Obstacle Avoidance Planning Research Articles

Localization, obstacle avoidance planning and control of a cooperative cable parallel robot for multiple mobile cranes

This paper addresses the cooperative problems in terms of localization, obstacle avoidance planning and automatic leveling control for a cable parallel robot for multiple mobile cranes (CPRMCs). The design model of the CPRMCs is elaborated on. The three-dimensional grid map method is utilized to plot the environment map based on the operation environment model. By combining the relative localization method with the absolute localization method, a cooperative localization scheme of the CPRMCs is developed, and an improved localization algorithm is designed on the basis of multilateration method. Then, according to the grid-based artificial potential field method, a global path planning of the CPRMCs is performed. Considering the possible collision of the single mobile crane, the sensor technology is applied to the cooperative obstacle avoidance. In addition, a four-point collaborative leveling method is adopted for automatic leveling control of the platform of the CPRMCs. Finally, the effectiveness of the CPRMCs system is verified through simulations. Cable parallel robots for multiple mobile cranes are designed.Localization analysis of the robots are reported.Obstacle avoidance planning and automatic leveling control are performed.Illustrative simulation studies highlight the performance of the robots.

Distance Measurement Algorithm Based on Binocular Stereo Vision

Distance measurement technology of binocular stereo vision has the advantages of wide-range detection, simplicity and reliability. The method is widely applied to robot obstacle avoidance and path planning. Binocular stereo vision can only measure the distance of images feature points generally. However, more information about distance of non-feature points is also needed to acquire in practical applications. This paper proposes a stereo distance measurement method, which can measure distance of points whether it is a feature point or not based on a dense matching method. A dense parallax map is obtained by the graph-cuts algorithm. On the basis of the calibration parameters of binocular camera and the left and right image dense parallax map, the three-dimensional coordinates of the any points and their distance will be gotten. The true image experiment has proved the feasibility of this algorithm with high accuracy and maneuverability.

Cooperative Obstacle Avoidance of Multiple Robotic Fishes Based on Grids Method

To resolve the problem of obstacle avoidance and path planning of multiple robotic fishes, this paper proposes a new approach which uses collaboration mechanism based on grids method. The proposed approach splits the workspace of those robotic fishes using grids method, identifies each grid with serial number, designs the cooperative mechanism to avoid collision among these fishes, and plans the path for each fish. This method was applied in the obstacle avoidance competition of multiple robotic fishes which happening in a field with obstacle in it. The experimental results show the new method is more effective, can get more optimal path, and avoid the local minima issue which arises frequently in the A-star algorithm and genetic algorithm. It significantly improves the ability of the multiple robotic fishes system on the aspect of path planning and coordination.

Unified Trajectory Planning Algorithms for Autonomous Underwater Vehicle Navigation

This paper presents two efficient methods for obstacle avoidance and path planning for Autonomous Underwater Vehicle (AUV). These methods take into account the dynamic constraints of the vehicle using advanced simulator of AUV considering low level control and stability effects. We present modified visibility graph local avoidance method and a spiral algorithm for obstacle avoidance. The algorithms were tested in challenged scenarios demonstrating safe trajectory planning.

808 重み付き環境地図に基づくポテンシャル法を用いたUAVの障害物回避経路生成

808 重み付き環境地図に基づくポテンシャル法を用いたUAVの障害物回避経路生成

Motion Path Control of Multi-DOF Handling Manipulator in Industrial Field Based on Vision and C-Space Obstacle Avoidance

In order to realize the intelligentization of the multi-DOF handling manipulator in the industrial field, this paper presents a motion path control approach which combines the vision based identification and localization and C-space (Configuration space) based obstacle avoidance planning. The objects in the image captured by CCD camera can be extracted by gray processing, background subtraction, binarization, and edge detecting, the work pieces and the obstacles can be matched based on roundness and squareness, and then the coordinates of above objects can be obtained by parallax and coordinate conversion. In addition, both the source and destination coordinates in C-space can be obtained by mapping these configurations position and orientation into C-space, and the motion path can be automatically generated by variable step A* search in free area which is built based on solid manipulator model, then the handling work can be finished and obstacle avoidance can also be realized. The results of simulation and experiment demonstrate the scientificalness and effectiveness of this control approach.

Design and Fabrication of an Autonomous Mobile Robot for Obstacle Avoidance and Path Planning in Partially Known Environment

In this paper, an autonomous mobile robot has been designed and fabricated which can be used in both indoor and outdoor for industrial and household applications. Here using six servo motors and four DC motors with their controllers (servo controller and L293D DC Motor controller) the mobile robot can pick any object from its workspace and by avoiding collision it can place the object in the desired location. ASCII ultrasonic sensor and motion sensor are used along with ATmega 2560 microcontroller which is programmed to take the sensors output as its input and controls the dc motor and servo motors to pick and place objects and avoid obstacle during motion of the mobile robot. Here low-cost solar panels have been used to recharge the Li-ion batteries used for the motors and microcontroller in case of outdoor environment. The obstacle avoidance and path planning algorithms have been developed and a case study has been presented in this paper.

Implementing Autonomous Navigation of a Mobile Robot Integrating Localization, Obstacle Avoidance and Path Planning

This paper presents an implementation of autonomous navigation of a mobile robot indoors. It explains methods for map building, localization, obstacle avoidance and path planning. Geometric map is used for localization and path planning. The localization method calculates sensor data based on the map for comparison with the real sensor data. Monte Carlo Localization(MCL) method is adopted for estimation of the robot position. For obstacle avoidance, an artificial potential field generates repulsive and attractive force to the robot. Dijkstra algorithm plans the shortest distance path from a start position to a goal point. The methods integrate into autonomous navigation method and implemented for indoor navigation. The experiments show that the proposed method works well for safe autonomous navigation.

Real-Time Computation of Spatial and Flat Obstacle Avoidance Trajectories for UUVs

This paper elaborates on the previous paper (Horner and Yakimenko, 2007) and develops a framework for planning obstacle avoidance trajectories in real-time. The proposed obstacle avoidance algorithm relies on the relevant situational awareness data provided by forward-looking sonar. Based on this information, current objectives, UUV attitude, vehicle agility and autopilot performance, an onboard computer chooses between avoiding newly discovered obstacles in the vertical plane, horizontal plane, or performing a spatial three-dimensional manoeuvre. Once information about an obstacle, which can be moving, is updated the obstacle-avoiding trajectory should be updated as well. Therefore, an UUV follows the originally determined trajectory only between two consecutive updates. One of the key features of the proposed approach is that a new trajectory should start from the current conditions defined by position, velocity and acceleration. Having the rate of situational awareness data update as fast as 1Hz, puts another severe constraint on the computational effectiveness of the path generation algorithm. These features rule out the usage of all but one known approaches. The advocated approach is the direct method of calculus of variations based on inverse dynamics in the virtual domain. The paper presents a detailed derivation of computational routine and demonstrates the results of some preliminary simulations.