Robots in industry : a shift towards autonomous and intelligent systems in the digital age

Robotics and autonomous systems (RAS) are playing an increasingly important role across a wide range of industries and applications. Certainly, the definition of robots have expanded drastically and is no longer used to describe only traditional preprogrammed manipulators that have been commonplace in many manufacturing areas. The world is beginning to shift to an era where robots possess higher levels of autonomy, intelligence and adaptability, enabling them to cope with dynamic and challenging environments. This has created an excellent opportunity for smarter robotic systems to be exploited for applications in industrial environments. In this note, we discuss the emerging shift of robotics and its implications in the digital age. We focus particularly on applications within industrial settings, giving our perspectives on future advancements with people and automation working together as an underlying theme

Planning of spatially-oriented locomotion following focal brain damage in humans: A pilot study

Motor impairments in human gait following stroke or focal brain damage are well documented. Here, we investigated whether stroke and/or focal brain damage also affect the navigational component of spatially oriented locomotion. Ten healthy adult participants and ten adult brain-damaged patients had to walk towards distant targets from different starting positions (with vision or blindfolded). No instructions as to which the path to follow were provided to them. We observed very similar geometrical forms of paths across the two groups of participants and across visual conditions. This spatial stereotypy of whole-body displacements was observed following brain damage, even in the most severely impaired (hemiparetic) patients. This contrasted with much more variability at the temporal level. In particular, healthy participants and non-hemiparetic patients varied their walking speed according to curvature changes along the path. On the contrary, the walking speed profiles were not stereotypical and were not systematically constrained by path geometry in hemiparetic patients where it was associated with different stepping behaviors. These observations confirm the dissociation between cognitive and motor aspects of gait recovery post-stroke. The impact of these findings on the understanding of the functional and anatomical organization of spatially-oriented locomotion and for rehabilitation purposes is discussed and contextualized in the light of recent advances in electrophysiological studies

Optimal path planning based on a multi-tree T-RRT* approach for robotic task planning in continuous cost spaces

This paper presents an integrated approach to robotic task planning in continuous cost spaces. This consists of a low-level path planning phase and a high-level Planning Domain Definition Language (PDDL)-based task planning phase. The path planner is based on a multi-tree implementation of the optimal Transition-based Rapidly-exploring Random Tree (T-RRT*) that searches the environment for paths between all pairs of configuration waypoints. A method for shortcutting paths based on cost function is also presented. The resulting minimized path costs are then passed to a PDDL planner to solve the high-level task planning problem while optimizing the overall cost of the solution plan. This approach is demonstrated on two scenarios consisting of different cost functions: obstacle clearance in a cluttered environment and elevation in a mountain environment. Preliminary results suggest that significant improvements to path quality can be achieved without significant increase to computation time when compared with a T-RRT-based implementation

Dynamic anytime task and path planning for mobile robots

The study of combined task and motion planning has mostly been concerned with feasibility planning for high-dimensional, complex manipulation problems. Instead this paper gives its attention to optimal planning for low-dimensional planning problems and introduces the dynamic, anytime task and path planner for mobile robots. The proposed approach adopts a multi-tree extension of the T-RRT* algorithm in the path planning layer and further introduces dynamic and anytime planning components to enable low-level path correction and high-level re-planning capabilities when operating in dynamic or partially-known environments. Evaluation of the planner against existing methods show cost reductions of solution plans while remaining computationally efficient, and simulated deployment of the planner validates the effectiveness of the dynamic, anytime behavior of the proposed approach

A Novel Clustering-Based Algorithm for Solving Spatially Constrained Robotic Task Sequencing Problems

The robotic task sequencing problem (RTSP) appears in various forms across many industrial applications and consists of developing an optimal sequence of motions to visit a set of target points defined in a task space. Developing solutions to problems involving complex spatial constraints remains challenging due to the existence of multiple inverse kinematic solutions and the requirements for collision avoidance. So far existing studies have been limited to relaxed RTSPs involving a small number of target points and relatively uncluttered environments. When extending existing methods to problems involving greater spatial constraints and large sets of target points, they either require substantially long planning times or are unable to obtain high-quality solutions. To this end, this article presents a clustering-based algorithm to efficiently address spatially constrained RTSPs involving several hundred to thousands of points. Through a series of benchmarks, we show that the proposed algorithm outperforms the state-of-the-art in terms of solution quality and planning efficiency for large, complex problems, achieving up to 60% reduction in task execution time and 91% reduction in computation time

Smart sensing and adaptive reasoning for enabling industrial robots with interactive human-robot capabilities in dynamic environments: a case study.

Traditional industry is seeing an increasing demand for more autonomous and flexible manufacturing in unstructured settings, a shift away from the fixed, isolated workspaces where robots perform predefined actions repetitively. This work presents a case study in which a robotic manipulator, namely a KUKA KR90 R3100, is provided with smart sensing capabilities such as vision and adaptive reasoning for real-time collision avoidance and online path planning in dynamically-changing environments. A machine vision module based on low-cost cameras and color detection in the hue, saturation, value (HSV) space is developed to make the robot aware of its changing environment. Therefore, this vision allows the detection and localization of a randomly moving obstacle. Path correction to avoid collision avoidance for such obstacles with robotic manipulator is achieved by exploiting an adaptive path planning module along with a dedicated robot control module, where the three modules run simultaneously. These sensing/smart capabilities allow the smooth interactions between the robot and its dynamic environment, where the robot needs to react to dynamic changes through autonomous thinking and reasoning with the reaction times below the average human reaction time. The experimental results demonstrate that effective human-robot and robot-robot interactions can be realized through the innovative integration of emerging sensing techniques, efficient planning algorithms and systematic designs

Making industrial robots smarter with adaptive reasoning and autonomous thinking for real-time tasks in dynamic environments: a case study.

In order to extend the abilities of current robots in industrial applications towards more autonomous and flexible manufacturing, this work presents an integrated system comprising real-time sensing, path-planning and control of industrial robots to provide them with adaptive reasoning, autonomous thinking and environment interaction under dynamic and challenging conditions. The developed system consists of an intelligent motion planner for a 6 degrees-of-freedom robotic manipulator, which performs pick-and-place tasks according to an optimized path computed in real-time while avoiding a moving obstacle in the workspace. This moving obstacle is tracked by a sensing strategy based on machine vision, working on the HSV space for color detection in order to deal with changing conditions including non-uniform background, lighting reflections and shadows projection. The proposed machine vision is implemented by an off-board scheme with two low-cost cameras, where the second camera is aimed at solving the problem of vision obstruction when the robot invades the field of view of the main sensor. Real-time performance of the overall system has been experimentally tested, using a KUKA KR90 R3100 robot

Comprehensive simulation of cooperative robotic system for advanced composite manufacturing : a case study

Composite materials are widely used because of their light weight and high strength properties. They are typically made up of multi-directional layers of high strength fibres, connected by a resin. The manufacturing of composite parts is complex, time-consuming and prone to errors. This work investigates the use of robotics in the field of composite material manufacturing, which has not been well investigated to date (particularly in simulation). Effective autonomous material transportation, accurate localization and limited material deformation during robotic grasping are required for optimum placement and lay-up. In this paper, a simulation of a proposed cooperative robotic system, which integrates an autonomous mobile robot with a fixed-base manipulator, is presented. An approach based on machine vision is adopted to accurately track the position and orientation of the fibre plies. A simulation platform with a built-in physics engine is used to simulate material deformation under gravity and external forces. This allows realistic simulation of robotic manipulation for raw materials. The results demonstrate promising features of the proposed system. A root mean square error of 9.00 mm for the estimation of the raw material position and 0.05 degrees for the fibre orientation detection encourages further research for developing the proposed robotic manufacturing system

Interfacing Toolbox for Robotic Arms with Real-Time Adaptive Behavior Capabilities

Industrial robotic systems are increasingly being used to perform tasks requiring in-loop adaptive behavior to accommodate the demands of data-driven and autonomous manufacturing in the era of Industry 4.0. Achieving effective integration and the full potential of robotic systems presents significant challenges. This paper presents a C++ language-based toolbox, developed to facilitate the integration of industrial robotic arms with server computers, sensors and actuators. The new toolbox, namely the “Interfacing Toolbox for Robotic Arms” (ITRA), is fully flexible and extensible. It is capable of controlling multiple robots simultaneously, thus providing the opportunity for sophisticated manufacturing operations to be coordinated among multiple robots. ITRA can be used to achieve fast adaptive robotic systems, with latency as low as 30ms. Moreover, ITRA is cross-platform, allowing great flexibility between different computer architectures. The paper describes the architecture of ITRA, presents all its functions and gives some application examples

Adaptive task planning and motion planning for robots in dynamic environments

Emerging applications involving a high degree of uncertainty, dynamics, variability and unpredictability have begun to impart greater complexity to tasks performed by robots. In these kinds of environments, one of the most common causes for robot failures has been linked to the inability of the underlying planner to adapt to changing conditions in the environment. While an extensive range of methods have been developed to solve static planning problems in the robotics domain, until now solving the dynamic counterparts of these problems remain mostly elusive. Motivated by these challenges, this thesis presents developments that advance the state-of-the-art in optimal task and motion planning to address the dynamic variant of common robotic planning problems. Studies into adaptive planning problems are conducted to investigate the challenges that arise when extending planning methods from offline planning to online planning. In particular, this research seeks to characterise the interactions between plan quality and computational efficiency when solving dynamic planning problems and to identify the practical considerations for implementing adaptive planning algorithms in physical systems. The contributions of this thesis are a number of fast yet practical planning techniques and methods that provide and maintain near-optimal, collision-free solutions to complex planning problems involving dynamic environments. In this thesis I first describe a case study that examines the challenges unique to dynamic motion planning through a robotic pick and place task. The observations derived from this case study inspired the development of two new methods for solving complex task planning problems. The first of these is an adaptive task and path planning framework that addresses the optimal task planning problem for mobile robots under dynamic conditions. This framework integrates a sampling-based multi-goal path planning algorithm with symbolic task planning to incrementally find high-quality task plans. Crucially, the framework supports anytime-like planning and dynamic re-planning of both tasks and low-level motions to enable fast and adaptive computation of optimal solutions. To support this, a tree pruning technique is proposed for multi-goal planning problems to substantially reduce the time and memory complexity of the planner. In the second half of this thesis, I present a highly competitive clustering-based algorithm for robotic task sequencing problems (RTSPs). Unlike existing methods, the algorithm is capable of finding near-optimal solutions for complex tasks involving hard spatial constraints. With a view towards dynamic robotic task sequencing, I go on to introduce two new concepts to the RTSP. The first is partial planning, which adopts the idea of planning-during-execution to reduce the pre-execution planning time of an algorithm for online applications. The second is the concept of dynamic RTSPs, a new sub-class of RTSPs that involve dynamically-changing problem variables. I subsequently present an adaptive algorithm for online tracking of near-optimal RTSP solutions under dynamic influences. As a pioneering work within the scope of dynamic task sequencing, I provide a quantitative evaluation of the algorithm for the purpose of benchmarking in future developments.Emerging applications involving a high degree of uncertainty, dynamics, variability and unpredictability have begun to impart greater complexity to tasks performed by robots. In these kinds of environments, one of the most common causes for robot failures has been linked to the inability of the underlying planner to adapt to changing conditions in the environment. While an extensive range of methods have been developed to solve static planning problems in the robotics domain, until now solving the dynamic counterparts of these problems remain mostly elusive. Motivated by these challenges, this thesis presents developments that advance the state-of-the-art in optimal task and motion planning to address the dynamic variant of common robotic planning problems. Studies into adaptive planning problems are conducted to investigate the challenges that arise when extending planning methods from offline planning to online planning. In particular, this research seeks to characterise the interactions between plan quality and computational efficiency when solving dynamic planning problems and to identify the practical considerations for implementing adaptive planning algorithms in physical systems. The contributions of this thesis are a number of fast yet practical planning techniques and methods that provide and maintain near-optimal, collision-free solutions to complex planning problems involving dynamic environments. In this thesis I first describe a case study that examines the challenges unique to dynamic motion planning through a robotic pick and place task. The observations derived from this case study inspired the development of two new methods for solving complex task planning problems. The first of these is an adaptive task and path planning framework that addresses the optimal task planning problem for mobile robots under dynamic conditions. This framework integrates a sampling-based multi-goal path planning algorithm with symbolic task planning to incrementally find high-quality task plans. Crucially, the framework supports anytime-like planning and dynamic re-planning of both tasks and low-level motions to enable fast and adaptive computation of optimal solutions. To support this, a tree pruning technique is proposed for multi-goal planning problems to substantially reduce the time and memory complexity of the planner. In the second half of this thesis, I present a highly competitive clustering-based algorithm for robotic task sequencing problems (RTSPs). Unlike existing methods, the algorithm is capable of finding near-optimal solutions for complex tasks involving hard spatial constraints. With a view towards dynamic robotic task sequencing, I go on to introduce two new concepts to the RTSP. The first is partial planning, which adopts the idea of planning-during-execution to reduce the pre-execution planning time of an algorithm for online applications. The second is the concept of dynamic RTSPs, a new sub-class of RTSPs that involve dynamically-changing problem variables. I subsequently present an adaptive algorithm for online tracking of near-optimal RTSP solutions under dynamic influences. As a pioneering work within the scope of dynamic task sequencing, I provide a quantitative evaluation of the algorithm for the purpose of benchmarking in future developments