Robot Navigation In Dynamic Environments Research Articles

Local Path Planner for Mobile Robot Considering Future Positions of Obstacles

Local path planning is a necessary ability for mobile robot navigation, but existing planners are not sufficiently effective at dynamic obstacle avoidance. In this article, an improved timed elastic band (TEB) planner based on the requirements of mobile robot navigation in dynamic environments is proposed. The dynamic obstacle velocities and TEB poses are fully integrated through two-dimensional (2D) lidar and multi-obstacle tracking. First, background point filtering and clustering are performed on the lidar points to obtain obstacle clusters. Then, we calculate the data association matrix of the obstacle clusters of the current and previous frame so that the clusters can be matched. Thirdly, a Kalman filter is adopted to track clusters and obtain the optimal estimates of their velocities. Finally, the TEB poses and obstacle velocities are associated: we predict the obstacle position corresponding to the TEB pose through the detected obstacle velocity and add this constraint to the corresponding TEB pose vertex. Then, a pose sequence considering the future positions of obstacles is obtained through a graph optimization algorithm. Compared with the original TEB, our method reduces the total running time by 22.87%, reduces the running distance by 19.23%, and increases the success rate by 21.05%. Simulations and experiments indicate that the improved TEB enables robots to efficiently avoid dynamic obstacles and reach the goal as quickly as possible.

Autonomous Robot Navigation in Dynamic Environments: A Temporal-Difference Learning Approach

Autonomous Robot Navigation in Dynamic Environments: A Temporal-Difference Learning Approach

A LiDAR Mapping System for Robot Navigation in Dynamic Environments

A LiDAR Mapping System for Robot Navigation in Dynamic Environments

A safe reinforcement learning approach for autonomous navigation of mobile robots in dynamic environments

AbstractWhen deploying mobile robots in real‐world scenarios, such as airports, train stations, hospitals, and schools, collisions with pedestrians are intolerable and catastrophic. Motion safety becomes one of the most fundamental requirements for mobile robots. However, until now, efficient and safe robot navigation in such dynamic environments is still an open problem. The critical reason is that the inconsistency between navigation efficiency and motion safety is greatly intensified by the high dynamics and uncertainties of pedestrians. To face the challenge, this paper proposes a safe deep reinforcement learning algorithm named Conflict‐Averse Safe Reinforcement Learning (CASRL) for autonomous robot navigation in dynamic environments. Specifically, it first separates the collision avoidance sub‐task from the overall navigation task and maintains a safety critic to evaluate the safety/risk of actions. Later, it constructs two task‐specific but model‐agnostic policy gradients for goal‐reaching and collision avoidance sub‐tasks to eliminate their mutual interference. Then, it further performs a conflict‐averse gradient manipulation to address the inconsistency between two sub‐tasks. Finally, extensive experiments are performed to evaluate the superiority of CASRL. Simulation results show an average 8.2% performance improvement over the vanilla baseline in eight groups of dynamic environments, which is further extended to 13.4% in the most challenging group. Besides, forty real‐world experiments fully illustrated that the CASRL could be successfully deployed on a real robot.

Mobile Robot Navigation in Dynamic Environments by Offline Reinforcement Learning using Flow-Based Generative Model

Mobile Robot Navigation in Dynamic Environments by Offline Reinforcement Learning using Flow-Based Generative Model

RL-DOVS: Reinforcement Learning for Autonomous Robot Navigation in Dynamic Environments.

Autonomous navigation in dynamic environments where people move unpredictably is an essential task for service robots in real-world populated scenarios. Recent works in reinforcement learning (RL) have been applied to autonomous vehicle driving and to navigation around pedestrians. In this paper, we present a novel planner (reinforcement learning dynamic object velocity space, RL-DOVS) based on an RL technique for dynamic environments. The method explicitly considers the robot kinodynamic constraints for selecting the actions in every control period. The main contribution of our work is to use an environment model where the dynamism is represented in the robocentric velocity space as input to the learning system. The use of this dynamic information speeds the training process with respect to other techniques that learn directly either from raw sensors (vision, lidar) or from basic information about obstacle location and kinematics. We propose two approaches using RL and dynamic obstacle velocity (DOVS), RL-DOVS-A, which automatically learns the actions having the maximum utility, and RL-DOVS-D, in which the actions are selected by a human driver. Simulation results and evaluation are presented using different numbers of active agents and static and moving passive agents with random motion directions and velocities in many different scenarios. The performance of the technique is compared with other state-of-the-art techniques for solving navigation problems in environments such as ours.

A Novel Method for Path Planning of Mobile Robots via Fuzzy Logic and ant Colony Algorithem in Complex Daynamic Environments

Researches on mobile robot path planning with meta-heuristic methods to improve classical approaches have grown dramatically in the recent 35 years. Because routing is one of the NP-hard problems, an ant colony algorithm that is a meta-heuristic method has had no table success in this area. In this paper, a new approach for solving mobile robot navigation in dynamic environments, based on the heuristic feature of an optimized ant colony algorithm is proposed. Decision-making influenced by the distances between the origin and destination points and the angle variance to the nearest obstacles. Ideal paths are selected by the fuzzy logic. The proposed ant colony algorithm will optimize the fuzzy rules’ parameters that have been using to On-line (instant) path planning in dynamic environments. This paper presents a new method that can plan local routs all over the area and to guide the moving robot toward the final track. Using this algorithm, mobile robots can move along the ideal path to the target based on the optimal fuzzy control systems in different environments, especially in dynamic and unknown environments.

Past, Present and Future of Path-Planning Algorithms for Mobile Robot Navigation in Dynamic Environments

Mobile robots have been making a significant contribution to the advancement of many sectors including automation of mining, space, surveillance, military, health, agriculture and many more. Safe and efficient navigation is a fundamental requirement of mobile robots, thus, the demand for advanced algorithms rapidly increased. Mobile robot navigation encompasses the following four requirements: perception, localization, pathplanning and motion control. Among those, path-planning is a vital part of a fast, secure operation. During the last couple of decades, many path-planning algorithms were developed. Despite most of the mobile robot applications being in dynamic environments, the number of algorithms capable of navigating robots in dynamic environments is limited. This paper presents a qualitative comparative study of the up-to-date mobile robot pathplanning methods capable of navigating robots in dynamic environments. The paper discusses both classical and heuristic methods including artificial potential field, genetic algorithm, fuzzy logic, neural networks, artificial bee colony, particle swarm optimization, bacterial foraging optimization, ant-colony and Agoraphilic algorithm. The general advantages and disadvantages of each method are discussed. Furthermore, the commonly used state-of-the-art methods are critically analyzed based on six performance criteria: algorithm's ability to navigate in dynamically cluttered areas, moving goal hunting ability, object tracking ability, object path prediction ability, incorporating the obstacle velocity in the decision, validation by simulation and experimentation. This investigation benefits researchers in choosing suitable path-planning methods for different applications as well as identifying gaps in this field.

Obstacle Avoidance during Robot Navigation in Dynamic Environment using Fuzzy Controller

A Simulink model containing fuzzy logic controller for collision-free robot navigation in a dynamic environment is presented in this paper. Two controllers, pure pursuit and fuzzy logic controller, are considered to handle robot navigation with obstacle avoidance. Ignoring the obstacles, the pure pursuit controller computes the required linear and angular velocities to direct robot from start to goal location. However, if obstacles are present in the navigation path then the robot will get collided with obstacles in the path. As a result, the robot will not reach to the provided goal location. The fuzzy logic controller is used to avoid obstacles in the navigation path. The fuzzy logic controller takes obstacle distance, obstacle angle, target direction and the x coordinate of goal location as inputs. Consequently, the fuzzy logic controller outputs the required change in angular velocity for the robot. This change in angular velocity is applied to the angular velocity provided by the pure pursuit controller. The experimental work is performed using Turtlebot Gazebo simulator. The navigation including environment, obstacles and resultant paths are also manifested.

Integrating a Path Planner and an Adaptive Motion Controller for Navigation in Dynamic Environments

Since an individual approach can hardly navigate robots through complex environments, we present a novel two-level hierarchical framework called JPS-IA3C (Jump Point Search improved Asynchronous Advantage Actor-Critic) in this paper for robot navigation in dynamic environments through continuous controlling signals. Its global planner JPS+ (P) is a variant of JPS (Jump Point Search), which efficiently computes an abstract path of neighboring jump points. These nodes, which are seen as subgoals, completely rid Deep Reinforcement Learning (DRL)-based controllers of notorious local minima. To satisfy the kinetic constraints and be adaptive to changing environments, we propose an improved A3C (IA3C) algorithm to learn the control policies of the robots’ local motion. Moreover, the combination of modified curriculum learning and reward shaping helps IA3C build a novel reward function framework to avoid learning inefficiency because of sparse reward. We additionally strengthen the robots’ temporal reasoning of the environments by a memory-based network. These improvements make the IA3C controller converge faster and become more adaptive to incomplete, noisy information caused by partial observability. Simulated experiments show that compared with existing methods, this JPS-IA3C hierarchy successfully outputs continuous commands to accomplish large-range navigation tasks at shorter paths and less time through reasonable subgoal selection and rational motions.

Mobile robot navigation based on tangent circle algorithm

A difficult issue in mobile robot navigation or path planning in static or dynamic environment is to find the path from the starting point to the target avoiding collisions with obstacles. This paper presents a new approach for solving the problem of mobile robot navigation in dynamic environment based on tangent circle algorithm. To plan the trajectory of the robot, the algorithm relies on the creation of different types of paths between the robot and the target. In fact, during the motion path, the robot switches between a straight line and/or circular trajectory in order to avoid obstacles and reach the goal. The proposed approach is implemented on different simulation scenarios involving state or dynamic obstacles in the environment. Results show that the novel method is efficient and that the mobile robot succeeds to reach its target in a collision-free path in different cases.

Interactive Motion Planning for Mobile Robot Navigation in Dynamic Environments

In dynamic environments, taking static and moving obstacles into consideration in motion planning for mobile robot navigation is a technical issue. In this paper, we use a single mobile robot, for which humans are moving obstacles. Since moving humans sometimes get in the way of the robot, it must avoid collisions with them. Furthermore, if a part of the environment is crowded with humans, it is better for the robot to detour around the congested area. For this navigational challenge, we focus on the interaction between humans and the robot, so this paper proposes a motion planner for successfully getting through the human-robot interaction. The interactive motion planner is based on the hybrid use of global and local path planners. Furthermore, the local path planner is executed repetitively during the navigation. Through the human-robot interaction, the robot is enabled not only to avoid the collisions with humans but also to detour around congested areas. The emergence of this movement is the main contribution of this paper. We discuss the simulation results in terms of the effectiveness of the proposed motion planner for robot navigation in dynamic environments that include humans.

Special Issue on Human-Robot Interaction Systems II

Six years have passed since the publication of our Special Issue on Human-Robot Interaction Systems in 2011. Since then, artificial intelligence and robotics have developed rapidly, and the opportunities for human beings and robots to work together have increased. The objective of this special issue’s twelve articles is to activate and expand high-quality research.In the first article, Y. Tamura, T. Akashi, and H. Osumi propose a computational model of robot’s gaze. In the second article, S. Hoshino and K. Uchida propose an interactive motion planner for robot navigation in dynamic environments. In the third article, T. Iio, Y. Yoshikawa, and H. Ishiguro develop a conversational robotic system based on human response. In the fourth article, K. Sakai, F. Dalla Libera, Y. Yoshikawa, and H. Ishiguro propose a method for generating bystander robots’ actions that is based on an analysis of the relative probabilities of human responses to robot actions. In the fifth article, T. Matsumaru and M. Narita present a newly developed support system for learning calligraphy strokes. In the sixth article, E. Tamura, Y. Yamashita, T. Yamashita, E. Sato-Shimokawara, and T. Yamaguchi present a method of driving a car simply by gesturing. In the seventh article, A. Kurosu and T. Hashimoto develop an eye robot with two degrees of freedom. It is intended for use as a communication robot. In the eighth article, T. Hashimoto, Y. Munakata, R. Yamanaka, and A. Kurosu report on a method for retrieving episodic memories. In the ninth article, Y. Nishikawa, Y. Kagawa, and A. Okazaki develop a spiral movement robot for inpatients. In the tenth article, Y. Umesawa, K. Doi, and H. Fujimoto develop an interface device that creates kinaesthetic illusions by inducing vibrations in muscle tendons, vibrations that coordinate with dual-joint movements. In the eleventh article, R. Horio, N. Uchiyama, and S. Sano propose a human-operated biped robot for transporting objects over rough terrain or up steps. In the closing contribution, T. Sakuraba, N. Uchiyama, S. Sano, and T. Sakaguchi present the design of a spring-based regenerative brake, and they verify its effectiveness by driving a system that uses it.We thank the referees for their comprehensive reviews and the staff members of Fuji Technology Press, Ltd. for their encouragement and advice.

Robot navigation in dynamic environments using global-appearance descriptors. State of the art

Map building and localization are two impor- tant abilities that autonomous mobile robots must develop. This way, much research has been carried out on these topics, and researchers have proposed many approaches to address these problems. This work presents a state of the art report on map building and localization using global appearance descriptors. In this approach, robots capture visual information from the environment and obtain, usually by means of a transformation, a global appearance descriptor for each image. Using these descriptors, the robot is able to estimate its location in a map previously built, which is also composed of a set of global appearance descriptors. Several previous investigations that have led to the approach of this research are summarized in this paper, such as researches that compare several methods of creating global appearance descriptors. In these works we observe how the continuous optimization of the algorithms has lead to better estimations of the robot position within the environment. Finally a number of future directions in which researches are currently working are listed.

Path planning with obstacle avoidance based on visibility binary tree algorithm

In this paper, a novel method for robot navigation in dynamic environments, referred to as visibility binary tree algorithm, is introduced. To plan the path of the robot, the algorithm relies on the construction of the set of all complete paths between robot and target taking into account inner and outer visible tangents between robot and circular obstacles. The paths are then used to create a visibility binary tree on top of which an algorithm for shortest path is run. The proposed algorithm is implemented on two simulation scenarios, one of them involving global knowledge of the environment, and the other based on local knowledge of the environment. The performance are compared with three different algorithms for path planning.

An Efficient Path Replanning Technique for EST-Based Robot Navigation in Dynamic Environments

Expansive-Spaces Trees (EST) is a single-query sampling-based path planner. When EST is applied to robot navigation in dynamic environments, EST confronts the risk of a collision with dynamic obstacles since it has generated a path without any consideration for dynamic obstacles. This paper proposes an efficient path replanning technique for EST-based robot navigation in dynamic environments. The proposed technique replans a collision-free and efficient path instead of the original EST path which may cause a collision with dynamic obstacles. Besides, the replanned path can be easily merged into the original EST path because it preserves the property of EST. Simulation results in various dynamic environments reveal that the proposed technique can successfully replan a collision-free and efficient path for EST-based navigation.

Grafting: A Path Replanning Technique for Rapidly-Exploring Random Trees in Dynamic Environments

The rapidly-exploring random trees (RRT) is a sampling-based path planner which utilizes simultaneously kinematics and dynamics of a robot. However, since the RRT has produced a robot path without taking the existence of dynamic obstacles into consideration, RRT-based navigation has the risk of a collision with dynamic obstacles. We proposed a path replanning technique for the RRT applied to robot navigation in dynamic environments, which is named grafting. The proposed technique replans a safe and efficient path in real time instead of the original path which may cause a collision with dynamic obstacles. Moreover, the replanned path can be easily merged into the original RRT path because the grafting technique preserves the property of the RRT. The grafting technique was tested by simulations in various dynamic environments, which revealed that the grafting technique was capable of replanning a safe and efficient path for RRT-based navigation in real time.

Adaptive Wavelet Fuzzy Control for Mobile Robot Navigation in Dynamic Environment

Adaptive Wavelet Fuzzy Control for Mobile Robot Navigation in Dynamic Environment

Efficient grid-based spatial representations for robot navigation in dynamic environments

In robotics, grid maps are often used for solving tasks like collision checking, path planning, and localization. Many approaches to these problems use Euclidean distance maps (DMs), generalized Voronoi diagrams (GVDs), or configuration space (c-space) maps. A key challenge for their application in dynamic environments is the efficient update after potential changes due to moving obstacles or when mapping a previously unknown area. To this end, this paper presents novel algorithms that perform incremental updates that only visit cells affected by changes. Furthermore, we propose incremental update algorithms for DMs and GVDs in the configuration space of non-circular robots. These approaches can be used to implement highly efficient collision checking and holonomic path planning for these platforms. Our c-space representations benefit from parallelization on multi-core CPUs and can also be integrated with other state-of-the-art path planners such as rapidly-exploring random trees.In various experiments using real-world data we show that our update strategies for DMs and GVDs require substantially less cell visits and computation time compared to previous approaches. Furthermore, we demonstrate that our GVD algorithm deals better with non-convex structures, such as indoor areas. All our algorithms consider actual Euclidean distances rather than grid steps and are easy to implement. An open source implementation is available online.

Multi-robot collision-free navigation based on reciprocal orientation

In this paper, a new method for robot navigation in dynamic environments, called the reciprocal orientation algorithm, is introduced. This algorithm deals with the case in which each robot moves without direct communication with the other robots. The algorithm suggested in this paper uses the concept of reciprocal orientation which guarantees both smooth and collision-free robot trajectories. The algorithm is implemented in several simulation scenarios, some of them involving tens of robots. The deadlock problem, which occurs between two robots, has been solved by using an adequate deadlock resolution algorithm.