Abstract. In order to solve the problem of global pathplanning for mobilerobot, a global optimization algorithm based on genetic bee colony algorithm is proposed. This algorithm is combining the global optimization strategy of the genetic algorithm and artificial bee colony algorithm. The improved crossover and mutation operator are introduced into the algorithm to increase the diversity of food source, effectively avoiding the swarm into local optimum, improving the ability of search for the food source. Besides, a strategy of adaptive selection has ability to search the optimal food source. A large number of the experiment and comparative analysis are carried out by using grid method in this paper. Experimental results show that this method has high precision and fast convergence speed, and it is an effective method for pathplanning.
A* path-finding search algorithm is very famous in games for finding shortest distance between two nodes. Today’s games have thousands of agents moving at a same time in the presence of obstacles. Thus it has become very important to find shortest paths concurrently and in a speedy way. Making use of Mobilerobot system nature suits this scenario perfectly. Implementing Simple A* algorithm using arrays (Parallel A*) has approximately the same results as compared to A* implementation using adjacent lists. Both implementations are greedy for space. Increase in the size of map increases the memory requirements and thus decreases the speed of algorithm. To further increase the overall performance of algorithm, the memory requirements must be reduced. One option is to use the fast, read-only constant memory for storing the map. Pre-computing some paths and then sharing this already computed information with other agents further increases the efficiency. Another solution to this problem is to exploit the parallel hardware architecture in a true sense. Some improvements are made in the basic A* algorithm to calculate each path using multiple threads that run concurrently and use
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 obstacle detection. 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.
A modification for the functions of potential field method can solve the problem of local minima and enable mobilerobot to navigate in the environment from start point to the goal point while avoiding obstacles. The scaling parameters of the attractive and repulsive potential functions (ζ and ƞ) determine the relative intensity of the attractive force and the repulsive force, so an optimization for these two parameters by using Particle Swarm Optimization (PSO) method gives the optimal motion and grantee fast and smooth robotpath. The simulation in matlab program shows that modified-optimized potential field method enables mobilerobot to navigate in environment from start point to the target with less time and short path. Actually in the future work the potential field method need to combine with an intelligent technique (for example Fuzzy logic) to produce a robust method for pathplanning of mobilerobot to navigate in complex and dynamic environment.
However, all of the above inter and intra-row mechanical weeding robots needed the crops to be well distributed. Selective weeding could provide solutions for crops regardless of distribution, which is more close to how human kill weed. In addition, it was reported that a selective weeding strategy could decrease the energy input even more by 10 to 90 % (Griepentrog et al., 2006). Midtiby et al. (2011) developed a real-time machine vision based micro-spraying weed control system, but only 37% of the smaller scentless mayweed were effectively controlled. After that, Underwood et al. (2015) presented a micro-dot targeted and steerable spraying robot for weeding control. However, selective spraying still relies on herbicides application, which is not allowed for organic farming. Due to the unsatisfactory aspects, other weeding techniques such as laser weeding seems to offer a potential alternative (Marx et al., 2012). Some researchers, such as Heisel et al. (2001), used a laser beam to cut weed stems for weed control. Another laser weeding application is to use laser irradiation. Marx et al. (2012) proposed a weed damage model for laser irradiation based weed control. Mathiassen et al. (2006) investigated the effects of laser treatment towards weeding. The results indicated that laser exposure time and laser spot size could be optimised to kill the weeds. Nadimi et al. (2009) designed a laser weeding test setup for simulate dynamic targeting weeding. However, none of the above researcher have successfully designed a whole mobilerobot for the purpose of laser weeding.
Abstract: In the present generation, surroundings molding has become a primary task in the navigation of mobile robots. It is very important for an autonomous robot to automatically build an abstract over surroundings as it is not possible for human intervention all the time. A robot must work on both the localization and mapping to adapt itself to the surroundings. In this work, a surrounding molded algorithm has been developed which purely based on the Extended Kalman Filter (EKF). The mapping and localization issue, called Simultaneous Localization and Mapping (SLAM), is an operation of developing an abstract map on the surroundings to identify the robot coordinates. The SLAM algorithm mainly begins with a mobilerobot in an undetermined position without previous knowledge of the surroundings on the map.
with a presumed knowledge about the distance from the current state to both the next state and the goal. This knowledge is efficiently used to update the entries in the Q-table once only by utilizing four derived properties of the Q-learning, instead of repeatedly updating them like the classical Q-learning. Naturally, the proposed algorithm has an insignificantly small time- complexity in comparison to its classical counterpart. Further, the proposed algorithm stores the Q-value for the best possible action at a state, and thus saves significant storage. Experiments undertaken on simulated maze and real platforms confirm that the Q-table obtained by the proposed Q-learning when used for path-planning application of mobile robots outperforms both the classical and extended Q-learning with respect to three metrics: traversal time, number of states traversed, and 90 o turns required. Reduction in 90 o turnings minimizes the energy consumption, and thus has importance in robotics literature.
In the experiment, the initial scan was done using a speed of 20cm/s forward and rotational speed of 20cm/s, this is considered an average speed, as the robot is capable of 50cm/s forward and rotational speeds. The speed of the robot in the course of the road is appropriately slow, it ensures that the environmental data is fully analyzed and the environmental maps are established. As shown in Fig. 9, the surrounding dark area is Kinect has not yet obtained the environmental information, the blue-green area is the obstacle obtained by the expansion operation during the movement, the black part is known as an obstacle after detecting and recognizing. If a dynamic obstacle is encountered (such as a dynamic pedestrian or man-made obstacle) during the course of the process, an optimal line is re-planed and the robot smoothly bypasses the obstacle to the target point.
out by researchers in this field. Each of these researchers has tried to get an optimal response to this problem by choosing the right method. This issue, especially on a large scale, has many complexities that cannot be solved by conventional optimization methods. For this reason, the use of intelligent algorithms for solving this problem has been of great interest to the researchers.
Usually, the planning involves an action policy to reach a desired goal state, through maximization of a value function -, which designates sub-objectives and helps choosing the best path. For instance, the value function could be the shortest path, the path with the shortest time, the safest path, or any combination of different sub-objectives. The definition of a task in this class may contain, besides the value function, some a priori knowledge about the domain, e.g., environmental map, environmental dynamics, goal position. Such knowledge allows a robotplanning, while the lack of such knowledge obliges the robot either to learn it previously or to make use of heuristic strategies, such as moving to goal direction while avoiding obstacles. Our research applies reinforcement learning techniques to real- world robots. Reinforcement learning has been tested in many simulated environments -  but on a limited basis in real- world scenarios. A real-world environment poses more challenges than a simulated environment, such as enlarged state spaces , increased computational complexity, significant safety issues (a real robot can cause real damage), and longer turnaround times for results. This research measures how well reinforcement-learning technique, such as Q-learning, can apply to the real robot for navigational problem, and in our research we modified the classical Q-learning algorithm, hereafter called improved Q-learning for increasing its performance one in the path-planning problem.
Many researches have investigated, during the last decades, the guidance of land autonomous vehicles, underseas robots, manipulators and walking machines. Many experiments have been carried out on real robots (wheeled mobile robots, and AUV) and on simulated ones , . A real-time obstacle avoidance algorithm coupled with path following is studied and implemented in this paper.
Obstacle detection algorithm was integrated with the pathplanningalgorithm hence it has been used to modify the direction of the mobilerobot based on obstacles detection in its path. It executes this algorithm to search for the obstacles, if any obstacle is found on the mobilerobotpath, the old path will be replaced with a new one to avoid that obstacle as shown if Figure (5) and Figure (6) where Figure (5) shows the original path before executing the obstacle detection algorithm while Figure (6) shows the modified path depending on the sensor readings. In Figure (6) the blue squares represent the obstacles that are not included in the map of the environment. For this reason it is important to integrate the pathplanningalgorithm with the obstacle detection algorithm.
Dijkstra algorithm is widely used in finding the shortest path from a starting point to other vertices [12-14] . The advantage of the algorithm lies in finding out the vertex closest to the starting point, automatically taking it as the new starting point (knee point), and updating the original path information simultaneously. In this paper, an improved Dijkstra algorithm  can find out the minimum distance matrix between two points. The method to solve this problem can be divided into three steps. The main computation procedure of Dijkstra algorithm is described as follows.
This constrained problem is highly complex and cannot be tracked analytically. Here, we use GA to optimize the parameters of the trajectory optimization (16), i.e. the objective polynomial’s coefficients (λ1, λ2). An outline of GA algorithm is as below . A random initial population is created. For each individual of the current population, a fitness value is assigned. A selection method is used to selects individual with better fitness values, called parents. Childs of these parents are produced either by mutation or crossover operators. Current population is replaced with the children. The procedure runs until a termination criterion is reached.
The development of mobilerobot technology shows the scientific and technological levels of a country to a cer- tain extent. Although the research of mobilerobot in China has made a lot of achievements in recent years, there are still many technical problems. Aiming at the problems of traditional pathplanning methods for mo- bile robots, Kalman filter is used to fuse data and realize localization, which makes it possible for mobile robots to use this localization method to obtain higher localization accuracy. In order to better realize the pathplanning of mobilerobot, the traditional artificial potential field method is introduced, and an improved dynamic arti- ficial potential field method is proposed to overcome the shortcomings of the traditional artificial field potential method. In order to verify the proposed algorithm, the pathplanning effect of tracked mobilerobot in static com- plex environment is tested by simulating in the laboratory of 4.5 m × 3.5 m. The simulation results show that the im- proved pathplanningalgorithm can make the mobilerobot avoid obstacles smoothly in complex and narrow space and realize the optimalpathplanning to achieve the set goal. Due to the limited experimental environment,
The pathplanning is the basic element of the mobilerobot. The so-called mobilerobotpathplanning is searching for an optimal or sub-optimal collision-free path connecting the initial position and target position. According to the environmental information that collected by the robot, the pathplanning can be divided into two categories: pathplanning with fully known global environmental information and completely unknown or partially unknown environmental information . The common methods that have been used, such as grid method and artificial potential field method and so on, but all these algorithms have limitations. The shortcomings of the grid method are when the space increases, it will lead to dramatic increase in storage space that required, and decision is made slowly. The structure of artificial potential field is simple, convenient for real-time control, but may generate minimal path and oscillation in front of obstacles.
into the RPRP_ACO algorithm to further optimize the local navigation path, resulting in the RPRP_HYBRID algorithm. In the process of local pathplanning, the information of the robot’s view and the target point information are fully utilized, in which the traditional sub-target point mapping process is omitted and the bionic algorithm’s self-organization and adaptability in pathplanning are presented. These two algorithms have the advantages of being simple but effective, being robust and generating optimal paths, facilitating robotpathplanning in an unknown environment. In the future, the avoidance and retreat strategies in the robotpath rolling planning based on the navigation idea in this paper will be studied. We are particularly interested in the situations when the obstacles in the unknown environment are large, especially the size of robot’s view is smaller than the U-shaped trap, and when dynamic obstacles exist in the working environment. Also, we are interested in comparing different algorithms according to the different view sizes of the robot, and exploring hybrid algorithms that have better effectiveness and efficiency.
Abstract: This study presents a pathplanning method for a mobilerobot to be effectively operated through a multi-objective decision making problem. Specifically, t he p roposed F uzzy analytic hierarchy process (FAHP) determines an optimal position as a sub-goal within the multi-objective boundary. The key features of the proposed FAHP is evaluating the candidates according to the fuzzified relative importance among objectives to select an optimal solution. In order to incorporate FAHP into pathplanning, an AHP framework is defined, which includes the highest level (goal), middle level (objectives), and the lowest level (alternatives). The distance to the target, robot’s rotation, and safety against collision between obstacles are considered as objective functions. Comparative results obtained from the artificial potential field and AHP/FAHP simulations show that FAHP is much preferable for mobile robot’s pathplanning than typical AHP.