Hitchhiking Based Symbiotic Multi-Robot Navigation in Sensor Networks

Information Engineering Express

Hoshino

et al. 2020

Self Cite

Service robots are expected to work in real world scenarios which are dynamic in nature. The traversable and non-traversable passages of the environment might change at different times. For example, it is common for some of the passages or areas in the map to be inaccessible due to cleaning, repair works, or other reasons. On the other hand, some passages in the environment could have a high congestion of people compared to other passages. This requires a feature to alter the path of the robots to address these dynamic changes, instead of only relying on the shortest paths criterion. To this end, this paper proposes Transient Virtual Obstacles (TVO): a method to actively block desired paths by using virtual obstacles. The virtual obstacles can be placed or removed by a user anywhere in the map for programmable intervals. The robot's plan their paths considering the virtual obstacles, thereby blocking certain paths, and planning the desired trajectories through specific paths. Another major advantage of the proposed TVO algorithm is that the existing robot path planner does not needs to be modified. The proposed TVO is tested in both simulation and real environments and results are discussed highlighting the merits of safe mobile robot navigation.

Section: State Of the Artmentioning

confidence: 99%

Transient Virtual Obstacles for Safe Robot Navigation in Indoor Environments

Information Engineering Express

Hoshino

et al. 2020

Self Cite

“…Work in Reference [17] presents a mechanism in which robots share information about their remaining battery power and accordingly avoid collision by giving priority to a robot with less battery power over the shortest path. An interesting approach of collaborative navigation through visual-servoing is presented in Reference [18,19] which heavily relies on reliable and efficient inter-robot communication to share information. The proposed work focuses on multiple robots sharing information about the dynamic changes in the remote area of the environment.…”

Section: Related Workmentioning

confidence: 99%

On Sharing Spatial Data with Uncertainty Integration Amongst Multiple Robots Having Different Maps

Hoshino

et al. 2019

Applied Sciences

Self Cite

Information sharing is a powerful feature of multi-robot systems. Sharing information precisely and accurately is important and has many benefits. Particularly, smart information sharing can improve robot path planning. If a robot finds a new obstacle or blocked path, it can share this information with other remote robots allowing them to plan better paths. However, there are two problems with such information sharing. First, the maps of the robots may be different in nature (e.g., 2D grid-map, 3D semantic map, feature map etc.) as the sensors used by the robots for mapping and localization may be different. Even the maps generated using the same sensor (e.g., Lidar) can vary in scale or rotation and the sensors used might have different specifications like resolution or range. In such scenarios, the ‘correspondence problem’ in different maps is a critical bottleneck in information sharing. Second, the transience of the obstacles has to be considered while also considering the positional uncertainty of the new obstacles while sharing information. In our previous work, we proposed a ‘node-map’ with a confidence decay mechanism to solve this problem. However, the previous work had many limitations due to the decoupling of new obstacle’s positional uncertainty and confidence decay. Moreover, the previous work applied only to homogeneous maps. In addition, the previous model worked only with static obstacles in the environment. The current work extends our previous work in three main ways: (1) we extend the previous work by integrating positional uncertainty in the confidence decay mechanism and mathematically model the transience of newly added or removed obstacles and discuss its merits; (2) we extend the previous work by considering information sharing in heterogeneous maps build using different sensors; and (3) we consider dynamic obstacles like moving people in the environment and test the proposed method in complex scenarios. All the experiments are performed in real environments and with actual robots and results are discussed.

“…The localized position of the robot is the start location, and the goal location is specified for the robot for it to navigate towards the goal using any of the state-of-the-art path planning algorithms. SLAM is considered to be a core module for autonomous mobile robots [ 7 ] because it is often a prerequisite to path planning, navigation, and manipulation for single and multi-robot systems [ 8 , 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Path Smoothing Techniques in Robot Navigation: State-of-the-Art, Current and Future Challenges

Kobayashi

et al. 2018

Sensors

Self Cite

234

110

Robot navigation is an indispensable component of any mobile service robot. Many path planning algorithms generate a path which has many sharp or angular turns. Such paths are not fit for mobile robot as it has to slow down at these sharp turns. These robots could be carrying delicate, dangerous, or precious items and executing these sharp turns may not be feasible kinematically. On the contrary, smooth trajectories are often desired for robot motion and must be generated while considering the static and dynamic obstacles and other constraints like feasible curvature, robot and lane dimensions, and speed. The aim of this paper is to succinctly summarize and review the path smoothing techniques in robot navigation and discuss the challenges and future trends. Both autonomous mobile robots and autonomous vehicles (outdoor robots or self-driving cars) are discussed. The state-of-the-art algorithms are broadly classified into different categories and each approach is introduced briefly with necessary background, merits, and drawbacks. Finally, the paper discusses the current and future challenges in optimal trajectory generation and smoothing research.