Survey of Robot 3D Path Planning Algorithms <s> Neural Network. <s> The potential field approach to path planning is computationally intensive but highly parallel. The authors propose a general neural network architecture called the wave expansion neural network (WENN), specifically designed to implement such potential field operations useful for path planning. The authors describe WENN-based neural network systems which implement certain types of artificial potential fields for path planning. The neural network takes the workspace which needs to be navigated as input and generates artificial potential fields which are then subsequently used to find a path. Neural network implementation results in greatly reduced computations. > <s> BIB001 </s> Survey of Robot 3D Path Planning Algorithms <s> Neural Network. <s> Abstract A model of a topologically organized neural network of a Hopfield type with nonlinear analog neurons is shown to be very effective for path planning and obstacle avoidance. This deterministic system can rapidly provide a proper path, from any arbitrary start position to any target position, avoiding both static and moving obstacles of arbitrary shape. The model assumes that an (external) input activates a target neuron, corresponding to the target position, and specifies obstacles in the topologically ordered neural map. The path follows from the neural network dynamics and the neural activity gradient in the topologically ordered map. The analytical results are supported by computer simulations to illustrate the performance of the network. <s> BIB002 </s> Survey of Robot 3D Path Planning Algorithms <s> Neural Network. <s> This paper introduces a new memetic algorithm specialized for the asymmetric instances of the traveling salesman problem (ATSP). The method incorporates a new local search engine and many other features that contribute to its effectiveness, such as: (i) the topological organization of the population as a complete ternary tree with thirteen nodess (ii) the hierarchical organization of the population in overlapping clusters leading to the special selection scheme; (iii) efficient data structures. Computational experiments are conducted on all ATSP instances available in the TSPLIB, and on a set of larger asymmetric instances with known optimal solutions. The comparisons show that the results obtained by our method compare favorably with those obtained by several other algorithms recently proposed for the ATSP. <s> BIB003 </s> Survey of Robot 3D Path Planning Algorithms <s> Neural Network. <s> A near-optimal routing algorithm employing a modified Hopfield neural network (HNN) is presented. Since it uses every piece of information that is available at the peripheral neurons, in addition to the highly correlated information that is available at the local neuron, faster convergence and better route optimality is achieved than with existing algorithms that employ the HNN. Furthermore, all the results are relatively independent of network topology for almost all source-destination pairs. <s> BIB004 </s> Survey of Robot 3D Path Planning Algorithms <s> Neural Network. <s> In this paper, a cooperative sweeping strategy of complete coverage path planning for multiple cleaning robots in a time-varying and unstructured environment is proposed using biologically inspired neural networks. Cleaning tasks require a special kind of trajectory being able to cover every unoccupied area in specified cleaning environments, which is an essential issue for cleaning robots and many other robotic applications. Multiple robots can improve the work capacity, share the cleaning tasks, and reduce the time to complete sweeping tasks. In the proposed model, the dynamics of each neuron in the topologically organized neural network is characterized by a shunting neural equation. Each cleaning robot treats the other robots as moving obstacles. Multiple cleaning robots can cooperate to achieve a common sweeping goal effectively. The robot path is autonomously generated from the dynamic activity landscape of the neural network, the previous robot location and the other robot locations. The proposed model algorithm is computationally efficient. The feasibility is validated by simulation studies on three cases of two cooperating cleaning robots. <s> BIB005 </s> Survey of Robot 3D Path Planning Algorithms <s> Neural Network. <s> Complete coverage path planning requires the robot path to cover every part of the workspace, which is an essential issue in cleaning robots and many other robotic applications such as vacuum robots, painter robots, land mine detectors, lawn mowers, automated harvesters, and window cleaners. In this paper, a novel neural network approach is proposed for complete coverage path planning with obstacle avoidance of cleaning robots in nonstationary environments. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation derived from Hodgkin and Huxley's (1952) membrane equation. There are only local lateral connections among neurons. The robot path is autonomously generated from the dynamic activity landscape of the neural network and the previous robot location. The proposed model algorithm is computationally simple. Simulation results show that the proposed model is capable of planning collision-free complete coverage robot paths. <s> BIB006 </s> Survey of Robot 3D Path Planning Algorithms <s> Neural Network. <s> Due to the NP-hard complexity, the path planning problem may perhaps best be resolved by stochastically searching for an acceptable solution rather than using a complete search to find the guaranteed best solution. Most other evolutionary path planners tend to produce jagged paths consisting of a set of nodes connected by line segments. This paper presents a novel path planning approach based on AppART and Particle Swarm Optimization (PSO). AppART is a neural model multidimensional function approximator, while PSO is a promising evolutionary algorithm. This path planning approach combines neural and evolutionary computing in order to evolve smooth motion paths quickly. In our simulation experiments, some complicated path-planning environments were tested, the result show that the hybrid approach is an effective path planner which outperforms many existing methods. <s> BIB007 </s> Survey of Robot 3D Path Planning Algorithms <s> Neural Network. <s> In this paper, a mixed-model assembly line (MMAL) sequencing problem is studied. This type of production system is used to manufacture multiple products along a single assembly line while maintaining the least possible inventories. With the growth in customers' demand diversification, mixed-model assembly lines have gained increasing importance in the field of management. Among the available criteria used to judge a sequence in MMAL, the following three are taken into account: the minimization of total utility work, total production rate variation, and total setup cost. Due to the complexity of the problem, it is very difficult to obtain optimum solution for this kind of problems by means of traditional approaches. Therefore, a hybrid multi-objective algorithm based on shuffled frog-leaping algorithm (SFLA) and bacteria optimization (BO) are deployed. The performance of the proposed hybrid algorithm is then compared with three well-known genetic algorithms, i.e. PS-NC GA, NSGA-II, and SPEA-II. The computational results show that the proposed hybrid algorithm outperforms the existing genetic algorithms, significantly in large-sized problems. <s> BIB008 </s> Survey of Robot 3D Path Planning Algorithms <s> Neural Network. <s> This paper presents a novel potential field based 3D path planning technique for differential drive mobile robots, moving in known environment. The path planners are based on description of the obstacles by simulated annealing neural networks. The generated paths are piecewise linear with changing directions at the corners of the obstacles. The proposed planner can be successfully applied to snake robots, flying robots, and control of Gantry cranes. Several simulation results show the effectiveness of the proposed algorithm. <s> BIB009 </s> Survey of Robot 3D Path Planning Algorithms <s> Neural Network. <s> Purpose – Attitude control of unmanned aerial vehicle (UAV) is the purposeful manipulation of controllable external forces to establish a desired attitude, which is inner‐loop of the autonomous flight control system. In the practical applications, classical control methods such as proportional‐integral‐derivative control are usually selected because of simple and high reliability. However, it is usually difficult to select or optimize the control parameters. The purpose of this paper is to investigate an intelligent algorithm based classical controller of UAV.Design/methodology/approach – Among the many intelligent algorithms, shuffled frog leaping algorithm (SFLA) combines the benefits of the genetic‐based memetic algorithm as well as social behavior based particle swarm optimization. SFLA is a population based meta‐heuristic intelligent optimization method inspired by natural memetics. In order to improve the performance of SFLA, a different dividing method of the memeplexes is presented to make their p... <s> BIB010 </s> Survey of Robot 3D Path Planning Algorithms <s> Neural Network. <s> Rapidly exploring random trees (RRT) and probabilistic roadmaps (PRM) are sampling-based techniques being extensively used for robot path planning. In this paper, the tree structure of the RRT is generalized to a graph structure which enables a greater exploration. Exploration takes place simultaneously from multiple points in the map, all explorations fusing at multiple points producing well-connected graph architecture. Initially, in a typical RRT manner, the search algorithm attempts to reach the goal by expansions, and thereafter furtherer areas are explored. With some additional computation cost, as compared to RRT with a single robot, the results can be significantly improved. The so-formed graph is similar to roadmap produced by PRM. However as compared to PRM, the proposed algorithm has a more judicious search strategy and is adaptable to the number of nodes as a parameter. Experimental results are shown with multiple robots planned using prioritization scheme. Results show the betterment of the p... <s> BIB011
Another subcategory of bioinspired path planning method is Neural Network (NN). NN was introduced first introduced by Glasius et al. BIB002 to avoid obstacle as well as navigation and then became popular and implemented to path planning in various areas BIB001 BIB005 BIB006 . NN approach aims to generate a dynamic landscape in a neural-like form. It shares some merits as Artificial Potential Field method; the unsearched areas attract the robot in the entire space globally. A typical shunting equation is used to express the dynamics of a robot in neuron network, which is represented in the following form: where denotes the neuron activity of the th neuron. Parameters , , and are nonnegative constants representing the passive decay rate and the upper and lower bounds of the neural activity, respectively. [ ] [ ] − are the excitatory and inhibitory inputs. is the external input to the th neuron, and it is defined as = if it is a safe unexplored area; = − if it is an obstacle area; = 0 means other cases. Here ≫ is a very large positive constant. For NN, in each step it first chooses the maximal neural activity among the neighbor neurons; then the next location SFLA High time complexity BIB010 It is more efficient than PSO and can achieve global convergence faster Parameter sensitive BIB008 MA High time complexity BIB003 It is more efficient than GA in path smoothness and with low computational complexity NN High time complexity BIB001 BIB009  Stable under sudden changes in the network Relying on suitable rules and organisms BIB004 is determined by this maximal neural activity. Equation BIB011 guarantees that the positive neural activity can propagate to all the free unexplored space with the maximal neural activity, but negative activity only stays locally. Although NN attracted so much attention, it shares the weakness as other bioinspired algorithms that it cannot form canonical rules and model; thus the reliability and time consumption are not guaranteed. Even though Hopfield model is introduced to solve the weakness BIB004 , this is only applicable to certain problems. Kassim and Kumar BIB001 propounded a new "wave expansion neural network (WENN)" which introduces grid potential for path planning; the method was implemented to 3D workspace and proved to have time efficiency. Recently Kroumov et al. BIB009  improved the method by combination with potential field methods; this method accelerates the speed of basic NN. But according to BIB001 , it clearly can be seen that when extended to 3D environments, the number of neighbor neurons will increase to 26, which means the computation complexity will explode simultaneously. Thus it is applicable to implement real time. 7.3. Analysis. Bioinspired algorithms stem from mimicking the natural behavior; they specify a set of optimizing rules as well as the model of processing the information. Then the whole process executes based on the rules and models by iterative optimization. Thus the final results rely much on the rules and model proposed, and the time consumption cannot be guaranteed. Often if the environment becomes more complex, they need a large computational resource to find a solution to meet the expectation (Table 5) . EA is named as a kind of population-based algorithm; all of its subcategories share almost the same procedures: reproduction, mutation, recombination, and selection. Although this kind of algorithms tends to be time consuming and premature convergence, it can deal with multiobjectives problems and solve NP-hard problems. GA is the most popular Evolutionary Algorithm, and PSO is proved to act much faster than GA, but PSO is sensitive to parameters. PSO can be solved by hybrid approach BIB007 but also holds the same shortcoming as GA. ACO can find optimal path without premature convergence, but it is impossible for the basic ACO to deal with real complex environment. SFLA operates the local exploration by using PSO-like method, but it shuffles virtual frogs periodically with ensuring global exploration. The PSO-like method relies strongly on suitable parameters BIB008 . MA is sometimes called hybrid GA, which differs from GA in that MA shares chunk of the gene rather than a neat crossover in chromosomes. Since MA coupled with a learning procedure to perform local refinements, it still has a more heavy computational burden. NN is not sensitive to environment changes, but it relies too much on its model.