The aim of the research was analyzing and implementing algorithms for a mobilerobot to has the ability to follow an optimum path when move from its position to a specific target position when put it a known environment. To achieve this aim, there are three algorithms have been built, tested and evaluated, pathplanning, motioncontrol and obstacledetection algorithms. In addition to building these algorithms, there are a set of calibration steps were done. The first calibration was done for odometry. This calibration has two main calibration types; the first test was to find the linear odometry error while the second test was to find the angular odometry error. The second odometry was for the sensor that will be used to get the environment readings.
Mobile robots feature some kind of damage avoidance by avoiding collision, ranging from basic algorithms that detect an blocks to avoid a collision, using some basic program code, that initialize the robot to detect blocks. The program code is easy, since it is involved simple obstacledetection as well as some kind of obstacle distance measurement to avoid collision. Once block is determined, the obstacle avoidance program needs to direct the robot around the block and halt motion and direct robot to desired direction. In this paper the redirecting algorithm makes the robot does not have to stop in front the block and find its path. Hence the robots may overcome problems during path finding, it can direct robot during its operation avoiding the bumping with walls.
Abstract: Pathplanning and real-time obstacle avoidance is the key technologies of mobilerobot intelligence. But the efficiency of the global pathplanning is not very high. It is not easy to avoid obstacles in real time. Aiming at these shortcomings it is proposed that a global dynamic pathplanning method based on improved A* algorithm and dynamic window method. At first the improved A* algorithm is put forward based on the traditional A* algorithm in the paper. Its optimized heuris- tic search function is designed. They can be eliminated that the redundant path points and unnecessary turning points. Simulation experiment 1 results show that the planned path length is reduced greatly. And the path transition points are less, too. And then it is focused on the global dynamic pathplanning of fusion improved A* Algorithm and Dynamic Window Method. The evaluation function is constructed taking into account the global optimal path. The real time dynamic path is planning. On the basis of ensuring the optimal global optimization of the planningpath, it is improved that the smoothness of the planningpath and the local real-time obstacle avoidance ability. The simulation experiments results show that the fusion algorithm is not only the shorter length, but also the smoother path compared the traditional pathplanning algorithms with the fusion algorithm in the paper. It is more fit to the dynamics of the robotcontrol. And when a dynamic obstacle is added, the new path can be gained. The barrier can be bypass and the robot is to reach the target point. It can be guaranteed the global optimality of the path. Finally the Turtlebot mobilerobot was used to experiment. The experimental results show that the global optimality of the proposed path can be guaranteed by the fusion algorithm. And the planned global path is smoother. When the random dynamic obstacle occurs in the experiment, the robot can be real-time dynamic obstacle avoidance. It can re-plan the path. It can bypass the random obstacle to reach the original target point. The outputting control parameters are more conducive to the robot’s automatic control. The fusion method is used for global dynamic pathplanning of mobile robots in this paper. In summary the experimental results show that the method is good efficiency and real-time performance. It has great reference value for the dynamic pathplanning application of mobilerobot.
The artificial potential field method is proposed by Dr. Khatib in 1985. It is originally proposed to enable the manipulator to have obstacle avoidance function. Artifi- cial potential field method (APF) is widely used in robotpathplanning because of its advantages such as less computation, simple structure, and easy control at the bottom. Artificial potential field method is to introduce the concept of physical field into the expression of plan- ning environment. Its essential idea is to describe the spatial structure through the numerical function of arti- ficial potential field, and the force traction robot in the potential field achieves the goal. Using the potential field method to study the pathplanning of mobilerobot is to regard the robot as a point under the influence of the field potential. The robot moves along the field like a ball rolling down a mountain. In the presence of poten- tial, obstacles represent repulsion, and the smallest value in space is gravity. In the configuration space, the robot is regarded as a point. Under the traction of the artificial field potential, the robot can avoid obstacles in the path and move smoothly to the target point.
This work is part of the relatively new interest in interaction between Autonomous Mobile Robots. Such systems bring in the problems of both multiple Robot coordination and autonomous navigation. However, due to the complexity in developing a complete system, this paper will focus on a specific task: Preference – Based Fuzzy Behavior Control System and Dynamic MotionPlanning for obstacle avoidance. This task appears to be very difficult when an environment also affect the motion of MobileRobot. Since the cooperation in the system will be based upon the interchange of information relating to position and force sensing of the Mobile Robots, the operative systems must integrate several characteristics to make this communication easier, besides, the force feedback must come from a reliable source, so the sensor should be robust enough to provide this information.
Pathplanning has got a lot of contributions from various researchers. Some of the several approaches for pathplanning are that of wall-following method [3, 4, and 5], edge detection method [6 and 7], hit and leave point approach , Histogramic in motion mapping approach  etc. In case of wall following method the robot starts from an initial position and then moves towards the nearest point along the wall and keeps on moving along it until it reaches the goal point. In case if an obstacle comes in the path of the robot it considers the sides of the obstacle as a wall and thus moves along it until it resumes to the original course in order to finally reach the goal point. In some other techniques edge detection mechanism is used. In this the robot takes in to consideration the vertical edges of the obstacle, for each edge the line connecting them is considered as the boundary of the robot and the robot tries to move along that boundary. One of the traditional approaches is that of hit and leave points using ultrasonic sensors in which as soon as an obstacle comes in front of the robot, it considers two points, one between itself and the obstacle (Hit point „H‟) and the other on the other side of the obstacle (Leave point „L‟) in such a way that line HL is in direction of the path from the source to the destination. Thus the robot starts from the hit point, moves along the obstacle while maintaining a safe distance, till it reaches the leave point.
Robotpathplanning is about finding a collision free motion from one position to another. Efficient algorithms for solving problems of this type have important applications in areas such as: industrial robotics, computer animation, drug design, and automated surveillance Mobile robots must know where they are and specify a suitable path to the goal to navigate reliably in indoor environments; this is the task of navigation and pathplanning to enable the robot to reach the goal point with optimal path without any knowledge about the environment, by using wireless camera as feedback sensor.
Mobility is an important aspect of modern robots. Sever- al approaches to mobility management of a mobilerobot have been studied over the last four decades. Some of these popular methods for pathplanning include Voronoi Dia- gram, A* Heuristic Algorithm, Neural Networks, Fuzzy algorithms. Since the beginning of this decade, researchers are taking keen interest to study the scope of optimization algorithms in mobility management of robots. The justifica- tion of using optimization techniques arises particularly when the motionplanning of a number of robots are consi- dered together in the same workspace. This paper attempts to overcome one fundamental problem in multi-agent ro- botics.
Abstract—Today’s mobile robots operate in natural industrial, commercial, or military environments and must interact with humans during the decision-making process. The natural environment is highly unstructured and often unknown, hence these robots must be able to process a large amount of information, and make planning and navigational decisions quickly. Autonomous mobilerobot have additional processing requirements where they have to collect, plan, and execute their planned route as well as performing tasks relevant to their specific missions. The pathplanning task is usually one of many important tasks that these robots must accomplish. Autonomous mobile applications require efficient and adaptive pathplanning approaches. A number of heuristic algorithms exist for producing optimal traverses given changing arc costs. But, most of analytical algorithms are focused on the path finding procedure in a known environment and leave higher level functions, such as obstacledetection. The Cognitive Based Adaptive pathplanning (CBAPP) is an adaptive and cognitive based thinking system. And efficiency of the CBAPP was compared with analytical and a heuristic based algorithm results. Based on the simulation results, it was shown that CBAPP finds fast, efficient, and acceptable paths.
There are several different approaches for planning the path of mobilerobot in unknown environments with sta- tic and dynamic obstacles ( Julia et al. 2012 ), other works determine feasible paths from the reachability analysis and topological constraints ( Aoude et al. 2013 ; Bhattacharya et al. 2012 ) and some visual-guided techniques are currently being applied to aerial vehicles ( Yu and Beard 2013 ). In literature the pathplanning for cable-driven robot followed multiple courses such as: similar algorithms follow the bug-based principles, where a path is calculated in two modes: the robot moves toward the goal on a straight line and when it finds an obstacle, the robot navigates in a near-mode ( Lahouar et al. 2009 ). In other works ( Trevisani 2010 ; Mourad Ismail 2013 ), the navigation procedure takes into account the effects of high accelerations during the motion of the cable robot, and formulates a dynamic controlling principle which maintains cable tensions in a well-defined interval. More recently, M. et al. ( 2014 ) uses optimal feedback linearization approach and the work of Gagliardini and Gouttefarde ( 2015 ) uses Cable-Driven Parallel Robots with reconfigurable exiting points that can be displaced into different locations. The opti- mal pathplanning is computed, by selecting the order of the points which minimizes the length of the path.
presented. An omnidirectional mobilerobot is a type of holonomic robots. It can move simultane- ously and independently in translation and rotation. The RoboCup small-size league, a robotic soc- cer competition, is chosen as the research platform in this paper. The first part of this research is to design and implement an embedded system that can communicate with a remote server using a wireless link, and execute received commands. Second, a fuzzy-tuned proportional–integral (PI) path planner and a related low-level controller are proposed to attain optimal input for driving a linear discrete dynamic model of the omnidirectional mobilerobot. To fit the planning requirements and avoid slippage, velocity, and acceleration filters are also employed. In particular, low-level optimal controllers, such as a linear quadratic regulator (LQR) for multiple-input-multiple-output acceleration and deceleration of velocity are investigated, where an LQR controller is running on the robot with feedback from motor encoders or sensors. Simultaneously, a fuzzy adaptive PI is used as a high- level controller for position monitoring, where an appropriate vision system is used as a source of position feedback. A key contribution presented in this research is an improvement in the combined fuzzy-PI LQR controller over a traditional PI controller. Moreover, the efficiency of the proposed approach and PI controller are also discussed. Simulation and experimental evaluations are conducted with and without external disturbance. An optimal result to decrease the variances between the target trajectory and the actual output is delivered by the onboard regulator controller in this paper. The modeling and experimental results confirm the claim that utilizing the new approach in trajectory-planning controllers results in more precise motion of four-wheeled omnidirectional mobile robots.
Abstract— In this paper, pathplanning which is based on Artificial Potential Field (APF) and the kinematic based control is integrated in order to solve an issue in the APF. Usually, the APF assumes the robot is modeled as a point mass. It means that the robot can move in any direction and neglect the nonholonomic constraint. In order to solve such a problem, the APF should be considered as part of the control system. This research proposed an approach integrating APF and control system under nonholonomic constraint. Naturally, the force of the APF can be used as linear velocity in the control system. Then, waypoint of APF is used as equilibrium point of kinematic control. In order to validate the proposed method, the experimental setup conducted on loop simulation. The scenario is that the robot moves along the certain trajectory to reach the goal point. The obstacle was set in between the robot and the goal point. The initial, goal, and the obstacles are set randomly. The experiments show that the integration of the proposed method can be implemented successfully. The real obstacle avoidance method and fulfilling the nonholonomic constraint are the proof that the method is running well. The results show that the integrated proposed method meets convergent and stable.
In this work DES as well supervisory control theory is utilized to design a motion plan- ner for mobile robotic platforms. To this aim the geographic space (map) is discretized to reduce the computational complexity and the dynamics of the robot are converted in symbolic dynamics using a set of elementary movements (primitives). The primitives are pieces of dynamically-feasible trajectories, that take into account the dynamic of the vehicle. The vehicle is controlled to perform a succession of elementary movements, i.e. the primitives. The single primitives are selected as feasible movements according to the dynamic of the robot, but its interconnection can be kino-dynamically feasible or non-feasible. In the latter case, the theory developed in the previous chapter can be used to derive bounds on the tracking error. The sequence of primitives to be executed is se- lected in real-time by a high-level supervisor that is designed as a discrete-event system . To decide the optimal sequence of primitives, the supervisor considers an optimiza- tion problem in which it takes into account for the current discrete-event model of the environment and of the vehicle. For an introduction to DES and to the tools used in this chapter, the reader can refers to the Appendix section.
1. Inability to plan under uncertainty of dynamic environments: Conventionally, global planners rely on a complete map of the environment in order to calculate the ideal and collision-free path between the starting point and the ending point prior to execution of the robot. The original plans of those conventional algorithms must be revised accordingly if a dynamic environment is encountered (Dijkstra, 1959; Hart et al., 1968). In practise, environment of robots often includes various hazard sources that robots must avoid, for example landmines, fire in rescue duty, and war enemies. Since it is impossible or expensive to acquire their accurate locations, decision- makers know only their action ranges in most cases (Zhang et al., 2013). Mobile robots must be able to evade both static and moving obstacles (Ferguson et al., 2006). Algorithms such as sampling-based methods (Khaksar et al., 2012) are not suitable for online planning when involving moving obstacles, due to the fact that these methods are designed based on a static environment model. These models are time- consuming when applied to a dynamic environment (involving interpolation cycle during each update, see (Huptych & Röck, 2015)). Therefore, classical pathplanning methods such as Visibility Graph (Lozano-Perez, 1987), Voronoi Diagrams (Leven & Sharir, 1987), Grids (Weigl et al., 1993), Cell Decomposition (Regli, 2007), Artificial Potential Field (Khatib, 1985), Rule Based methods (Fujimura, 1991) and Rules Learning techniques (Ibrahim & Fernandes, 2004) are not practical (Mohanty & Parhi, 2013). Occasionally, these algorithms are optimized to handle a specific problem at the expense of sacrificing the performance of other parameters such as increasing of the computational cost of the algorithm.
Khepera II (Fig. 11) is a miniature robot (diameter of 7 cm) equipped with 8 built-in infrared proximity sensors, and 2 relatively accurate encoders for the two motors. The range sensors are positioned at fixed angles and have limited range detection capabilities. The sensors are numbered between 0 and 7 with the leftmost sensor, designated by 0, and the rightmost by 7 (Fig. 12). The robot represents measured range data in the scale: [0, 1023]. When an obstacle is away from the sensor by more than 5cm, it is represented by zero. When an obstacle is approximately 2 cm away, it is represented by 1023. The onboard Microprocessor includes a flash memory of 512 KB, and a Motorola 68331, 25MHz processor. The Khepera model we used is a table-top robot, connected to a workstation through a wired serial link. This configuration allows an optional experimental configuration with everything at hand: the robot, the environment and the host computer.
Reinforcement learning is an alternative learning policy, which rests on the principle reward and punishment. No prior training instances are presumed in reinforcement learning. A learning agent here does an action on the environment, and receives a feedback from the environment based on its action. The feedback provides an immediate reward for the agent. The learning agent here usually adapts its parameter based on the current and cumulative (futuristic) rewards. Since the exact value of the futuristic reward is not known, it is guessed from the knowledge about the robot’s world map. The primary advantage of reinforcement learning lies in its inherent power of automatic learning even in presence of small changes in the world map. Motionplanning is one of the important tasks in intelligent control of a mobilerobot. The problem of motionplanning is often decomposed into pathplanning and trajectory planning. In pathplanning, we need to generate a collision-free path in an environment with obstacles and optimize it with respect to some
The goal agents are agents added to the system in order to treat exceptions that occur in complex goal groups. The goal agent, first, has to detect faults among agents that participate to achieve the complex goal. Failure detection is done using the same technique as the controller. If an agent is considered as a failed one, the action that it has to achieve gives an exception signal. The goal agent verifies the criticality of the action and returns a decision that may ignore the exception or take it in consideration. The exception is handled by the goal agent using the method mentioned in the previous sections. The following figure (Figure2) describes the goal agent activities using Petri Nets.
[11, 12]; the local sub-objective is optimized by using a heuristic function according to the local information in the rolling window obtained by the robot; the behavior dynamics model is used to perform autonomous pathplanning  in the rolling windows; and the planning trajectory of a series of windows is connected end to end to realize the global pathplanning. Behavior dynamics uses point attractor and point repulsor to build robot behavior, and the robot’s heading angle and motion speed are used as behavior variables to describe robots moving in a plane , but in the application, the line speed is limited by the heading angular velocity con- trol and causes a deadlock; in addition, because of the non additivity of the virtual forces, there are also pseudo attractor problems. The literature [15, 16] uses the linear velocity control method to solve this problem, but it is required that the linear velocity ensures that the robot’s heading angle is always near the heading angle of the attractor. At the same time, there is also a situation in which the path between windows in the rolling windows method is not smooth because of the different directions of the robotmotion.
The obstacledetection is an important topic and in this paper we have considered a mobilerobot equipped with infra-red sensors able to measure the distance between the robot and its environment. This paper is organized into five sections. In section 2 the model of the robot and its equipments are presented. In section 3 the mathematical formulation for pathfollowing and obstacle avoidance is described. Finally, section 5 contains the results of the simulation and experimental tests.
system, on the basis of nonlinear control theory, we constructed a control system  for preventing the generation of a fixed point that corresponds to non-oscillatory responses and is unsuitable for dynamic image segmentation. Moreover, we proposed a method  to identify target image regions for a given binary (black and white) image using bifurcation theory. This supports that we know the number of image regions to be segmented before the process of image segmentation.