Enhanced navigation for mobile robots in unknown environments using PER-DDQN with multi-step techniques

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.

Mobile robot navigation with the combination of supervised learning in cerebellum and reward-based learning in basal ganglia

The article presents a random neural Q-learning strategy for the obstacle avoidance problem of an autonomous mobile robot in unknown environments. In the proposed strategy, two independent modules, namely, avoidance without considering the target and goal-seeking without considering obstacles, are first trained using the proposed random neural Q-learning algorithm to obtain their best control policies. Then, the two trained modules are combined based on a switching function to realize the obstacle avoidance in unknown environments. For the proposed random neural Q-learning algorithm, a single-hidden layer feedforward network is used to approximate the Q-function to estimate the Q-value. The parameters of the single-hidden layer feedforward network are modified using the recently proposed neural algorithm named the online sequential version of extreme learning machine, where the parameters of the hidden nodes are assigned randomly and the sample data can come one by one. However, different from the original online sequential version of extreme learning machine algorithm, the initial output weights are estimated subjected to quadratic inequality constraint to improve the convergence speed. Finally, the simulation results demonstrate that the proposed random neural Q-learning strategy can successfully solve the obstacle avoidance problem. Also, the higher learning efficiency and better generalization ability are achieved by the proposed random neural Q-learning algorithm compared with the Q-learning based on the back-propagation method.

This paper presents a simultaneous localization,mapping (SLAM) and object tracking(OT) method based on interacting multiple model (IMM) filter to achieve simultaneous estimation of robot and object's trajectories in unknown environments. The proposed approach integrates IMM based object tracking and EKF-based SLAM. Since the implementation of IMM, the method can help the robot to track object and estimate its motion model, which improves the accuracy of object localization. Moreover, this approach can help robot to track the object that have more ability to escape, and improve it practicality. Simulation and real robot experiment results validate the effectiveness of the proposed method in robot and target localization and mapping. The results also show the accuracy of target motion model estimation and the system ability to track maneuvering target.

A path planning method for the tractor-trailer mobile robot in unknown environment based on rapid-exploring random trees is proposed. The space model of the environment is firstly constructed with sonar sensors. Mechanism of evaporation and enhancement are designed to reduce the dimension of the search space. The feasible path is generated with Dual-RRT algorithm, which is satisfied with the nonholonomic and mechanical constraints. Disbranch operation on the RRT is executed to avoid the repeated searches and increase the search speed. Results of simulation and pro-type experiment show that the tractor-traile mobile robot can quickly plan a feasible path under complex environment, which not only avoid the any collision with the obstacles but also satisfies the constraints of the system.

Several algorithms have been developed to help guide mobile robots in unknown environments. Various kinds of Bug algorithms are available and each one these algorithms has an advantage over the others under different circumstances. This paper introduces a new approach, the Diligent-Bug (D-Bug) algorithm, which is developed to enable a collision free navigation of robots in an unknown 2-dimensional environment. Static obstacles of arbitrary shapes have been considered to evaluate the developed algorithm. This algorithm also enables robots to avoid getting stuck in both local and global loops.

Aiming at the problems of robot path planning with artificial potential field method, such as target unreachable and local minimum points, an improved artificial potential field method model is proposed. The obstacles in unknown environment are designed in the form of grid map, so that the robot can avoid obstacles and move towards the target through perception. Matlab GUI is used to set up a two-dimensional coordinate system environment including obstacles and target points, and the optimal trajectory movement simulation experiment is carried out from the initial position to the desired position in the plane map. Experiments show that the improved artificial potential field method can make the mobile robot navigate in unknown environment, avoid obstacles and find the appropriate path, so as to achieve the requirement of collision free. The simulation results are close to the expected results, which shows that this method can effectively improve the feasibility of path planning and the effectiveness of obstacle avoidance of mobile robot in unknown environment.

A new complete coverage path planning algorithm based on rolling path planning, dynamic heuristic searching and directly filling is proposed to improve efficiency of path planning for mobile robot in unknown environments. The biological inspired neural network was used to model the environment of mobile robot. After the characteristic of local environment around robot was identified on line, the dynamic heuristic planning method combing static searching and following the boundary of obstacle is applied and generated path is much shorter and less turning. Using virtual obstacle and directly filling algorithm, the continuously covered area in U shape obstacle is obtained in unknown environments. The effectiveness of the proposed algorithm is validated by simulation in unknown complicated environments.

In order to solve the problem of moving object tracking by robot in unknown environment,an estimation algorithm based on extended Kalman filter(EKF) is proposed.The states of robot,environment feature and object are used to form system state as a whole in the algorithm,such that sufficient relation is established gradually among states of different objects in iteration process,which improves accuracy of object state estimation.Moreover,a method of moving object detection based on occupancy grid map is combined with our algorithm to obtain the measurements of moving object and environment landmarks,so that the final algorithm can be used in actual environment.Furthermore,the step of data association proposed in algorithm can deal with the system state estimation disturbance caused by false object observations.Simulation experiment and real robot experiment results prove the effectiveness and accuracy of the presented approach.

A general scheme of cooperative hunting for a moving target by multiple mobile robots in unknown environments is presented. To realize successful hunting, the point stabilization control algorithm is used for path planning of hunting mobile robots. Simultaneously, collision risk based obstacle avoidance scheme is used to keep away from random obstacles during hunting. The synthesized behavior is obtained by fusing avoidance behavior, encirclement behavior and pincer attack behavior. Simulated hunting experiments were conduct under different conditions in Microsoft Robotics Studio (MRS) and simulation results demonstrate the correctness and effectiveness of the proposed scheme.

This paper presents an improvement for the software implementation (MOFS) of a user adaptive fuzzy control system for autonomous navigation of mobile robots in unknown environments. This improvement consists of a priority areas deflnition where the environment is measured by a PLS laser sensor, in order to get a reduction in the number of fuzzy rules and also in the computational cost, and hence obtaining improvements in the trajectory. This system has been tested in a pioneer mobile robot and on a robotic wheelchair, odometry sensors are used to localize the robots and the goal positions. The system is able to drive the robots to their goal position avoiding static and dynamic obstacles, without using any pre-built map. This approach improves the way to measure the danger of the obstacles, the way to follow the walls of corridors and the detection of doors. These improvements reduce the zigzag efiect of the previous system by making the trajectories signiflcantly straighter and hence reducing the time to reach the goal position.

Learning-based autonomous navigation for mobile robot in unknown environments

In this paper, a navigation method is proposed for cooperative load-carrying mobile robots. The behavior mode manager is used efficaciously in the navigation control method to switch between two behavior modes, wall-following mode (WFM) and goal-oriented mode (GOM), according to various environmental conditions. Additionally, an interval type-2 neural fuzzy controller based on dynamic group artificial bee colony (DGABC) is proposed in this paper. Reinforcement learning was used to develop the WFM adaptively. First, a single robot is trained to learn the WFM. Then, this control method is implemented for cooperative load-carrying mobile robots. In WFM learning, the proposed DGABC performs better than the original artificial bee colony algorithm and other improved algorithms. Furthermore, the results of cooperative load-carrying navigation control tests demonstrate that the proposed cooperative load-carrying method and the navigation method can enable the robots to carry the task item to the goal and complete the navigation mission efficiently.

This paper addresses the problems related to the mapless navigation control of wheeled mobile robots based on deep learning technology. The traditional navigation control framework is based on a global map of the environment, and its navigation performance depends on the quality of the global map. In this paper, we proposes a mapless Light Detection and Ranging (LiDAR) navigation control method for wheeled mobile robots based on deep imitation learning. The proposed method is a data-driven control method that directly uses LiDAR sensors and relative target position for mobile robot navigation control. A deep convolutional neural network (CNN) model is proposed to predict motion control commands of the mobile robot without the requirement of the global map to achieve navigation control of the mobile robot in unknown environments. While collecting the training dataset, we manipulated the mobile robot to avoid obstacles through manual control and recorded the raw data of the LiDAR sensor, the relative target position, and the corresponding motion control commands. Next, we applied a data augmentation method on the recorded samples to increase the number of training samples in the dataset. In the network model design, the proposed CNN model consists of a LiDAR CNN module to extract LiDAR features and a motion prediction module to predict the motion behavior of the robot. In the model training phase, the proposed CNN model learns the mapping between the input sensor data and the desired motion behavior through end-to-end imitation learning. Experimental results show that the proposed mapless LiDAR navigation control method can safely navigate the mobile robot in four unseen environments with an average success rate of 75%. Therefore, the proposed mapless LiDAR navigation control system is effective for robot navigation control in an unknown environment without the global map.

In this paper, we propose an autonomous exploration and a tapping mechanism-based material mapping system for a mobile robot in unknown environments. The goal of the proposed system is to integrate simultaneous localization and mapping (SLAM) modules and sound-based material classification to enable a mobile robot to explore an unknown environment autonomously and at the same time identify the various objects and materials in the environment. This creates a material map that localizes the various materials in the environment which has potential applications for search and rescue scenarios. A tapping mechanism and tapping audio signal processing based on machine learning techniques are exploited for a robot to identify the objects and materials. We demonstrate the proposed system through experiments using a mobile robot platform installed with Velodyne LiDAR, a linear solenoid, and microphones in an exploration-like scenario with various materials. Experiment results demonstrate that the proposed system can create useful material maps in unknown environments.