Pursuit Algorithm for Robot Trash Can Based on Fuzzy-Cell Decomposition

Scooby Smart Trash can is a trash can equipped with artificial intelligence algorithms that is able to capture and clean up garbages thrown by people who do not care about the environment. The can is called smart because it acts like scoobydoo in a children's cartoon in that the can will react if there is garbage thrown and it catches and cleans them up. This paper presents pursuit algorithm that uses cell decomposition algorithm in which algorithms are used to create a map of the robot's path and fuzzy algorithm as one of the artificial intelligence algorithm for robot path planning. By using the combined algorithms, the robot is able to pursuit and chases the trash carelessly discarded, but it has not been able to find the shortest distance. Therefore, this paper considers a second modification of the algorithm by adding a potential field algorithm used to add weight values on the map, so that the robot can pursue trash by finding the shortest path. The proposed algorithm shows that the robot can avoid obstacles and find the shortest path so that the time required to get to the destination point is fast

Simplified and Smoothed Rapidly-Exploring Random Tree Algorithm for Robot Path Planning

Rapidly-exploring Random Tree (RRT) is a prominent algorithm with quite successful results in achieving the optimal solution used to solve robot path planning problems. The RRT algorithm works by creating iteratively progressing random waypoints from the initial waypoint to the goal waypoint. The critical problem in the robot movement is the movement and time costs caused by the excessive number of waypoints required to be able to reach the goal, which is why reducing the number of waypoints created after path planning is an important process in solving the robot path problem. Ramer-Douglas-Peucker (RDP) is an effective algorithm to reduce waypoints. In this study, the Waypoint Simplified and Smoothed RRT Method (WSS-RRT) is proposed which reduces the distance costs between 8.13% and 13.36% by using the RDP algorithm to reduce the path into the same path with fewer waypoints, which is an array of waypoints created by the RRT algorithm

A generalized laser simulator algorithm for mobile robot path planning with obstacle avoidance

This paper aims to develop a new mobile robot path planning algorithm, called generalized laser simulator (GLS), for navigating autonomously mobile robots in the presence of static and dynamic obstacles. This algorithm enables a mobile robot to identify a feasible path while finding the target and avoiding obstacles while moving in complex regions. An optimal path between the start and target point is found by forming a wave of points in all directions towards the target position considering target minimum and border maximum distance principles. The algorithm will select the minimum path from the candidate points to target while avoiding obstacles. The obstacle borders are regarded as the environment’s borders for static obstacle avoidance. However, once dynamic obstacles appear in front of the GLS waves, the system detects them as new dynamic obstacle borders. Several experiments were carried out to validate the effectiveness and practicality of the GLS algorithm, including path-planning experiments in the presence of obstacles in a complex dynamic environment. The findings indicate that the robot could successfully find the correct path while avoiding obstacles. The proposed method is compared to other popular methods in terms of speed and path length in both real and simulated environments. According to the results, the GLS algorithm outperformed the original laser simulator (LS) method in path and success rate. With application of the all-direction border scan, it outperforms the A-star (A*) and PRM algorithms and provides safer and shorter paths. Furthermore, the path planning approach was validated for local planning in simulation and real-world tests, in which the proposed method produced the best path compared to the original LS algorithm

Trajectory Planning Algorithms in Two-Dimensional Environment with Obstacles

This article proposes algorithms for planning and controlling the movement of a mobile robot in a two-dimensional stationary environment with obstacles. The task is to reduce the length of the planned path, take into account the dynamic constraints of the robot and obtain a smooth trajectory. To take into account the dynamic constraints of the mobile robot, virtual obstacles are added to the map to cover the unfeasible sectors of the movement. This way of accounting for dynamic constraints allows the use of map-oriented methods without increasing their complexity. An improved version of the rapidly exploring random tree algorithm (multi-parent nodes RRT – MPN-RRT) is proposed as a global planning algorithm. Several parent nodes decrease the length of the planned path in comprise with the original one-node version of RRT. The shortest path on the constructed graph is found using the ant colony optimization algorithm. It is shown that the use of two-parent nodes can reduce the average path length for an urban environment with a low building density. To solve the problem of slow convergence of algorithms based on random search and path smoothing, the RRT algorithm is supplemented with a local optimization algorithm. The RRT algorithm searches for a global path, which is smoothed and optimized by an iterative local algorithm. The lower-level control algorithms developed in this article automatically decrease the robot’s velocity when approaching obstacles or turning. The overall efficiency of the developed algorithms is demonstrated by numerical simulation methods using a large number of experiments