Quantum robot: structure, algorithms and applications

Multi-sensory Integration in a Quantum-Like Robot Perception Model

Formalisms inspired by Quantum theory have been used in Cognitive Science for decades. Indeed, Quantum-Like (QL) approaches provide descriptive features that are inherently suitable for perception, cognition, and decision processing. A preliminary study on the feasibility of a QL robot perception model has been carried out for a robot with limited sensing capabilities. In this paper, we generalize such a model for multi-sensory inputs, creating a multidimensional world representation directly based on sensor readings. Given a 3-dimensional case study, we highlight how this model provides a com…

ClipBot: an educational, physically impaired robot that learns to walk via genetic algorithm optimization

Educational robots allow experimenting with a variety of principles from mechanics, electronics, and informatics. Here we propose ClipBot, a low-cost, do-it-yourself, robot whose skeleton is made of two paper clips. An Arduino nano microcontroller actuates two servo motors that move the paper clips. However, such mechanical configuration confers physical impairments to movement. This creates the need for and allows experimenting with artificial intelligence methods to overcome hardware limitations. We report our experience in the usage of this robot during the study week 'fascinating informati…

Fast Damage Recovery in Robotics with the T-Resilience Algorithm

Damage recovery is critical for autonomous robots that need to operate for a long time without assistance. Most current methods are complex and costly because they require anticipating each potential damage in order to have a contingency plan ready. As an alternative, we introduce the T-resilience algorithm, a new algorithm that allows robots to quickly and autonomously discover compensatory behaviors in unanticipated situations. This algorithm equips the robot with a self-model and discovers new behaviors by learning to avoid those that perform differently in the self-model and in reality. Ou…

Path planning for Robotic Mobile Fulfillment Systems

This paper presents a collection of path planning algorithms for real-time movement of multiple robots across a Robotic Mobile Fulfillment System (RMFS). Robots are assigned to move storage units to pickers at working stations instead of requiring pickers to go to the storage area. Path planning algorithms aim to find paths for the robots to fulfill the requests without collisions or deadlocks. The state-of-the-art path planning algorithms, including WHCA*, FAR, BCP, OD&ID and CBS, were adapted to suit path planning in RMFS and integrated within a simulation tool to guide the robots from their…

A distributed, plug-n-play algorithm for multi-robot applications with a priori non-computable objective functions

This paper presents a distributed algorithm applicable to a wide range of practical multi-robot applications. In such multi-robot applications, the user-defined objectives of the mission can be cast as a general optimization problem, without explicit guidelines of the subtasks per different robot. Owing to the unknown environment, unknown robot dynamics, sensor nonlinearities, etc., the analytic form of the optimization cost function is not available a priori. Therefore, standard gradient-descent-like algorithms are not applicable to these problems. To tackle this, we introduce a new algorithm…

Learning over time using a neuromorphic adaptive control algorithm for robotic arms

In this paper, we explore the ability of a robot arm to learn the underlying operation space defined by the positions (x, y, z) that the arm's end-effector can reach, including disturbances, by deploying and thoroughly evaluating a Spiking Neural Network SNN-based adaptive control algorithm. While traditional control algorithms for robotics have limitations in both adapting to new and dynamic environments, we show that the robot arm can learn the operational space and complete tasks faster over time. We also demonstrate that the adaptive robot control algorithm based on SNNs enables a fast res…

The Application of a Dendritic Cell Algorithm to a Robotic Classifier

The dendritic cell algorithm is an immune-inspired technique for processing time-dependant data. Here we propose it as a possible solution for a robotic classification problem. The dendritic cell algorithm is implemented on a real robot and an investigation is performed into the effects of varying the migration threshold median for the cell population. The algorithm performs well on a classification task with very little tuning. Ways of extending the implementation to allow it to be used as a classifier within the field of robotic security are suggested.

Gradient-based Taxis Algorithms for Network Robotics

Finding the physical location of a specific network node is a prototypical task for navigation inside a wireless network. In this paper, we consider in depth the implications of wireless communication as a measurement input of gradient-based taxis algorithms. We discuss how gradients can be measured and determine the errors of this estimation. We then introduce a gradient-based taxis algorithm as an example of a family of gradient-based, convergent algorithms and discuss its convergence in the context of network robotics. We also conduct an exemplary experiment to show how to overcome some of …

Active exploration of sensor networks from a robotics perspective

Traditional algorithms for robots who need to integrate into a wireless network often focus on one specific task. In this work we want to develop simple, adaptive and reusable algorithms for real world applications for this scenario. Starting with the most basic task for mobile wireless network nodes, finding the position of another node, we introduce an algorithm able to solve this task. We then show how this algorithm can readily be employed to solve a large number of other related tasks like finding the optimal position to bridge two static network nodes. For this we first introduce a meta-…

Ant Robotics: Covering Continuous Domains by Multi-A(ge)nt Systems

In this work we present an algorithm for covering continuous connected domains by ant-like robots with very limited capabilities. The robots can mark visited places with pheromone marks and sense the level of the pheromone in their local neighborhood. In case of multiple robots these pheromone marks can be sensed by all robots and provide the only way of (indirect) communication between the robots. The robots are assumed to be memoryless, and to have no global information such as the domain map, their own position (either absolute or relative), total marked area percentage, maximal pheromone l…

Towards semi-episodic learning for robot damage recovery

The recently introduced Intelligent Trial and Error algorithm (IT\&E) enables robots to creatively adapt to damage in a matter of minutes by combining an off-line evolutionary algorithm and an on-line learning algorithm based on Bayesian Optimization. We extend the IT\&E algorithm to allow for robots to learn to compensate for damages while executing their task(s). This leads to a semi-episodic learning scheme that increases the robot's lifetime autonomy and adaptivity. Preliminary experiments on a toy simulation and a 6-legged robot locomotion task show promising results.

Prioritized Planning Algorithms for Trajectory Coordination of Multiple Mobile Robots

An important capability of autonomous multi-robot systems is to prevent collision among the individual robots. One approach to this problem is to plan conflict-free trajectories and let each of the robots follow its pre-planned trajectory. A widely used practical method for multi-robot trajectory planning is prioritized planning, which has been shown to be effective in practice, but is in general incomplete. Formal analysis of instances that are provably solvable by prioritized planning is still missing. Moreover, prioritized planning is a centralized algorithm, which may be in many situations…

SPACE：一个用于评估去中心化多机器人任务分配算法的基于 Python 的仿真器

SPACE: A Python-based Simulator for Evaluating Decentralized Multi-Robot Task Allocation Algorithms

本文提出基于 Python 的 SPACE 仿真器，以插件、行为树 GUI、通信与局部任务感知支持去中心化 MRTA 算法评估，并通过 CBBA 与 GRAPE 的动态任务实验验证其效用。

Differentiable Algorithm Networks for Composable Robot Learning

This paper introduces the Differentiable Algorithm Network (DAN), a composable architecture for robot learning systems. A DAN is composed of neural network modules, each encoding a differentiable robot algorithm and an associated model; and it is trained end-to-end from data. DAN combines the strengths of model-driven modular system design and data-driven end-to-end learning. The algorithms and models act as structural assumptions to reduce the data requirements for learning; end-to-end learning allows the modules to adapt to one another and compensate for imperfect models and algorithms, in o…

Online Submodular Coordination with Bounded Tracking Regret: Theory, Algorithm, and Applications to Multi-Robot Coordination

We enable efficient and effective coordination in unpredictable environments, i.e., in environments whose future evolution is unknown a priori and even adversarial. We are motivated by the future of autonomy that involves multiple robots coordinating in dynamic, unstructured, and adversarial environments to complete complex tasks such as target tracking, environmental mapping, and area monitoring. Such tasks are often modeled as submodular maximization coordination problems. We introduce the first submodular coordination algorithm with bounded tracking regret, i.e., with bounded suboptimality …

Multi-robot Symmetric Rendezvous Search on the Line with an Unknown Initial Distance

In this paper, we study the symmetric rendezvous search problem on the line with n > 2 robots that are unaware of their locations and the initial distances between them. In the symmetric version of this problem, the robots execute the same strategy. The multi-robot symmetric rendezvous algorithm, MSR presented in this paper is an extension our symmetric rendezvous algorithm, SR presented in [23]. We study both the synchronous and asynchronous cases of the problem. The asynchronous version of MSR algorithm is called MASR algorithm. We consider that robots start executing MASR at different times…

Genetic algorithm for robotic telescope scheduling

This work was inspired by author experiences with a telescope scheduling. Author long time goal is to develop and further extend software for an autonomous observatory. The software shall provide users with all the facilities they need to take scientific images of the night sky, cooperate with other autonomous observatories, and possibly more. This works shows how genetic algorithm can be used for scheduling of a single observatory, as well as network of observatories.

Design and Simulation of an Autonomous Quantum Flying Robot Vehicle: An IBM Quantum Experience

The application of quantum computation and information in robotics has caught the attention of researchers off late. The field of robotics has always put its effort on the minimization of the space occupied by the robot, and on making the robot `smarter. `The smartness of a robot is its sensitivity to its surroundings and the user input and its ability to react upon them desirably. Quantum phenomena in robotics make sure that the robots occupy less space and the ability of quantum computation to process the huge amount of information effectively, consequently making the robot smarter. Braitenb…

A Preliminary Study for a Quantum-like Robot Perception Model

Formalisms based on quantum theory have been used in Cognitive Science for decades due to their descriptive features. A quantum-like (QL) approach provides descriptive features such as state superposition and probabilistic interference behavior. Moreover, quantum systems dynamics have been found isomorphic to cognitive or biological systems dynamics. The objective of this paper is to study the feasibility of a QL perception model for a robot with limited sensing capabilities. We introduce a case study, we highlight its limitations, and we investigate and analyze actual robot behaviors through …

An advantage actor-critic algorithm for robotic motion planning in dense and dynamic scenarios

Intelligent robots provide a new insight into efficiency improvement in industrial and service scenarios to replace human labor. However, these scenarios include dense and dynamic obstacles that make motion planning of robots challenging. Traditional algorithms like A* can plan collision-free trajectories in static environment, but their performance degrades and computational cost increases steeply in dense and dynamic scenarios. Optimal-value reinforcement learning algorithms (RL) can address these problems but suffer slow speed and instability in network convergence. Network of policy gradie…

Identification Algorithm to Determine the Trajectory of Robots with Singularities

Singularity in robot controls is an important problem. By identifying an appropriate trajectory for the robots, the singular situations can be avoided. In this paper an identification algorithm is proposed to control the robot such that it can change its direction to avoid the singularity situation. Base on the singular value decomposition, the proposed algorithm is developed for the non-redundant, single-rank robots. The proposed method is employed on a robot with six degrees of freedom, in order to identify its feasible trajectory. Keywords: Singularity; Trajectory identification; Robot cont…

采用自适应课程动力学随机化的强化学习用于容错机器人控制

Reinforcement Learning with Adaptive Curriculum Dynamics Randomization for Fault-Tolerant Robot Control

针对四足机器人执行器故障容错问题，提出带动力学随机化的自适应课程强化学习算法ACDR，通过hard2easy课程训练单一鲁棒策略，无需故障检测与策略切换模块，并在平均奖励和行走距离上优于传统算法。

基于密度矩阵的动力学方法用于量子机器人群

Density Matrix-based Dynamics for Quantum Robotic Swarms

本文提出利用密度矩阵表示机器人群作为混合量子态，以解决传统矩阵方法在大群规模下的计算瓶颈，并实现去中心化行为。

基于改进DARP算法的多机器人覆盖路径规划算法

A Multi-robot Coverage Path Planning Algorithm Based on Improved DARP Algorithm

针对多机器人覆盖路径规划问题，本文改进DARP算法，利用A*分配任务并结合基于Up-First的STC实现全覆盖，提升了效率与覆盖率。

Research on the mobile robots intelligent path planning based on ant colony algorithm application in manufacturing logistics

With the development of robotics and artificial intelligence field unceasingly thorough, path planning as an important field of robot calculation has been widespread concern. This paper analyzes the current development of robot and path planning algorithm and focuses on the advantages and disadvantages of the traditional intelligent path planning as well as the path planning. The problem of mobile robot path planning is studied by using ant colony algorithm, and it also provides some solving methods.

评估模拟四足机器人导航中的强化学习算法：一项受导盲犬行为启发的比较研究

Evaluating Reinforcement Learning Algorithms for Navigation in Simulated Robotic Quadrupeds: A Comparative Study Inspired by Guide Dog Behaviour

本研究比较了PPO、DQN和Q学习三种强化学习算法在模拟四足机器人导航与避障中的表现，发现PPO在所有指标上最优，为开发机器人导盲犬及医疗辅助机器人提供了依据。

Vision based range and bearing algorithm for robot swarms

This paper presents a novel computer vision the algorithm proposed for the on-line range and bearing detection of the robot swarms. Results demonstrated the reliability of the proposed vision system such that it can be used for the robot swarms applications.

异构多机器人系统聚合博弈中的分布式纳什均衡求解算法

Distributed Nash Equilibrium Seeking Algorithm in Aggregative Games for Heterogeneous Multi-Robot Systems

本文针对异构多机器人系统在聚合博弈中的纳什均衡求解问题，提出一种结合分布式优化与输出控制的算法，并通过仿真和实物实验验证其收敛性与有效性。

量子算法辅助的机器人定位

Robot localization aided by quantum algorithms

本文针对移动机器人定位计算量大的问题，提出在2D地图中利用Grover搜索算法提升定位效率，实验证明其相比AMCL等经典方法具有显著加速，为量子机器人学提供了实用方案。

k-Color Multi-Robot Motion Planning

We present a simple and natural extension of the multi-robot motion planning problem where the robots are partitioned into groups (colors), such that in each group the robots are interchangeable. Every robot is no longer required to move to a specific target, but rather to some target placement that is assigned to its group. We call this problem k-color multi-robot motion planning and provide a sampling-based algorithm specifically designed for solving it. At the heart of the algorithm is a novel technique where the k-color problem is reduced to several discrete multi-robot motion planning pro…

具有充足和有限感知能力的多机器人多目标跟踪分配算法

Assignment Algorithms for Multi-Robot Multi-Target Tracking with Sufficient and Limited Sensing Capability

针对机器人不同感知能力下的多机器人多目标跟踪分配问题，提出两种贪心算法分别解决单机器人足跟踪单目标、双机器人需跟踪单目标场景，证明其近似界与多项式时间特性，实验表明性能接近最优且优于近似界。

Sensor Network Based Collision-Free Navigation and Map Building for Mobile Robots

Safe robot navigation is a fundamental research field for autonomous robots including ground mobile robots and flying robots. The primary objective of a safe robot navigation algorithm is to guide an autonomous robot from its initial position to a target or along a desired path with obstacle avoidance. With the development of information technology and sensor technology, the implementations combining robotics with sensor network are focused on in the recent researches. One of the relevant implementations is the sensor network based robot navigation. Moreover, another important navigation probl…

Towards Optimized Distributed Multi-Robot Printing: An Algorithmic Approach

This paper presents a distributed multi-robot printing method which utilizes an optimization approach to decompose and allocate a printing task to a group of mobile robots. The motivation for this problem is to minimize the printing time of the robots by using an appropriate task decomposition algorithm. We present one such algorithm which decomposes an image into rasterized geodesic cells before allocating them to the robots for printing. In addition to this, we also present the design of a numerically controlled holonomic robot capable of spraying ink on smooth surfaces. Further, we use this…

多机器人系统的冲突感知最优目标分配算法

A Conflict-Aware Optimal Goal Assignment Algorithm for Multi-Robot Systems

本文针对多机器人目标分配中的NP难问题，提出一种冲突感知的最优目标分配算法，通过冲突引导的次优分配计算和两项路径计算优化，在基准测试中实现近一个数量级加速。

一种基于增强RRT的移动机器人动态路径规划与能量管理算法

An Enhanced RRT based Algorithm for Dynamic Path Planning and Energy Management of a Mobile Robot

本文提出一种增强RRT算法，通过持续生成到充电站的路径并结合能量需求判断充电时机，利用先前树信息优化后续路径，实现移动机器人动态路径规划与电池能量管理。

Optimal Sequential Stochastic Deployment of Multiple Passenger Robots

We present a new algorithm for deploying passenger robots in marsupial robot systems. A marsupial robot system consists of a carrier robot (e.g., a ground vehicle), which is highly capable and has a long mission duration, and at least one passenger robot (e.g., a short-duration aerial vehicle) transported by the carrier. We optimize the performance of passenger robot deployment by proposing an algorithm that reasons over uncertainty by exploiting information about the prior probability distribution of features of interest in the environment. Our algorithm is formulated as a solution to a seque…

A Predictive Application Offloading Algorithm Using Small Datasets for Cloud Robotics

Many robotic applications that are critical for robot performance require immediate feedback, hence execution time is a critical concern. Furthermore, it is common that robots come with a fixed quantity of hardware resources; if an application requires more computational resources than the robot can accommodate, its onboard execution might be extended to a degree that degrades the robot performance. Cloud computing, on the other hand, features on-demand computational resources; by enabling robots to leverage those resources, application execution time can be reduced. The key to enabling robot …

PPMC RL Training Algorithm: Rough Terrain Intelligent Robots through Reinforcement Learning

Robots can now learn how to make decisions and control themselves, generalizing learned behaviors to unseen scenarios. In particular, AI powered robots show promise in rough environments like the lunar surface, due to the environmental uncertainties. We address this critical generalization aspect for robot locomotion in rough terrain through a training algorithm we have created called the Path Planning and Motion Control (PPMC) Training Algorithm. This algorithm is coupled with any generic reinforcement learning algorithm to teach robots how to respond to user commands and to travel to designa…

Intelligent Bug Algorithm (IBA): A Novel Strategy to Navigate Mobile Robots Autonomously

This research proposed an intelligent obstacle avoidance algorithm to navigate an autonomous mobile robot. The presented Intelligent Bug Algorithm (IBA) over performs and reaches the goal in relatively less time as compared to existing Bug algorithms. The improved algorithm offers a goal oriented strategy by following smooth and short trajectory. This has been achieved by continuously considering the goal position during obstacle avoidance. The proposed algorithm is computationally inexpensive and easy to tune. The paper also presents the performance comparison of IBA and reported Bug algorith…

Visualizing Local Minima in Multi-Robot Motion Planning using Multilevel Morse Theory

Multi-robot motion planning problems often have many local minima. It is essential to visualize those local minima such that we can better understand, debug and interact with multi-robot systems. Towards this goal, we present the multi-robot motion explorer, an algorithm which extends previous results on multilevel Morse theory by introducing a component-based framework, where we reduce multi-robot configuration spaces by reducing each robots component space using fiber bundles. Our algorithm exploits this component structure to search for and visualize local minima. A user of the algorithm ca…

Long-Lived Distributed Relative Localization of Robot Swarms

This paper studies the problem of having mobile robots in a multi-robot system maintain an estimate of the relative position and relative orientation of near-by robots in the environment. This problem is studied in the context of large swarms of simple robots which are capable of measuring only the distance to near-by robots. We present two distributed localization algorithms with different trade-offs between their computational complexity and their coordination requirements. The first algorithm does not require the robots to coordinate their motion. It relies on a non-linear least squares bas…

Flying Trapeze Act Motion Planning Algorithm for Two-Link Free-Flying Acrobatic Robot

A flying trapeze act can be a challenging task for a robotics system since some act requires the performer to catch another trapeze or catcher at the end after being airborne. The objective of this paper is to design and validate a motion planning algorithm for a two-link free-flying acrobatic robot that can accurately land on another trapeze after free-flying in the air. First, the proposed algorithm plan the robot trajectory with the non-linear constrained optimization method. Then, a feedback controller is implemented to stabilize the posture. However, since the spatial position of the cent…

Robot Path Planning Algorithms: A Review of Theory and Experiment

The aim of this book is to introduce different robot path planning algorithms and suggest some of the most appropriate ones which are capable of running on a variety of robots and are resistant to disturbances. Being real-time, being autonomous, and the ability to identify high-risk areas and risk management are the other features that will be mentioned throughout these methods.

A Comparison of Various Approaches to Reinforcement Learning Algorithms for Multi-robot Box Pushing

In this paper, a comparison of reinforcement learning algorithms and their performance on a robot box pushing task is provided. The robot box pushing problem is structured as both a single-agent problem and also a multi-agent problem. A Q-learning algorithm is applied to the single-agent box pushing problem, and three different Q-learning algorithms are applied to the multi-agent box pushing problem. Both sets of algorithms are applied on a dynamic environment that is comprised of static objects, a static goal location, a dynamic box location, and dynamic agent positions. A simulation environm…

使用 Wavefront Algorithm 进行路径规划后的简单机器人选择标准

A Simple Robot Selection Criteria After Path Planning Using Wavefront Algorithm

本文提出一种在已知环境地图下选择最佳机器人的技术，通过 Wavefront Algorithm 探测地形特征，并基于五种机器人的性能与局限为其匹配任务，从而完成既定使命。

RLSS: Real-time Multi-Robot Trajectory Replanning using Linear Spatial Separations

Trajectory replanning is a critical problem for multi-robot teams navigating dynamic environments. We present RLSS (Replanning using Linear Spatial Separations): a real-time trajectory replanning algorithm for cooperative multi-robot teams that uses linear spatial separations to enforce safety. Our algorithm handles the dynamic limits of the robots explicitly, is completely distributed, and is robust to environment changes, robot failures, and trajectory tracking errors. It requires no communication between robots and relies instead on local relative measurements only. We demonstrate that the …

Mobile Robot Navigation on Partially Known Maps using a Fast A Star Algorithm Version

Mobile robot navigation in total or partially unknown environments is still an open problem. The path planning algorithms lack completeness and/or performance. Thus, there is the need for complete (i.e., the algorithm determines in finite time either a solution or correctly reports that there is none) and performance (i.e., with low computational complexity) oriented algorithms which need to perform efficiently in real scenarios. In this paper we evaluate the efficiency of two versions of the A star algorithm for mobile robot navigation inside indoor environments with the help of two software …

机器人技术、计算机视觉与算法设计的融合：一款中国扑克自主对战机器人

Integration of Robotics, Computer Vision, and Algorithm Design: A Chinese Poker Self-Playing Robot

本研究融合机械设计、计算机视觉与算法设计，打造了搭载YOLOv5检测模型与贪心算法的TM5-900机械臂系统，可自主完成中国扑克的取牌、识牌、选牌与出牌全流程。

3D Path Planning and Obstacle Avoidance Algorithms for Obstacle-Overcoming Robots

This article introduces a multimodal motion planning (MMP) algorithm that combines three-dimensional (3-D) path planning and a DWA obstacle avoidance algorithm. The algorithms aim to plan the path and motion of obstacle-overcoming robots in complex unstructured scenes. A novel A-star algorithm is proposed to combine the characteristics of unstructured scenes and a strategy to switch it into a greedy best-first strategy algorithm. Meanwhile, the algorithm of path planning is integrated with the DWA algorithm so that the robot can perform local dynamic obstacle avoidance during the movement alon…

用于超冗余9自由度机器人高精度逆运动学的白头鹰搜索算法

Bald Eagle Search Algorithm for High Precision Inverse Kinematics of Hyper-Redundant 9-DOF Robot

本文针对超冗余9自由度机器人逆运动学求解问题，比较多种元启发式优化算法，发现白头鹰搜索算法在末端执行器定位精度和位置误差方面表现最优。

Congestion control algorithms for robotic swarms with a common target based on the throughput of the target area

When a large number of robots try to reach a common area, congestions happen, causing severe delays. To minimise congestion in a robotic swarm system, traffic control algorithms must be employed in a decentralised manner. Based on strategies aimed to maximise the throughput of the common target area, we developed two novel algorithms for robots using artificial potential fields for obstacle avoidance and navigation. One algorithm is inspired by creating a queue to get to the target area (Single Queue Former -- SQF), while the other makes the robots touch the boundary of the circular area by us…

Quantum Multi-Agent Actor-Critic Neural Networks for Internet-Connected Multi-Robot Coordination in Smart Factory Management

As one of the latest fields of interest in both academia and industry, quantum computing has garnered significant attention. Among various topics in quantum computing, variational quantum circuits (VQC) have been noticed for their ability to carry out quantum deep reinforcement learning (QRL). This paper verifies the potential of QRL, which will be further realized by implementing quantum multi-agent reinforcement learning (QMARL) from QRL, especially for Internet-connected autonomous multi-robot control and coordination in smart factory applications. However, the extension is not straightforw…

Swarm Relays: Distributed Self-Healing Ground-and-Air Connectivity Chains

The coordination of robot swarms - large decentralized teams of robots - generally relies on robust and efficient inter-robot communication. Maintaining communication between robots is particularly challenging in field deployments. Unstructured environments, limited computational resources, low bandwidth, and robot failures all contribute to the complexity of connectivity maintenance. In this paper, we propose a novel lightweight algorithm to navigate a group of robots in complex environments while maintaining connectivity by building a chain of robots. The algorithm is robust to single robot …

An Advancing Ensemble with Diversified Algorithms for Robot Arm Calibration

Recently, industrial robots plays a significant role in intelligent manufacturing. Hence, it is an urgent issue to ensure the robot with the high positioning precision. To address this hot issue, a novel calibration method based on an powerful ensemble with various algorithms is proposed. This paper has two ideas: a) developing eight calibration methods to identify the kinematic parameter errors; 2) establishing an effective ensemble to search calibrated kinematic parameters. Enough experimental results show that this ensemble can achieve: 1) higher calibration accuracy for the robot; 2) model…