Reactive Navigation Algorithm Research Articles

A 3D Vision Cone Based Method for Collision Free Navigation of a Quadcopter UAV among Moving Obstacles

In the near future, it’s expected that unmanned aerial vehicles (UAVs) will become ubiquitous surrogates for human-crewed vehicles in the field of border patrol, package delivery, etc. Therefore, many three-dimensional (3D) navigation algorithms based on different techniques, e.g., model predictive control (MPC)-based, navigation potential field-based, sliding mode control-based, and reinforcement learning-based, have been extensively studied in recent years to help achieve collision-free navigation. The vast majority of the 3D navigation algorithms perform well when obstacles are sparsely spaced, but fail when facing crowd-spaced obstacles, which causes a potential threat to UAV operations. In this paper, a 3D vision cone-based reactive navigation algorithm is proposed to enable small quadcopter UAVs to seek a path through crowd-spaced 3D obstacles to the destination without collisions. The proposed algorithm is simulated in MATLAB with different 3D obstacles settings to demonstrate its feasibility and compared with the other two existing 3D navigation algorithms to exhibit its superiority. Furthermore, a modified version of the proposed algorithm is also introduced and compared with the initially proposed algorithm to lay the foundation for future work.

Minimizing Driving Risk of Mobile Robots by Combining a Goal Guidance Vector Algorithm with Reactive Navigation

In the navigation of mobile robots, the driving risk can be minimized by increasing the probability of success. The algorithm, which is currently commonly known as the shortest path algorithm, performs efficiently, but does not exhibit a good probability of success for achieving the final goal. In this paper, we develop a new reactive navigation algorithm, known as the goal guidance vector (G2V), which can minimize the driving risk within the sensing range. The G2V is designed to improve the performance of the reactive navigation algorithm using a hazard cost function (HCF) that accounts for the scale and locations of the obstacles within the sensing range. We also adopt real-time fuzzy reactive control to determine the weighting factors of the HCF in an unknown environment to determine the optimal G2V. Simulations are conducted to validate the use of this approach for various environments.

Generating optimal path by level set approach for a mobile robot moving in static/dynamic environments

According to the analogy between the mobile robot navigation path and the heat transferring path under steady state, the robot path planning problem during navigation is converted into identify the heat transferring path that minimizes the thermal compliance across the analysis domain. A new path planning approach which combines the concept of growth simulation and level set based heat conduction topology optimization framework is adopted to determine the heat transferring path. By introducing the concept of growth simulation, the proposed approach could calculate a few steps of the navigation path, which is of great significance for online reactive navigation. The proposed approach could avoid local minima and search for the optimal growth orientation freely without constraints from background mesh since the inherent characteristics of heat conduction and the level set approach, respectively. A new reactive navigation algorithm based on the proposed path planning approach and the concept of temporarily safe path is proposed to navigate the mobile robot from the start point to the goal point in unknown dynamic environment with static and dynamic obstacles. Diverse simulation cases are carried to illustrate the effectiveness of the reactive navigation algorithm.

A semi-autonomous motorized mobile hospital bed for safe transportation of head injury patients in dynamic hospital environments without bed switching

SUMMARYWe present a novel motorized semi-autonomous mobile hospital bed guided by a human operator and a reactive navigation algorithm. The proposed reactive navigation algorithm is launched when the sensory device detects that the hospital bed is in the potential danger of collision. The semi-autonomous hospital bed is able to safely and quickly deliver critical neurosurgery (head trauma) patients to target locations in dynamic uncertain hospital environments such as crowded hospital corridors while avoiding en-route steady and moving obstacles. We do not restrict the nature or the motion of the obstacles, meaning that the shapes of the obstacles may be time-varying or deforming and they may undergo arbitrary motions. The only information available to the navigation system is the current distance to the nearest obstacle. Performance of the proposed navigation algorithm is verified via theoretical studies. Simulation and experimental results also confirm the performance of the reactive navigation algorithm in real world scenarios.

Rapid control prototyping for robot soccer

SUMMARYIn this paper, we propose rapid-control prototyping (RCP) for a robot soccer using the SIMTool that has been developed in Seoul National University, Korea, for the control-aided control system design (CACSD). The proposed RCP enables us to carry out the rapid design and the verification of controls for two-wheeled mobile robots (TWMRs), players in the robot soccer, without writing C codes directly and requiring a special H/W. On the basis of the proposed RCP, a blockset for the robot soccer is developed for easy design of a variety of mathematical and logical algorithms. All blocks in the blockset are made up of basic blocks offered by the SIMTool. Applied algorithms for specific purposes can be easily and efficiently constructed with just a combination of the blocks in the blockset. As one of the algorithms implemented with the developed blockset, a novel navigation algorithm, called a reactive navigation algorithm using the direction and the avoidance vectors based scheme (RNDAVS), is proposed. It is shown through simulations and experiments that the RNDAVS designed with the proposed RCP can avoid a local minima and the goal non-reachable with obstacles nearby (GNRON) arising from the existing methods. Furthermore, in order to validate the proposed RCP in a real game, we employ an official simulation game for the robot soccer, the SimuroSot. Block diagrams are constructed for strategy, path calculation, and the interface to the SIMTool. We show that the algorithms implemented with the proposed RCP work well in the simulation game.

An Intelligent Navigation Method for Service Robots in the Smart Environment

Autonomous navigation is one of primitive functionalities which service robots should have; nevertheless, the navigation problem for a service robot still has many difficulties because the real environment where service robots should works is so complex and dynamical. This paper proposes a framework of intelligent navigation for service robots based on a semantic map of the smart environment. In the smart environment, the robot can receive his position information from location sensors by sensor networks, and it can eliminate the accumulated localization errors. So the robot can accomplish confidential localization in the dynamic and complex environment. With the topological information in the semantic map, path planning problem can be simple even in the wide and complex spaces, and topological path from semantic map can be divided into several sub goals. Robust navigation can be accomplished by moving towards these sub goals with reactive navigation algorithm which has robust characteristics in the dynamic environments. Our approaches have ascertained the good performance on the localization and the navigation, and the feasibility of these methods could be confirmed with the result of experiments in the real environment.

Crowd of Virtual Humans: a New Approach for Real Time Navigation in Complex and Structured Environments

Abstract The navigation activity is an every day practice for any human being capable of locomotion. Our objective in this work is to reproduce this crucial human activity inside virtual environments. Putting together the high complexity of a realistic environment such as a city, a big amount of virtual humans and the real‐time constraint requires to optimize each aspect of the animation process. In this paper, we present a suitable topological structuring of the geometric environment to allow fast path finding as well as an efficient reactive navigation algorithm for virtual humans evolving inside a crowd.