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

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.

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.

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.

Recently, various autonomous mobile robots have been developed for practical use. To support the coexistence of robots and humans in real environments, we propose a concept named ‘Region with Velocity Constraints (RVC),’ which is set around hazardous areas. RVCs are regions where the velocities of the robot are constrained to predefined values. Inside the RVCs, the robot has to reduce its translational velocity to avoid predicted hazards such as collisions with obstacles, and to reduce its rotational velocity to prevent undesirable motions such as sharp turns. We also propose a motion planning method for navigating the mobile robot in an environment with RVCs based on the Navigation Function and Global Dynamic Window Approach. Our method generates a trajectory satisfying both translational and rotational velocity constraints to be compatible with the surroundings. Moreover, to demonstrate the validity of our method, we performed numerical simulations and experiments.

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.

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.

In this paper, a novel genetic algorithm based approach to path planning of a mobile robot is proposed. The major characteristic of the proposed algorithm is that the chromosome has a variable length. The location target and obstacles are included to find a path for a mobile robot in an environment that is a 2D workplace discretized into a grid net. Each cell in the net is a gene. The number of genes in one chromosome depends on the environment. The locations of the robot, the target and the obstacle are marked in the workplace. The proposed algorithm is capable of generating collision-free paths for a mobile robot in both static and dynamic environments. In a static environment, the generated robot path is optimal in the sense of the shortest distance. The effectiveness of the proposed model is demonstrated by simulation studies.

Knowing the position of a mobile robot in the environment in which it operates is an important element for effectively accomplishing complex tasks requiring autonomous navigation. Among several existing techniques for robot self-localization, a new approach called Hough localization was proposed for map matching in the Hough domain, that turned out to be reliable and efficient for position tracking in polygonal environments. In this paper we present an extension of Hough localization which is able to deal with the global localization problem, in which the robot does not know its initial position in the environment.

A hybrid navigation strategy for multiple mobile robots

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 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.

A control strategy for real-time collision avoidance of a mobile robot in an environment containing moving obstacles is proposed. Objects, including the robot, are modelled as convex polyhedra. Collision avoidance is guaranteed if the minimum distance between the robot and the objects is nonzero. A nominal trajectory is assumed to be known from offline planning. The main idea is to change the velocity along the nominal trajectory so that collisions are avoided. Consistency with the nominal plan is desirable. The process is formulated as an optimization problem and a close to optimal solution is obtained. >

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.