UAV assistance paradigm: State-of-the-art in applications and challenges

A constrained A* approach towards optimal path planning for an unmanned surface vehicle in a maritime environment containing dynamic obstacles and ocean currents

This paper proposes an improved ant colony algorithm to achieve efficient searching capabilities of path planning in complicated maps for mobile robot. The improved ant colony algorithm uses the characteristics of A* algorithm and MAX-MIN Ant system. Firstly, the grid environment model is constructed. The evaluation function of A* algorithm and the bending suppression operator are introduced to improve the heuristic information of the Ant colony algorithm, which can accelerate the convergence speed and increase the smoothness of the global path. Secondly, the retraction mechanism is introduced to solve the deadlock problem. Then the MAX-MIN ant system is transformed into local diffusion pheromone and only the best solution from iteration trials can be added to pheromone update. And, strengths of the pheromone trails are effectively limited for avoiding premature convergence of search. This gives an effective improvement and high performance to ACO in complex tunnel, trough and baffle maps and gives a better result as compare to traditional versions of ACO. The simulation results show that the improved ant colony algorithm is more effective and faster.

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Mobile Robot Path Planning Based on Ant Colony Algorithm With A* Heuristic Method.

Advancing an efficient coverage path planning in robots set up for application such as cleaning, painting and mining are becoming more crucial. Such drive in the coverage path planning field proposes numerous techniques over the past few decades. However, the proposed approaches were only applied and tested with a fixed morphological robot in which the coverage performance was significantly degraded in a complex environment. To this end, an A-star based zigzag global planner for a novel self-reconfigurable Tetris inspired cleaning robot (hTetro) presented in this paper. Unlike the traditional A-star algorithm, the presented approach can generate waypoints in order to cover the narrow spaces while assuming appropriate morphology of the hTtero robot with the objective of maximizing the coverage area. We validated the efficiency of the proposed planning approach in the Robot Operation System (ROS) Based simulated environment and tested with the hTetro robot in real-time under the controlled scenarios. Our experiments demonstrate the efficiency of the proposed coverage path planning approach resulting in superior area coverage performance in all considered experimental scenarios.

Modified A-Star Algorithm for Efficient Coverage Path Planning in Tetris Inspired Self-Reconfigurable Robot with Integrated Laser Sensor.

The unmanned aerial vehicle (UAV) route planning problem mainly centralizes on the process of calculating the best route between the departure point and target point as well as avoiding obstructions on route to avoid collisions within a given flight area A highly efficient route planning approach is required for this complex high dimensional optimization problem However, many algorithms are infeasible or have low efficiency, particularly in the complex three-dimensional (3d) flight environment In this paper, a modified sparrow search algorithm named CASSA has been presented to deal with this problem Firstly, the 3d task space model and the UAV route planning cost functions are established, and the problem of route planning is transformed into a multi-dimensional function optimization problem Secondly, the chaotic strategy is introduced to enhance the diversity of the population of the algorithm, and an adaptive inertia weight is used to balance the convergence rate and exploration capabilities of the algorithm Finally, the Cauchy-Gaussian mutation strategy is adopted to enhance the capability of the algorithm to get rid of stagnation The results of simulation demonstrate that the routes generated by CASSA are preferable to the sparrow search algorithm (SSA), particle swarm optimization (PSO), artificial bee colony (ABC), and whale optimization algorithm (WOA) under the identical environment, which means that CASSA is more efficient for solving UAV route planning problem when taking all kinds of constraints into consideration

https://www.mdpi.com/1424-8220/21/4/1224/pdf

A Modified Sparrow Search Algorithm with Application in 3d Route Planning for UAV.

Path Planning with Modified a Star Algorithm for a Mobile Robot

The main idea of proposed paper is to specify and verify the segmentation method (or a group of segmentation techniques) ideal for tracking a moving object in a scene. The object is represented by an animal (guinea pig) in a laboratory cage. Object tracking based on visual system uses and compares different techniques of segmentation: global thresholding, adaptive thresholding, differential operators used for motion detection in a video sequence and color matching algorithm implemented from industrial applications. Animal activity is a significant marker corresponding with health status. Trajectory of an animal is a good quantitative parameter for evaluation of its activity. In our case, animal tracking was used for induced gastrointestinal diseases (gastric ulcer) in the group of guinea pigs. The results serve as a supporting tool in pharmacology and pathological physiology to set optimal therapy and medication for selected groups of diseases and also help to understand the mechanisms of diseases in the medical research. Experimental measurements were taken in the large database of healthy (reference) animals and “non-healthy” ones. The visual system-based animal (object) tracking could be used as a non-expensive and flexible (reconfigurable) replacement of radio-frequency identifiers. The paper also discusses the possibility to use introduced methods in real-time applications.

Visual system-based object tracking using image segmentation for biomedical applications

Machine vision application in animal trajectory tracking

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Peter Beňo

Papers

Path Planning with Modified a Star Algorithm for a Mobile Robot

Visual system-based object tracking using image segmentation for biomedical applications

Machine vision application in animal trajectory tracking

Evaluation Criteria for Trajectories of Robotic Arms

RGBD mapping solution for low-cost robot