Neural network approaches to dynamic collision-free trajectory generation

Biologically Inspired Complete Coverage Path Planning Algorithm Based on Q-Learning

Complete coverage path planning requires that the mobile robot traverse all reachable positions in the environmental map. Aiming at the problems of local optimal path and high path coverage ratio in the complete coverage path planning of the traditional biologically inspired neural network algorithm, a complete coverage path planning algorithm based on Q-learning is proposed. The global environment information is introduced by the reinforcement learning method in the proposed algorithm. In addition, the Q-learning method is used for path planning at the positions where the accessible path points are changed, which optimizes the path planning strategy of the original algorithm near these obstacles. Simulation results show that the algorithm can automatically generate an orderly path in the environmental map, and achieve 100% coverage with a lower path repetition ratio.

End-to-End One-Shot Path-Planning Algorithm for an Autonomous Vehicle Based on a Convolutional Neural Network Considering Traversability Cost

Path planning plays an important role in navigation and motion planning for robotics and automated driving applications. Most existing methods use iterative frameworks to calculate and plan the optimal path from the starting point to the endpoint. Iterative planning algorithms can be slow on large maps or long paths. This work introduces an end-to-end path-planning algorithm based on a fully convolutional neural network (FCNN) for grid maps with the concept of the traversability cost, and this trains a general path-planning model for 10 × 10 to 80 × 80 square and rectangular maps. The algorithm outputs the lowest-cost path while considering the cost and the shortest path without considering the cost. The FCNN model analyzes the grid map information and outputs two probability maps, which show the probability of each point in the lowest-cost path and the shortest path. Based on the probability maps, the actual optimal path is reconstructed by using the highest probability method. The proposed method has superior speed advantages over traditional algorithms. On test maps of different sizes and shapes, for the lowest-cost path and the shortest path, the average optimal rates were 72.7% and 78.2%, the average success rates were 95.1% and 92.5%, and the average length rates were 1.04 and 1.03, respectively.

Finding Optimal Paths Using Networks Without Learning-Unifying Classical Approaches

Trajectory or path planning is a fundamental issue in a wide variety of applications. In this article, we show that it is possible to solve path planning on a maze for multiple start point and endpoint highly efficiently with a novel configuration of multilayer networks that use only weighted pooling operations, for which no network training is needed. These networks create solutions, which are identical to those from classical algorithms such as breadth-first search (BFS), Dijkstra's algorithm, or TD(0). Different from competing approaches, very large mazes containing almost one billion nodes with dense obstacle configuration and several thousand importance-weighted path endpoints can this way be solved quickly in a single pass on parallel hardware.

One-Shot Multi-Path Planning Using Fully Convolutional Networks in a Comparison to Other Algorithms

Path planning plays a crucial role in many applications in robotics for example for planning an arm movement or for navigation. Most of the existing approaches to solve this problem are iterative, where a path is generated by prediction of the next state from the current state. Moreover, in case of multi-agent systems, paths are usually planned for each agent separately (decentralized approach). In case of centralized approaches, paths are computed for each agent simultaneously by solving a complex optimization problem, which does not scale well when the number of agents increases. In contrast to this, we propose a novel method, using a homogeneous, convolutional neural network, which allows generation of complete paths, even for more than one agent, in one-shot, i.e., with a single prediction step. First we consider single path planning in 2D and 3D mazes. Here, we show that our method is able to successfully generate optimal or close to optimal (in most of the cases <10% longer) paths in more than 99.5% of the cases. Next we analyze multi-paths either from a single source to multiple end-points or vice versa. Although the model has never been trained on multiple paths, it is also able to generate optimal or near-optimal (<22% longer) paths in 96.4 and 83.9% of the cases when generating two and three paths, respectively. Performance is then also compared to several state of the art algorithms.

Optimal Motion Planning for Differential Drive Mobile Robots based on Multiple-Interval Chebyshev Pseudospectral Methods

This paper presents a Chebyshev Pseudospectral (PS) method for solving the motion planning problem of nonholonomic mobile robots with kinematic and dynamic constraints. The state and control variables are expanded in the Chebyshev polynomial of order N, and Chebyshev–Gauss–Lobatto (CGL) nodes are provided for approximating the system dynamics, boundary conditions, and performance index. For the lack of enough nodes nearby the obstacles, the interpolation of trajectory may violate the obstacles and the multiple-interval strategy is proposed to deal with the violation. Numerical examples demonstrate that multiple-interval strategy yields more accurate results than the single-interval Chebyshev PS method.

An Intelligent Multi-Sensor Variable Spray System with Chaotic Optimization and Adaptive Fuzzy Control

During the variable spray process, the micro-flow control is often held back by such problems as low initial sensitivity, large inertia, large hysteresis, nonlinearity as well as the inevitable difficulties in controlling the size of the variable spray droplets. In this paper, a novel intelligent double closed-loop control with chaotic optimization and adaptive fuzzy logic is developed for a multi-sensor based variable spray system, where a Bang-Bang relay controller is used to speed up the system operation, and adaptive fuzzy nonlinear PID is employed to improve the accuracy and stability of the system. With the chaotic optimization of controller parameters, the system is globally optimized in the whole solution space. By applying the proposed double closed-loop control, the variable pressure control system includes the pressure system as the inner closed-loop and the spray volume system as the outer closed-loop. Thus, the maximum amount of spray droplets deposited on the plant surface may be achieved with the minimum medicine usage for plants. Multiple sensors (for example: three pressure sensors and two flow rate sensors) are employed to measure the system states. Simulation results show that the chaotic optimized controller has a rise time of 0.9 s, along with an adjustment time of 1.5 s and a maximum overshoot of 2.67% (in comparison using PID, the rise time is 2.2 s, the adjustment time is 5 s, and the maximum overshoot is 6.0%). The optimized controller parameters are programmed into the hardware to control the established variable spray system. The experimental results show that the optimal spray pressure of the spray system is approximately 0.3 MPa, and the flow rate is approximately 0.08 m³/h. The effective droplet rate is 89.4%, in comparison to 81.3% using the conventional PID control. The proposed chaotically optimized composite controller significantly improved the dynamic performance of the control system, and satisfactory control results are achieved.

Trajectory planning and tracking control of a ground mobile robot:A reconstruction approach towards space vehicle

With the development of the similarity calculation method, the orbital motion of space vehicle can be translated into a sequence of waypoints that reflect position and velocity on the ground. In this paper, a motion control system is proposed to make the mobile robot pass through the desired waypoints for reconstructing the orbital motion. First, the parameterized trajectory optimization method is applied to generate a curvature-continuous trajectory from the waypoints, the position and velocity demands are presented as the equality constraints. Virtual positions are introduced to reduce the oscillation, and the total execution time of the whole trajectory is selected as the optimization parameter to reduce the computational burden. Then, an equivalence transformation is provided to translate the error system into an affine form, which is beneficial for the feedback controller design. Based on this, a nonlinear trajectory tracking controller is proposed, which includes a feedforward controller and an error feedback controller, and its exponential stability is proved using Persistency of Excitation Lemma. In addition, a shunting neural dynamics model is employed to avoid sharp velocity jumps. Finally, the performed experiments verify the effectiveness of the proposed method.

Adaptive Neuro-Fuzzy Inference System Based Path Planning for Excavator Arm

This paper presents a scheme based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to generate trajectory for excavator arm. Firstly, the trajectory is predesigned with some specific points in the work space to meet the requirements about the shape. Next, the inverse kinematic is used and optimization problems are solved to generate the via-points in the joint space. These via-points are used as training set for ANFIS to synthesis the smooth curve. In this scheme, the outcome trajectory satisfies the requirements about both shape and optimization problems. Moreover, the algorithm is simple in calculation as the numbers of via-points are large. Finally, the simulation is done for two cases to test the effect of ANFIS structure on the generated trajectory. The simulation results demonstrate that, by using suitable structure of ANFIS, the proposed scheme can build the smooth trajectory which has the good matching with desired trajectory even that the desired trajectory has the complicated shape.

Underactuated tracking control of underwater vehicles using control moment gyros

This article studies the trajectory tracking control of underactuated underwater vehicles using control moment gyros through a biologically inspired approach based on homeomorphism transformation and Lyapunov functions in the horizontal plane. First, a series of assumptions and simplifications need to be made to build the kinematic and dynamic equations of the underwater vehicle under a single-frame pyramid configuration structured with four control moment gyros. Second, the error dynamics analysis of the submarine based on the control moment gyros is derived from the equations, and a tracking control algorithm is proposed to demonstrate the feasibility and stabilization of this tracking control scheme from theoretical analysis. Finally, the numerical simulation results are given for verifying the effectiveness and feasibility of the rendered control law.

A Dynamic Bioinspired Neural Network Based Real-Time Path Planning Method for Autonomous Underwater Vehicles

Real-time path planning for autonomous underwater vehicle (AUV) is a very difficult and challenging task. Bioinspired neural network (BINN) has been used to deal with this problem for its many distinct advantages: that is, no learning process is needed and realization is also easy. However, there are some shortcomings when BINN is applied to AUV path planning in a three-dimensional (3D) unknown environment, including complex computing problem when the environment is very large and repeated path problem when the size of obstacles is bigger than the detection range of sensors. To deal with these problems, an improved dynamic BINN is proposed in this paper. In this proposed method, the AUV is regarded as the core of the BINN and the size of the BINN is based on the detection range of sensors. Then the BINN will move with the AUV and the computing could be reduced. A virtual target is proposed in the path planning method to ensure that the AUV can move to the real target effectively and avoid big-size obstacles automatically. Furthermore, a target attractor concept is introduced to improve the computing efficiency of neural activities. Finally, some experiments are conducted under various 3D underwater environments. The experimental results show that the proposed BINN based method can deal with the real-time path planning problem for AUV efficiently.

Efficient shortest-path-tree computation in network routing based on pulse-coupled neural networks

Shortest path tree (SPT) computation is a critical issue for routers using link-state routing protocols, such as the most commonly used open shortest path first and intermediate system to intermediate system. Each router needs to recompute a new SPT rooted from itself whenever a change happens in the link state. Most commercial routers do this computation by deleting the current SPT and building a new one using static algorithms such as the Dijkstra algorithm at the beginning. Such recomputation of an entire SPT is inefficient, which may consume a considerable amount of CPU time and result in a time delay in the network. Some dynamic updating methods using the information in the updated SPT have been proposed in recent years. However, there are still many limitations in those dynamic algorithms. In this paper, a new modified model of pulse-coupled neural networks (M-PCNNs) is proposed for the SPT computation. It is rigorously proved that the proposed model is capable of solving some optimization problems, such as the SPT. A static algorithm is proposed based on the M-PCNNs to compute the SPT efficiently for large-scale problems. In addition, a dynamic algorithm that makes use of the structure of the previously computed SPT is proposed, which significantly improves the efficiency of the algorithm. Simulation results demonstrate the effective and efficient performance of the proposed approach.

Trajectory Planning and Optimized Adaptive Control for a Class of Wheeled Inverted Pendulum Vehicle Models

In this paper, we investigate optimized adaptive control and trajectory generation for a class of wheeled inverted pendulum (WIP) models of vehicle systems. Aiming at shaping the controlled vehicle dynamics to be of minimized motion tracking errors as well as angular accelerations, we employ the linear quadratic regulation optimization technique to obtain an optimal reference model. Adaptive control has then been developed using variable structure method to ensure the reference model to be exactly matched in a finite-time horizon, even in the presence of various internal and external uncertainties. The minimized yaw and tilt angular accelerations help to enhance the vehicle rider's comfort. In addition, due to the underactuated mechanism of WIP, the vehicle forward velocity dynamics cannot be controlled separately from the pendulum tilt angle dynamics. Inspired by the control strategy of human drivers, who usually manipulate the tilt angle to control the forward velocity, we design a neural-network-based adaptive generator of implicit control trajectory (AGICT) of the tilt angle which indirectly "controls" the forward velocity such that it tracks the desired velocity asymptotically. The stability and optimal tracking performance have been rigorously established by theoretic analysis. In addition, simulation studies have been carried out to demonstrate the efficiency of the developed AGICT and optimized adaptive controller.

Mobile Robot Collision Avoidance in Human Environments

Collision avoidance is a fundamental requirement for mobile robots. Avoiding moving obstacles (also termed dynamic obstacles) with unpredictable direction changes, such as humans, is more challenging than avoiding moving obstacles whose motion can be predicted. Precise information on the future moving directions of humans is unobtainable for use in navigation algorithms. Furthermore, humans should be able to pursue their activities unhindered and without worrying about the robots around them. In this paper, both active and critical regions are used to deal with the uncertainty of human motion. A procedure is introduced to calculate the region sizes based on worst-case avoidance conditions. Next, a novel virtual force field-based mobile robot navigation algorithm (termed QVFF) is presented. This algorithm may be used with both holonomic and nonholonomic robots. It incorporates improved virtual force functions for avoiding moving obstacles and its stability is proven using a piecewise continuous Lyapunov function. Simulation and experimental results are provided for a human walking towards the robot and blocking the path to a goal location. Next, the proposed algorithm is compared with five state-of-the-art navigation algorithms for an environment with one human walking with an unpredictable change in direction. Finally, avoidance results are presented for an environment containing three walking humans. The QVFF algorithm consistently generated collision-free paths to the goal.

VIRTUAL INSTRUMENTATION BASED SYSTEMS FOR REAL-TIME PATH PLANNING OF MOBILE ROBOTS USING BIO-INSPIRED NEURAL NETWORKS

In this paper, novel virtual instrumentation based systems for real-time collision-free path planning and tracking control of mobile robots are proposed. The developed virtual instruments are computationally simple and efficient in comparison to other approaches, which act as a new soft-computing platform to implement a biologically-inspired neural network. This neural network is topologically arranged with only local lateral connections among neurons. The dynamics of each neuron is described by a shunting equation with both excitatory and inhibitory connections. The neural network requires no off-line training or on-line learning, which is capable of planning a comfortable trajectory to the target without suffering from neither the too close nor the too far problems. LabVIEW is chosen as the software platform to build the proposed virtual instrumentation systems, as it is one of the most important industrial platforms. We take the initiative to develop the first neuro-dynamic application in LabVIEW. The developed virtual instruments could be easily used as educational and research tools for studying various robot path planning and tracking situations that could be easily understood and analyzed step by step. The effectiveness and efficiency of the developed virtual instruments are demonstrated through simulation and comparison studies.

Bioinspired neural network for real-time cooperative hunting by multirobots in unknown environments

Multiple robot cooperation is a challenging and critical issue in robotics. To conduct the cooperative hunting by multirobots in unknown and dynamic environments, the robots not only need to take into account basic problems (such as searching, path planning, and collision avoidance), but also need to cooperate in order to pursue and catch the evaders efficiently. In this paper, a novel approach based on a bioinspired neural network is proposed for the real-time cooperative hunting by multirobots, where the locations of evaders and the environment are unknown and changing. The bioinspired neural network is used for cooperative pursuing by the multirobot team. Some other algorithms are used to enable the robots to catch the evaders efficiently, such as the dynamic alliance and formation construction algorithm. In the proposed approach, the pursuing alliances can dynamically change and the robot motion can be adjusted in real-time to pursue the evader cooperatively, to guarantee that all the evaders can be caught efficiently. The proposed approach can deal with various situations such as when some robots break down, the environment has different boundary shapes, or the obstacles are linked with different shapes. The simulation results show that the proposed approach is capable of guiding the robots to achieve the hunting of multiple evaders in real-time efficiently.

Real-time robot path planning via a distance-propagating dynamic system with obstacle clearance

An efficient grid-based distance-propagating dynamic system is proposed for real-time robot path planning in dynamic environments, which incorporates safety margins around obstacles using local penalty functions. The path through which the robot travels minimizes the sum of the current known distance to a target and the cumulative local penalty functions along the path. The algorithm is similar to D* but does not maintain a sorted queue of points to update. The resulting gain in computational speed is offset by the need to update all points in turn. Consequently, in situations where many obstacles and targets are moving at substantial distances from the current robot location, this algorithm is more efficient than D*. The properties of the algorithm are demonstrated through a number of simulations. A sufficient condition for capture of a target is provided.

Real-time collision-free motion planning of a mobile robot using a Neural Dynamics-based approach

A neural dynamics based approach is proposed for real-time motion planning with obstacle avoidance of a mobile robot in a nonstationary environment. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation or an additive equation. The real-time collision-free robot motion is planned through the dynamic neural activity landscape of the neural network without any learning procedures and without any local collision-checking procedures at each step of the robot movement. Therefore the model algorithm is computationally simple. There are only local connections among neurons. The computational complexity linearly depends on the neural network size. The stability of the proposed neural network system is proved by qualitative analysis and a Lyapunov stability theory. The effectiveness and efficiency of the proposed approach are demonstrated through simulation studies.

An efficient dynamic system for real-time robot-path planning

This paper presents a simple yet efficient dynamic-programming (DP) shortest path algorithm for real-time collision-free robot-path planning applicable to situations in which targets and barriers are permitted to move. The algorithm works in real time and requires no prior knowledge of target or barrier movements. In the case that the barriers are stationary, this paper proves that this algorithm always results in the robot catching the target, provided it moves at a greater speed than the target, and the dynamic-system update frequency is sufficiently large. Like most robot-path-planning approaches, the environment is represented by a topologically organized map. Each grid point on the map has only local connections to its neighboring grid points from which it receives information in real time. The information stored at each point is a current estimate of the distance to the nearest target and the neighbor from which this distance was determined. Updating the distance estimate at each grid point is done using only the information gathered from the point's neighbors, that is, each point can be considered an independent processor, and the order in which grid points are updated is not determined based on global knowledge of the current distances at each point or the previous history of each point. The robot path is determined in real time completely from the information at the robot's current grid-point location. The computational effort to update each point is minimal, allowing for rapid propagation of the distance information outward along the grid from the target locations. In the static situation, where both the targets and the barriers do not move, this algorithm is a DP solution to the shortest path problem, but is restricted by lack of global knowledge. In this case, this paper proves that the dynamic system converges in a small number of iterations to a state where the minimal distance to a target is recorded at each grid point and shows that this robot-path-planning algorithm can be made to always choose an optimal path. The effectiveness of this algorithm is demonstrated through a number of simulations.

Full-state tracking control of a mobile robot using neural networks

In this paper a nonholonomic mobile robot with completely unknown dynamics is discussed. A mathematical model has been considered and an efficient neural network is developed, which ensures guaranteed tracking performance leading to stability of the system. The neural network assumes a single layer structure, by taking advantage of the robot regressor dynamics that expresses the highly nonlinear robot dynamics in a linear form in terms of the known and unknown robot dynamic parameters. No assumptions relating to the boundedness is placed on the unmodeled disturbances. It is capable of generating real-time smooth and continuous velocity control signals that drive the mobile robot to follow the desired trajectories. The proposed approach resolves speed jump problem existing in some previous tracking controllers. Further, this neural network does not require offline training procedures. Lyapunov theory has been used to prove system stability. The practicality and effectiveness of the proposed tracking controller are demonstrated by simulation and comparison results.

The dynamic wave expansion neural network model for robot motion planning in time-varying environments

We introduce a new type of neural network--the dynamic wave expansion neural network (DWENN)--for path generation in a dynamic environment for both mobile robots and robotic manipulators. Our model is parameter-free, computationally efficient, and its complexity does not explicitly depend on the dimensionality of the configuration space. We give a review of existing neural networks for trajectory generation in a time-varying domain, which are compared to the presented model. We demonstrate several representative simulative comparisons as well as the results of long-run comparisons in a number of randomly-generated scenes, which reveal that the proposed model yields dominantly shorter paths, especially in highly-dynamic environments.

A neural network approach to complete coverage path planning

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.