Path Planning Of Autonomous Mobile Robot Research Articles

Application of Object Grasping Using Dual-Arm Autonomous Mobile Robot—Path Planning by Spline Curve and Object Recognition by YOLO—

In the trash-collection challenge of the Nakanoshima Robot Challenge, an autonomous robot must collect trash (bottles, cans, and bentos) scattered in a defined area within a time limit. A method for collecting the trash is to use machine learning to recognize the objects, move to the target location, and grasp the objects. An autonomous robot can achieve the target position and posture by rotating on the spot at the starting point, moving in a straight line, and rotating on the spot at the destination, but the rotation requires stopping and starting. To achieve faster movement, we implemented a smooth movement approach without sequential stops using a spline curve. When using the training data previously generated by the authors in their laboratory for object recognition, the robot could not correctly recognize objects in the environment of the robot competition, where strong sunlight shines through glass, because of the varying brightness and darkness. To solve this problem, we added our newly generated training data to YOLO, an image-recognition algorithm based on deep learning, and performed machine learning to achieve object recognition under various conditions.

A modified Q-learning path planning approach using distortion concept and optimization in dynamic environment for autonomous mobile robot

Autonomous mobile robot path planning in unknown and dynamic environment is a crucial task for successful mobile robot navigation. This study proposes an improved Q-learning (IQL) algorithm to address the challenges of path planning in such environments. To this end, three different modes are introduced into the IQL algorithm, namely the normal mode, the distortion mode, and the optimization mode. The normal mode operates according to the standard Q-learning procedures. The distortion mode distorts the Q-values of states around dynamic obstacles to facilitate avoidance, while the optimization mode is employed to overcome the local minimum problem. The efficacy of the IQL algorithm is assessed through a series of comparative studies involving fourteen navigation environments, each with distinct obstacle layouts and types. Comparative analyses are performed based on several metrics, including computational time, travelled distance, collision rate, and success rate. The proposed IQL algorithm exhibits a lower collision rate and a higher success rate when compared to dynamic window approach, influence zone and inflated A*.

A fuzzy based local minima avoidance path planning in autonomous robots

Autonomous mobile robot path planning is considered as a complex task in unknown, highly uncertain and dynamic environments. The presence of diverse and uncertain objects present in the environment makes the autonomous navigation of a mobile robot a difficult and expensive task. Artificial potential field (APF) provides a good and easy solution to the path planning problem resulting in a collision free navigation of a mobile robot from source to destination. One of the inherent problems in APF based path planning is the Local Minima problem. This paper provides a fuzzy based approach to path planning in addition to steering a robot out of local minima configuration with a comparative analysis of some non-fuzzy approaches.

Autonomous mobile robot path planning in unknown dynamic environments using neural dynamics

In this paper, a novel variant of bio-inspired planning algorithms is presented for robot collision-free path planning in dynamic environments without prior information. The first contribution of this paper is that, with mild technical analysis, the traditional neural dynamic model almost always returns a sub-optimal choice in some challenging scenarios, such as the boundary map and the narrow pathway map. Second, the proposed planning algorithm, namely the padding mean neural dynamic model, is a topologically organized network with connections among neighbouring neurons and is good for spreading nerve impulses such as a waves without coupling effects. The signal transduction method within a network is based on a dynamic neural activity field, which propagates high neural activity from the target state to the whole field, excluding obstacle regions. Third, simulation studies are conducted to compare the performance of the proposed planning algorithm and other popular planning algorithms in terms of effectiveness and efficiency. As a result, the proposed method can drive a robot to find more reasonable paths in both static maps and unknown dynamic scenarios with moving obstacles and a moving target. Finally, the novel excitatory input design of the proposed algorithm is discussed and analysed to explore the neural stimulus propagation mechanism within the network.

A Review on Autonomous Mobile Robot Path Planning Algorithms

The emerging trend of modern industry automation requires intelligence to be embedded into mobile robot for ensuring optimal or near-optimal solutions to execute certain task. This yield to a lot of improvement and suggestions in many areas related to mobile robot such as path planning. The purpose of this paper is to review the mobile robots path planning problem, optimization criteria and various methodologies reported in the literature for global and local mobile robot path planning. In this paper, commonly use classical approaches such as cell decomposition (CD), roadmap approach (RA), artificial potential field (AFP), and heuristics approaches such as genetic algorithm (GA), particle swarm optimization (PSO) approach and ant colony optimization (ACO) method are considered.It is observed that when it comes to dynamic environment where most of the information are unknown to the mobile robots before starting, heuristics approaches are more popular and widely used compared to classical approaches since it can handle uncertainty, interact with objects and making quick decision. Finally, few suggestions for future research work in this field are addressed at the end of this paper.

Path Planning of an Autonomous Mobile Robot using Enhanced Bacterial Foraging Optimization Algorithm

This paper describes the problem of online autonomous mobile robot path planning, which is consisted of finding optimal paths or trajectories for an autonomous mobile robot from a starting point to a destination across a flat map of a terrain, represented by a 2-D workspace. An enhanced algorithm for solving the problem of path planning using Bacterial Foraging Optimization algorithm is presented. This nature-inspired metaheuristic algorithm, which imitates the foraging behavior of E-coli bacteria, was used to find the optimal path from a starting point to a target point. The proposed algorithm was demonstrated by simulations in both static and dynamic different environments. A comparative study was evaluated between the developed algorithm and other two state-of-the-art algorithms. This study showed that the proposed method is effective and produces trajectories with satisfactory results.

Path Planning of an Autonomous Mobile Robot using Directed Artificial Bee Colony Algorithm

paper describes the problem of offline autonomous mobile robot path planning, which is consist of generating optimal paths or trajectories for an autonomous mobile robot from a starting point to a destination across a flat map of a terrain, represented by a 2-D workspace. An improved algorithm for solving the problem of path planning using Artificial Bee Colony algorithm is presented. This nature- inspired metaheuristic algorithm, which imitates the foraging behavior of bees around their hive, is used to find the optimal path from a starting point to a target point. The proposed algorithm is demonstrated by simulations in three different environments. A comparative study is evaluated between the developed algorithm, the original ABC and other two state-of- the-art algorithms. This study shows that the proposed method is effective and gets trajectories with satisfactory results.

Effective improved artificial potential field-based regression search method for autonomous mobile robot path planning

This paper presents an effective improved artificial potential field-based regression search (improved APF-based RS) method that can obtain a better and shorter path efficiently without local minima and oscillations in an environment including known, partially known or unknown, static, and dynamic environments. We redefine potential functions to eliminate oscillations and local minima problems, and use improved wall-following methods for the robots to escape non-reachable target problems. Meanwhile, we develop a regression search method to optimise the planned path. The optimisation path is calculated by connecting the sequential points produced by improved APF. The simulations demonstrate that the improved APF method easily escapes from local minima, oscillations, and non-reachable target problems. Moreover, the simulation results confirm that our proposed path planning approach can calculate a shorter or more nearly optimal than the general APF can. Results prove our improved APF-based RS method’s feasi...

New Potential Functions with Random Force Algorithms Using Potential Field Method

Autonomous mobile robot path planning is a common topic for robotics and computational geometry. Many important results have been found, but a lot of issues are still veiled. This paper first describes new problem of symmetrically aligned robot-obstacle-goal (SAROG) when using potential field methods for mobile robot path planning. In addition, we consider constant robot speed for practical use. The SAROG and the constant speed involve two potential risks: robot-obstacle collision and local minima trap. For dealing with the two potential risks, we analyze the conditions of the collision and the local minima trap, and propose new potential functions and random force based algorithms. For the algorithm verification, we use WiRobot X80 with three ultrasonic range sensor modules.

Multiple Objective Genetic Algorithms for Path-planning Optimization in Autonomous Mobile Robots

This paper describes the use of a genetic algorithm (GA) for the problem of offline point-to-point autonomous mobile robot path planning. The problem consists of generating “valid” paths or trajectories, for an Holonomic Robot to use to move from a starting position to a destination across a flat map of a terrain, represented by a two-dimensional grid, with obstacles and dangerous ground that the Robot must evade. This means that the GA optimizes possible paths based on two criteria: length and difficulty. First, we decided to use a conventional GA to evaluate its ability to solve this problem (using only one criteria for optimization). Due to the fact that we also wanted to optimize paths under two criteria or objectives, then we extended the conventional GA to implement the ideas of Pareto optimality, making it a multi-objective genetic algorithm (MOGA). We describe useful performance measures and simulation results of the conventional GA and of the MOGA that show that both types of GAs are effective tools for solving the point-to-point path-planning problem.