Reactive navigation in partially familiar planar environments using semantic perceptual feedback

This article solves the planar navigation problem by recourse to an online reactive scheme that exploits recent advances in simultaneous localization and mapping (SLAM) and visual object recognition to recast prior geometric knowledge in terms of an offline catalog of familiar objects. The resulting vector field planner guarantees convergence to an arbitrarily specified goal, avoiding collisions along the way with fixed but arbitrarily placed instances from the catalog as well as completely unknown fixed obstacles so long as they are strongly convex and well separated. We illustrate the generic robustness properties of such deterministic reactive planners as well as the relatively modest computational cost of this algorithm by supplementing an extensive numerical study with physical implementation on both a wheeled and legged platform in different settings.

Investigation of the Influence of the Parameters of the VFH + Method on the Navigation Efficiency of the Mobile Robot

The article focuses on verifying the effects of the VFH + navigation method parameters, proving to be very effective for the robot's reactive navigation. This research is based on our long-standing knowledge of histogram methods used in robot navigation. The article focuses on verifying the influence of crucial parameters - thresholds in a binary histogram, the smax parameter defining wide and narrow valleys, constants setting the criterion function, and the impact of robot dynamics on navigation. Many experiments were performed in a ROS simulation environment, and the article lists those significant confirming certain assumptions in setting these parameters.

Implementation of a novel reactive navigation algorithm

In this project a reactive navigation algorithm is applied to a non-holonomic differential drive robot. The algorithm uses a stochastic process to navigate a robot through terrain while lacking a priori information. A graph is made from a random array of points that is used to connect the current location of the robot to its destination. Dijkstra's algorithm is used to select the shortest route that leads to the destination. The robot attempts to traverse this route until it detects that it is being blocked by an obstacle. The graph is then recreated with different random points, an a new route is calculated. This procedure is repeated until the robot arrives at its destination. This is tested by making a simulated robot with perfect localization travel through two kinds of environments. Processing speed is maintained by hashing location information according to its coordinates.

Implementation of a novel reactive navigation algorithm

In this project a reactive navigation algorithm is applied to a non-holonomic differential drive robot. The algorithm uses a stochastic process to navigate a robot through terrain while lacking a priori information. A graph is made from a random array of points that is used to connect the current location of the robot to its destination. Dijkstra's algorithm is used to select the shortest route that leads to the destination. The robot attempts to traverse this route until it detects that it is being blocked by an obstacle. The graph is then recreated with different random points, an a new route is calculated. This procedure is repeated until the robot arrives at its destination. This is tested by making a simulated robot with perfect localization travel through two kinds of environments. Processing speed is maintained by hashing location information according to its coordinates.

Reactive Navigation Framework for Mobile Robots by Heuristically Evaluated Pre-sampled Trajectories

This paper describes and analyzes a reactive navigation framework for mobile robots in unknown environments. The approach does not rely on a global map and only considers the local occupancy in its robot-centered 3D grid structure. The proposed algorithm enables fast navigation by heuristic evaluations of pre-sampled trajectories on-the-fly. At each cycle, these paths are evaluated by a weighted cost function, based on heuristic features such as closeness to the goal, previously selected trajectories, and nearby obstacles. This paper introduces a systematic method to calculate a feasible pose on the selected trajectory, before sending it to the controller for the motion execution. Defining the structures in the framework and providing the implementation details, the paper also explains how to adjust its offline and online parameters. To demonstrate the versatility and adaptability of the algorithm in unknown environments, physics-based simulations on various maps are presented. Benchmark tests show the superior performance of the proposed algorithm over its previous iteration and another state-of-art method. The open-source implementation of the algorithm and the benchmark data can be found at https://github.com/RIVeR-Lab/tentabot.

Means of IoT and Fuzzy Cognitive Maps in Reactive Navigation of Ubiquitous Robots

Development of accessible and cheap sensors as well as the possibility to transfer and process huge amounts of data offer new possibilities for many areas utilizing till now conventional approaches. Navigation of robots and autonomous vehicles is no exception in this aspect and Internet of Things (IoT), together with the means of computational intelligence, represents a new way for construction and use of robots. In this paper, the possibility to move sensors from robots to their surroundings with the help of IoT is presented and the modification of the IoT concept in the form of intelligent space as well as the concept of ubiquitous robot are shown in the paper. On an example of route tracking, we will clarify the potential of distributed networked sensors and processing their data with the use of fuzzy cognitive maps for robotic navigation. Besides, two modifications of adaptation approaches, namely particle swarm optimization and migration algorithm, are presented here. A series of simulations was performed, which are discussed and future research directions are proposed.