A 3D Reactive Navigation Algorithm for Mobile Robots by Using Tentacle-Based Sampling

PurposeThe purpose of this paper is to improve the traditional bug algorithms for the navigation of mobile robots in unknown environments by considering the limitations in previous works such as generating long path, limited to static environments as well as ignoring implementation issues. With this purpose, a new bug‐type algorithm termed Distance Histogram Bug (DH‐Bug) is proposed for overcoming these limitations.Design/methodology/approachDH‐Bug redefines the traditional motion modes and switching criteria to shorten the path length. In order to extend the framework of the traditional bug algorithms to tackle the navigation problem in dynamic environments, a new mode and the related switching conditions are designed for dealing with moving obstacles. Moreover, a realization method termed the Distance Histogram (DH) method which takes many implementation issues into full account is proposed for implementing DH‐Bug on real robots.FindingsDH‐Bug is convergent in static environments and it also guarantees “approximate” convergence based on several reasonable assumptions when there are moving obstacles. Simulations results show that DH‐Bug generates shorter average path length than some classical Bug algorithms in static environments and it also performs well in most simulations that contain moving obstacles except for some extremely adverse scenarios which have been discussed in the paper. Experiments on real robots further verify its applicability in both static environments and dynamic environments containing moving obstacles.Originality/valueCompared with previous works, DH‐Bug has three main contributions. First, in static environments, it can shorten the average path length than many classical bug algorithms in the premise of guaranteeing convergence. Second, it can be applied in dynamic environments containing moving obstacles. Third, unlike the previous bug algorithms that always ignore the practical implementation issues, DH‐Bug presents not only an abstract concept, but also a realization approach for realizing this concept on real robots.

This paper presents a comparative study conducted on the performance of the commonly used object tracking and location prediction algorithms for mobile robot navigation in a dynamically cluttered and GPS-denied mining environment. The study was done to test the different algorithms for the same set criteria (such as accuracy and computational time) under the same conditions.The identified commonly used algorithms for object tracking and location prediction of moving objects used in this investigation are Kalman filter (KF), extended Kalman filter (EKF) and particle filter (PF). The study results of those algorithms are analyzed and discussed in this paper. A trade-off was apparent. However, in overall performance KF has shown its competitiveness.The result from the study has found that the KF based algorithm provides better performance in terms of accuracy in tracking dynamic objects under commonly used benchmarks. This finding can be used in development of an efficient robot navigation algorithm.

SUMMARYWe present BioBug, a bionic cognitive response navigation algorithm for mobile robots based on neuroethology principles. It includes a biological antenna model for environment perception and an improved Bug algorithm for motion planning and control. The biological antenna model delineates the interested sensing areas, and thus decreases the computational burden. Then, this obtained environment stimulation is responded to generate the corresponding walking behavior according to BioBug. Simulations and experiments have been carried out in different conditions of obstacle density and boundary shape through algorithm comparisons. Compared with the competitors, BioBug is characterized by not only a smaller path length, but also shorter time for obstacle escape. The results demonstrate the practicality, environment robustness, and obstacle avoidance efficiency of the algorithm.

Navigation is defined as the problem of getting from one place to another in an efficient manner. This is a significant and fundamental problem in robotics. In this paper a new navigation algorithm for mobile robot was proposed. In our navigation system, the robot can navigate autonomously with aid of a wireless sensor network (WSN) so that the additional entity such as the middleware-server is not needed. So this system can be constructed at low cost, and high accuracy.

Artificial Potential Filed (APF) is the most well-known method that is used in mobile robot path planning, however, the shortcoming is that the local minima. To overcome this issue, we present a deadlock free APF based path planning algorithm for mobile robot navigation. The Proposed-APF (P-APF) algorithm searches the goal point in unknown 2D environments. This method is capable of escaping from deadlock and non-reachability problems of mobile robot navigation. In this method, the effective front-face obstacle information associated with the velocity direction is used to modify the Traditional APF (T-APF) algorithm. This modification solves the deadlock problem that the T-APF algorithm often converges to local minima. The proposed algorithm is explained in details and to show the effectiveness of the proposed approach, the simulation experiments were carried out in the MATLAB environment. Furthermore, the numerical analysis of the proposed approach is given to prove a deadlock free motion of the mobile robot.

This article presents a summary of the work done on the development of a new algorithm for mobile robot navigation in unknown dynamic environment. The developed humanlike algorithm uses a free-space attraction (Agoraphilic) concept for robot navigation. The algorithm presented in this article is an advanced development of the Agoraphilic navigation algorithm. The Agoraphilic algorithm does not look for obstacles (problems) to avoid but rather for free spaces toward the goal (solutions) to follow. The original Agoraphilic while it was able to overcome the limitations of the traditional algorithms had its own limitations in navigating robots in environments cluttered with moving obstacles. The new Agoraphilic Navigation Algorithm under Dynamic Environment (ANADE) was developed to overcome those limitations. ANADE consists of seven main modules reported in this article. The objects tracking and objects prediction methodologies developed for the algorithm estimate future growing free-space passages toward the goal. The algorithm generates a time-varying single attractive force to pull the robot through the free space toward the predicted (future) growing free-space passages leading to the goal. The new algorithm was tested, not only through simulation, but also through experimental work. Summary of the experimental results is presented and discussed in this article.

Purpose This study concerns an on-line path planning technique for a behaviour-based wheeled mobile robot local navigation in an unknown environment with hurdles, using the feedforward back-propagation neural network sensor-actuator control technique. The purpose of this study is to find the non-collision path for the mobile robot moving towards the goal in a cluttered environment. Design/methodology/approach Neural network architecture input layers are the different hurdle distance information, which are acquired by an array of equipped sensors, and the output layer is the turning angle (motor control). In this way, the mobile robot is effectively being trained to move autonomously in the environment. Findings Computer simulation and real-time experimental results show that the proposed neural network controller can improve navigation performance in cluttered and unknown environments. Originality/value The proposed neural network controller gives better results (in terms of path length) as compared to previously developed models, which verifies the effectiveness of the proposed architecture.

A local navigation algorithm for mobile robots is proposed, based on the new extended virtual force field (EVFF) concept, neural network-based fusion for the three primitive behaviors generated by the EVFF, and the evolutionary programming-based optimization of the neural network weights. Furthermore, a multi-network version of the above neurally-combined EVFF has been proposed that lends itself not only to an efficient architecture but also to a greatly enhanced generalization capability. These techniques have been verified through both simulation and real experiments under a collection of complex environments.

A new hybrid navigation algorithm for mobile robots in environments with incomplete knowledge

This paper proposes a visual servoing algorithm for mobile robot navigation based on the epipolar geometry retrieved by object profiles. The main motivation for this approach is that for unstructured scenes the task of solving correspondences is definitely a harder task than contour detection. An unstructured three-dimensional (3-D) scene consists mainly of objects whose most important 2-D features are their apparent contours in the image plane. Apparent contours are used to estimate the positions of the epipoles and some special symmetry conditions whereby the visual servoing is able to steer the mobile robot to the desired position.

We discuss mobile robot navigation under linguistic instructions of a route. The instructions given to the robot describe the route using landmarks such as intersections, for example, “turn right at the 2nd intersection, and ….”However, it is difficult to give the robot landmark models so that it recognizes each landmark properly as a human does. We have already proposed an algorithm for mobile robot navigation, which verifies the result of landmark recognition considering the positional relationship among landmarks. In this paper, we describe the motion planning to acquire additional information effectively for navigation of the mobile robot equipped with a range sensor based on the algorithm, aiming at realizing the real robot navigation using the linguistic instructions. The environment is described by a graph based on skeletons of its free space. The motion of the robot is first roughly planned with the graph-based global map and then precisely planned with a geometric local sensory information. In experimental results using a real robot, the robot reached the goal specified by the instructions despite of errors in sensing and motion.

SUMMARYA new motion-free iterative closest point (ICP) algorithm is proposed for building a two-dimensional (2D) map for mobile robot navigation. A laser range finder (LRF) sensor is installed on a mobile robot to scan and measure the depth data of the environment to form a 2D map during mobile robot navigation. Because the scanning and navigation motions are performed independently, the scanned data contain distortions from the motions of the mobile robot. To compensate for the distortions, the proposed motion-free ICP algorithm estimates the effects of the dynamic motions of the robot on the scanning process. That is, the motion-free algorithm compensates for the distance measurement errors related to the dynamic changes in the mobile robot's velocity. Experiments were performed with actual velocity changes of a mobile robot to demonstrate and verify the effective performance of the proposed algorithm.

Deep reinforcement learning methods have been applied to mobile robot navigation to find the optimal path to the target. The rewards are usually given when the task is completed, which may lead to the local optima during the training procedure. It seriously affects the training efficiency and navigation performance of the mobile robot. To this end, this paper proposes an intrinsic reward mechanism with intrinsic curiosity module and randomness enhanced module, combining the TD3 (twin-delayed deep deterministic policy gradient) reinforcement learning algorithm for mobile robot navigation. It effectively resolves the issue of slow convergence caused by sparse rewards in continuous action spaces. It also encourages mobile robots to explore unknown areas and reduces the occurrence of local optima. The experimental results show that the proposed navigation method significantly improves the training efficiency of mobile robots. Out of 1000 test episodes, only 3 exceeded the maximum step limit. This approach significantly reduces the occurrence of local optima. Furthermore, it increases the success rate to an impressive 83.5%, outperforms the existing navigation methods.

This paper presents a new neural network based reactive navigation algorithm for wheeled mobile robots (WMR) in unstructured indoor environments. The mobile robot travels to a pre-defined goal position safely and efficiently without any prior map of the environment. This navigation algorithm is optimized by a user-defined objective function which minimizes the traveled distance to the goal position while avoiding obstacles. The network is trained through off-line learning followed by an on-line learning algorithm with guaranteed convergence. The performance of the proposed algorithm is verified over a variety of real unstructured indoor environments using an autonomous mobile robot platform.

In this paper, a new navigation algorithm for wheeled mobile robots is proposed. The proposed algorithm utilizes fuzzy multi-attribute decision-making method in deciding which via-point the robot would proceed at each decision step. Via-point is defined as the local target point of robot's movement at each decision instance. We define a few fuzzy goals to achieve successful robot navigation. At each decision step, a set of the candidates of next via-point in 2D navigation space is constructed by combining various heading angles and velocities. Given the fuzzy goals, the fuzzy decision-making enables the robot to choose the optimal via-point among the candidates. Results from a series of simulations and an experiment in a real environment show the effectiveness of the proposed method.