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

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

Enabling methodologies for optimal coverage by multiple autonomous industrial robots

University of Technology Sydney. Faculty of Engineering and Information Technology.Unlike traditional industrial robots which are purpose-built for a particular repetitive application, Autonomous Industrial Robots (AIRs) are adaptable to new operating conditions or environments. An AIR is an industrial robot, with or without a mobile platform, that has the intelligence needed to operate autonomously in a complex and unstructured environment. This intelligence includes aspects such as self-awareness, environmental awareness, and collision avoidance. In this thesis, research is focused on developing methodologies that enable multiple AIRs to perform complete coverage tasks on objects that can have complex geometric shapes while aiming to achieve optimal team objectives. For the AIRs to achieve optimal complete coverage for tasks such as grit-blasting and spray painting several problems need to be addressed. One problem is to partition and allocate the surface areas that multiple AIRs can reach. Another problem is to find a set of appropriate base placements for each AIR and to determine the visiting sequence of the base placements such that complete coverage is obtained. Uncertainties in base placements, due to sensing and localization errors, need to be accounted for if necessary. Coverage path planning, i.e. generating the AIRs’ end-effector path, is another problem that needs to be addressed. Coverage path planning needs to be adaptable with respect to dynamic obstacles and unexpected changes. In solving these problems, it is vital for the AIRs to optimize the team's objectives while accounting for relevant constraints. This research develops new methodologies to address the above problems, including (1) a Voronoi partitioning based approach for simultaneous area partitioning and allocation utilizing Voronoi partitioning and multi-objective optimization; (2) optimization-based methods for multi-AIR base placements with uncertainties; and (3) a prey-predator behaviour-based algorithm for adaptive and efficient real-time coverage path planning, which accounts for stationary or dynamic obstacles and unexpected changes in the coverage area. Real-world and simulated experiments have been carried out to verify the proposed methodologies. Various comparative studies are presented against existing methods. The results show that the proposed methodologies enable effective and efficient complete coverage by the AIRs

Keep Rollin' - Whole-Body Motion Control and Planning for Wheeled Quadrupedal Robots

We show dynamic locomotion strategies for wheeled quadrupedal robots, which combine the advantages of both walking and driving. The developed optimization framework tightly integrates the additional degrees of freedom introduced by the wheels. Our approach relies on a zero-moment point based motion optimization which continuously updates reference trajectories. The reference motions are tracked by a hierarchical whole-body controller which computes optimal generalized accelerations and contact forces by solving a sequence of prioritized tasks including the nonholonomic rolling constraints. Our approach has been tested on ANYmal, a quadrupedal robot that is fully torque-controlled including the non-steerable wheels attached to its legs. We conducted experiments on flat and inclined terrains as well as over steps, whereby we show that integrating the wheels into the motion control and planning framework results in intuitive motion trajectories, which enable more robust and dynamic locomotion compared to other wheeled-legged robots. Moreover, with a speed of 4 m/s and a reduction of the cost of transport by 83 % we prove the superiority of wheeled-legged robots compared to their legged counterparts.Comment: IEEE Robotics and Automation Letter

A Hierarchical Extension of the D ∗ Algorithm

In this paper a contribution to the practice of path planning using a new hierarchical extension of the D ∗ algorithm is introduced. A hierarchical graph is stratified into several abstraction levels and used to model environments for path planning. The hierarchical D∗ algorithm uses a downtop strategy and a set of pre-calculated trajectories in order to improve performance. This allows optimality and specially lower computational time. It is experimentally proved how hierarchical search algorithms and on-line path planning algorithms based on topological abstractions can be combined successfully