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Optimal Path Planner for Mobile Robot in 2D Environment
Valeri Kroumov,Jianli Yu,Hiroshi Negishi
Journal of Systemics, Cybernetics and Informatics , 2004,
Abstract: The problem of path planning for the case of a mobile robot moving in an environment filled with obstacles with known shapes and positions is studied. A path planner based on the genetic algorithm approach, which generates optimal in length path is proposed. The population member paths are generated by another algorithm, which uses for description of the obstacles an artificial annealing neural network and is based on potential field approach. The resulting path is piecewise linear with changing directions at the corners of the obstacles. Because of this feature, the inverse kinematics problems in controlling differential drive robots are simply solved: to drive the robot to some goal pose (x, y, theta), the robot can be spun in place until it is aimed at (x, y), then driven forward until it is at (x, y), and then spun in place until the required goal orientation
A Parallel Randomized Path Planner for Robot Navigation
S. Sengupta
International Journal of Advanced Robotic Systems , 2008,
Abstract: Computation of a collision-free path for a movable object among obstacles is an important problem in the fields of robotics, CIM and AI. Various automatic task level programming systems can be build for robot guidance, teleoperation, assembly and disassembly among others, if a suitable method for motion planning is available. In the basic variation of motion planning, the task is to generate a collision-free path for a movable object among known and static obstacles. Classically the problem was defined for a rigid 6 degrees-of-freedom body as 'the piano mover's problem'. However, the majority of the research has been conducted in the field of robotics, often under the title of path planning. Rapidly-Exploring Random Trees (RRTs) are a recently developed representation on which fast continuous domain path planners can be based. In this work, we have built a parallel path planning system based on RRTs that interleaves planning and execution, first evaluating it in simulation and then applying it to physical robots. Our distributed algorithm, PRRT (parallel RRT), introduces a parallel extension of previous RRT work, the process splitting and parallel cost penalty search with a comment on Real Time Stagnancy reduction, which improves re-planning efficiency, decreases latency involved in finding feasible paths and the quality of generated paths. PRRT is successfully applied to a real-time multi-robot system. In this paper we illustrate how it is possible to implement a parallel version of RRT based motion planner which yields optimal speed up.
LP-based Path Planning Method in Acceleration Space for Mobile Robot

ZU Di,HAN Jian-Da,TAN Da-Long,

自动化学报 , 2007,
Abstract: A linear programming(LP)based method in the acceleration space is proposed for the path planning of the mobile robot in the dynamic and uncertain environment.By using the relative information,the path planning of the mobile robot in the dynamic environment is described as minimizing an objective function subject to a set of linear inequalities that are easily embedded into the LP path planner.Simulations are carried out and the results show that the proposed method is more efficient and more convergent than the artificial potential guided evolution algorithm(APEA).
Trajectory Planner for Mobile Robot with Avoidance of Obstacles Based on the Constraints Method and Heuristic Rules
Guesbaya Tahar,Benmahammed Khier,Benali Abderraouf
Asian Journal of Information Technology , 2012,
Abstract: In this study, the aim is to present our contribution for the local planning of trajectory for nonholonomic mobile robot with avoidance of obstacles. This contribution is based on the constraintsmethod and some heuristics rules. The constraint method put some constraints on the permitted velocities of the mobile robot and obliges it to move away from the obstacles. The mobile robot possesses ultrasonic sensors to measure the minimal distance into the obstacles. Modeling the obstacles and sensors by convex polygons makes use of calculation methods of minimal distance easy. The simulation results show the suppleness with the mobile robot reach the target going across difficult path.
Path Planning and Trajectory Control of Collaborative Mobile Robots Using Hybrid Control Architecture
Trevor Davies,Amor Jnifene
Journal of Systemics, Cybernetics and Informatics , 2008,
Abstract: This paper presents the development and implementation a hybrid control architecture to direct a collective of three X80 mobile robots to multiple user-defined waypoints. The Genetic Algorithm Path Planner created an optimized, reduction in the time to complete the task, path plan for each robot in the collective such that each waypoint was visited once without colliding with a priori obstacles. The deliberative Genetic Algorithm Path Planner was then coupled with a reactive Potential Field Trajectory Planner and kinematic based controller to create a hybrid control architecture allowing the mobile robot to navigate between multiple user-defined waypoints, while avoiding a priori obstacles and obstacles detected using the robots' range sensors. The success of this hybrid control architecture was proven through simulation and experimentation using three of Dr. Robot's wireless X80 mobile robots.
Mobile Robot Path Planning with Randomly Moving Obstacles and Goal  [cached]
Vanitha Aenugu,Peng-Yung Woo
International Journal of Intelligent Systems and Applications , 2012,
Abstract: This article presents the dynamic path planning for a mobile robot to track a randomly moving goal with avoidance of multiple randomly moving obstacles. The main feature of the developed scheme is its capability of dealing with the situation that the paths of both the goal and the obstacles are unknown a priori to the mobile robot. A new mathematical approach that is based on the concepts of 3-D geometry is proposed to generate the path of the mobile robot. The mobile robot decides its path in real time to avoid the randomly moving obstacles and to track the randomly moving goal. The developed scheme results in faster decision-making for successful goal tracking. 3-D simulations using MATLAB validate the developed scheme.
A Neural Networks-based Approach to Safe Path Planning of Mobile Robot in Unknown Environment

FAN Chang-Hong,CHEN Wei-Dong,XI Yu-Geng,

自动化学报 , 2004,
Abstract: For safe path planning of mobile robot in unknown environment, the paper uses a local connected Hopfield neural network (HNN) planner. The stability of the HNN is analyzed, and the condition for the existence of the feasible path(s) is given. If a feasible path(s) exists, the HNN does not have any unexpected local attractive point. The connected weight design considers both "too close" and "too far" to plan the safe path. For the HNN to on-line plan a path on a sequential processor, multi-sequential Gauss-Seidel iteration is used to accelerate the propagation of the HNN potential field. Results demonstrate that the method has good real-time ability and adaptability to environments.
Path Tracking Control of a Mobile Robot by Using Dual Estimation Algorithm  [PDF]
Xuan Vinh Ha,Cheolkeun Ha,Hyoung Sik Choi
Advances in Mechanical Engineering , 2013, DOI: 10.1155/2013/367127
Abstract: Skid-steered mobile robots have been developed to explore unknown environments, especially in rough terrain situations, where the wheel slips always occur and vary when the robot is traveling with different trajectory shapes. The wheel slippage limits the traction and braking abilities of the robot. In this paper, we propose a new dual estimation algorithm to overcome the previous limitations. The estimated values such as the robot’s positions and wheel slips are obtained based on the Kalman filtering technique. As demonstrated by our experimental results, the advantages of the new method are effective to overcome the slip limitations in the path tracking control problem of a four-track wheel skid-steered mobile robot (4-TW SSMR). 1. Introduction Recently, several studies have proposed and developed various approaches for estimating the position of a mobile robot. For example, one study used an extended Kalman filter (EKF) with a low-cost GPS unit and inclinometer to develop a localization scheme for Ackerman steering vehicles in indoor autonomous navigation situations by estimating the positions of the vehicles and their sensor biases [1]. In another study, a relative localization method was used to determine the navigational route of a convoy of robotic units in an indoor environment using an EKF based on a low-cost laser range system and built-in odometric sensors [2]. In skid-steered vehicles, discrepancies remain between position measurements and the navigational estimations obtained from odometry, infrared, and ultrasonic measurements and Kalman filtering techniques [3]. However, this study only mentioned the advantages of the different extensions of the KF for nonlinear estimations. In [4], a laser range finder (LRF) was used to find the localization of a mobile robot based on the EKF design. That study focused on a statistically exact derivation of the input noise covariance matrix, according to the known noise variances of the angular velocity measurements of both robot wheels. Additionally, the exploration of unknown planetary surfaces with the help of mobile robot is increasingly being developed. A localization and path selection of any vehicle like a mobile robot to achieve any planetary exploration missions were described in [5]. In this study, by using odometry and gyro measurements, these sensors information was integrated by EKF. In another research, a robot, named Hyperion, was designed for solar-powered operation in polar environments and developed sun-cognizant navigation method to enable rovers to dodge shadows, seek sun, and drive
3D Maps Registration and Path Planning for Autonomous Robot Navigation  [PDF]
Konstantinos Charalampous,Ioannis Kostavelis,Dimitrios Chrysostomou,Angelos Amanatiadis,Antonios Gasteratos
Computer Science , 2013,
Abstract: Mobile robots dedicated in security tasks should be capable of clearly perceiving their environment to competently navigate within cluttered areas, so as to accomplish their assigned mission. The paper in hand describes such an autonomous agent designed to deploy competently in hazardous environments equipped with a laser scanner sensor. During the robot's motion, consecutive scans are obtained to produce dense 3D maps of the area. A 3D point cloud registration technique is exploited to merge the successively created maps during the robot's motion followed by an ICP refinement step. The reconstructed 3D area is then top-down projected with great resolution, to be fed in a path planning algorithm suitable to trace obstacle-free trajectories in the explored area. The main characteristic of the path planner is that the robot's embodiment is considered for producing detailed and safe trajectories of $1$ $cm$ resolution. The proposed method has been evaluated with our mobile robot in several outdoor scenarios revealing remarkable performance.
GA-based Global Path Planning for Mobile Robot Employing A* Algorithm  [cached]
Cen Zeng,Qiang Zhang,Xiaopeng Wei
Journal of Computers , 2012, DOI: 10.4304/jcp.7.2.470-474
Abstract: Global path planning for mobile robot using genetic algorithm and A* algorithm is investigated in this paper. The proposed algorithm includes three steps: the MAKLINK graph theory is adopted to establish the free space model of mobile robots firstly, then Dijkstra algorithm is utilized for finding a feasible collision-free path, finally the global optimal path of mobile robots is obtained based on the hybrid algorithm of A* algorithm and genetic algorithm. Experimental results indicate that the proposed algorithm has better performance than Dijkstra algorithm in term of both solution quality and computational time, and thus it is a viable approach to mobile robot global path planning.
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