI) had been used to monitor the activity of mobile robot during run the systems.
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Mean crossover in evolutionary path planning method for maritime collision avoidance

Mean crossover in evolutionary path planning method for maritime collision avoidance

This paper presents the use of mean crossover genetic operator for path planning using evolutionary algorithm for collision avoidance on sea. Mean crossover ensures widening of the possible solutions’ set that can be achieved in comparison to exchange crossover variant. The research shown, that the mean crossover allows to achieve results independent from the initial generation and quicker transition of the algorithm from the exploration to the exploitation phase. New version of the algorithm allows for an effective solution search for the problem of a collision scenario on sea.
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Odometry Algorithm with Obstacle Avoidance on Mobile Robot Navigation

Odometry Algorithm with Obstacle Avoidance on Mobile Robot Navigation

Odometry is measurement method from motion sensor or rotation sensor to estimate change in position over time. Odometry is used by some robots, whether they be legged or wheeled, to estimate (not determine) their position relative to a starting location. This method is sensitive to errors due to the integration of velocity measurements over time to give position estimates. Rapid and accurate data col- lection, equipment calibration, and processing are required in most cases for odometry to be used effectively.

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Integrated Trajectory Planning and Torque Vectoring for Autonomous Emergency Collision Avoidance

Integrated Trajectory Planning and Torque Vectoring for Autonomous Emergency Collision Avoidance

and transient driving conditions [11]. The concept of torque vectoring is to control the traction and braking torque of each wheel to generate a direct yaw moment. In order to benefit from advantages of torque vectoring controllers in collision avoidance system applications of autonomous vehicles in emergency scenarios [12][13], this paper proposes a novel control architecture where MPC is used to design feasible and safe trajectories for emergency collision avoidance and a torque vectoring controller to ensure that the vehicle can follow the designed reference without any loss of yaw-lateral stability. The results show that the proposed control strategy can maintain autonomous vehicle’s stability while avoiding collision in high-speed and low surface friction conditions under presence of external distur- bances such as heavy crosswind. The trajectory plan- ning controller presented in this paper is an extension of the work done in [14]. The main difference lies in the use of a bicycle model of the vehicle instead of a point mass model as used in [14] since a bicycle model captures vehicle dynamics more accurately which is a crucial requirement for computing admissible tra- jectories for collision avoidance. The stabilization of the vehicle motion during the emergency maneuver is based on the torque-vectoring technique proposed in [15]. In accordance to [15], this approach allows the vehicle to be manipulated as nominal driving con- ditions (e.g., high friction surface) even in the pres- ence of uncertainties and external disturbances. The efficacy of the integrated trajectory planning and the stabilization controller is demonstrated for emergency collision avoidance in high-speed and low friction envi- ronments. Moreover, the robustness of such integrated control scheme has been tested in the presence of heavy cross wind and has been validated in IPG CarMaker- Simulink co-simulation environment. The remaining of the paper is structured as follows: Section II introduces control system architecture in the paper. In section III, The control formulation for trajectory planning, torque vectoring and reference torque generator will be discussed. In section IV the numerical results will be examined. At the end, the concluding remarks are discussed in Section V.
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Path Following and Velocity Optimizing for an Omnidirectional Mobile Robot

Path Following and Velocity Optimizing for an Omnidirectional Mobile Robot

Some related work is as follows: Vazquez et al. [17] presented an extension of the computed torque control commonly used in the field of robot manipulators to the case of an OMR. Conceicao et al. [9] proposed a nonlinear model based predictive controller for an OMR. The cost function penalizes the robot position error, the robot orientation angle error and the control effort. Huang and Tsai [18] presented an adaptive robust control method for path following of an OMR with actuators’ uncertainties. Kanjanawanishkul and Zell [14] used model predictive control to generate an optimal velocity of the virtual vehicle. Recently, Oftadeh et al. [15] proposed a new solution to the path following problem of independently steered mobile robots that possess omnidirectionality. The speed of the base can be determined analytically to keep the steering and driving velocities of the wheels under prespecified values. In this work, the path following controller proposed by Oftadeh et al. [15] was adopted with slight modification. The interesting point of this controller is that stability of the path following controller is attained without regard to the forward velocity. As a result, the forward velocity becomes an independent variable. This allows us to find its optimal value by using the model predictive control (MPC) framework. It is optimal in the sense that the forward velocity is close to the desired one, whereas the actuator constraints are not violated. To do so, in our MPC framework design, a path following closed-loop system is used as a predicted model. The forward velocity is integrated into the objective function, while the actuator constraints are considered as hard constraints of the MPC framework. The rest of the paper is organized as follows: in Section 2, the OMR kinematics is derived, while the path following controller proposed by Oftadeh et al. [15] with slight modification is explained in Section 3. Our MPC framework with consideration of actuator constraints and optimal forward velocity are developed in Section 4. Then, simulation experiments are conducted in Section 5 to show the effectiveness of our proposed controller.
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A Cooperative Path Planning Algorithm for a Multiple Mobile Robot System in a Dynamic Environment

A Cooperative Path Planning Algorithm for a Multiple Mobile Robot System in a Dynamic Environment

In the cooperative analytical path planning algorithm, the analytical path planning method is combined with the distributed priority scheme to obtain a new multiple mobile robot path planning method that can plan sub-optimal obstacle-avoidance paths in real time. In the cooperative analytical path planning algorithm, each robot uses the same planning scheme at each instant. First, the initial and goal conditions should be verified; then, the path from the current time position to the goal position is determined and each robot will move along its planned path at this time. At the next time interval, the boundary condition is modified and a new trajectory that can also satisfy the obstacle-avoidance criterion is planned. The flow of the cooperative analytical path planning algorithm for MMRS can be illustrated by the i th robot
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A Mobile Robot Path Planning Based on Multiple Destination points

A Mobile Robot Path Planning Based on Multiple Destination points

Abstract. Path planning is an important aspect of mobile robot. This paper mainly studies the global path planning with multiple sub-path targets. Firstly, this paper build two initial distance matrix, then construct a minimum distance matrix by Dijkstra algorithm. Finally, the paper determine the destination nodes which have all minimum path. The path planning method can be used to some service robots, including: family service robots, medical robots, industrial robots.

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