Intuitive haptic control surface for mobile robot motion control

Haptic human-machine interfaces and similar techniques to enhancing human-robotic interaction offer significant potential over conventional approaches. This work considers achieving intuitive motion control of a tracked mobile robotic platform utilising a 3D virtual haptic cone. The 3D haptic cone extends upon existing approaches by introducing of a third dimension to the haptic control surface. It is suggested that this approach improves upon existing methods by providing the human operator with an intuitive method for issuing vehicle motion commands whilst still facilitating simultaneous real-time haptic augmentation regarding the task at hand. The presented approach is considered in the context of mobile robotic teleoperation however offers potential across many applications. Using the 2D haptic control surface as a benchmark, preliminary evaluation of the 3D haptic cone approach demonstrates a significant improvement in the ability to command the robot to cease motion.

Intuitive haptic control surface for mobile robot motion control

Haptic human-machine interfaces and similar techniques to enhancing human-robotic interaction offer significant potential over conventional approaches. This work considers achieving intuitive motion control of a tracked mobile robotic platform utilising a 3D virtual haptic cone. The 3D haptic cone extends upon existing approaches by introducing of a third dimension to the haptic control surface. It is suggested that this approach improves upon existing methods by providing the human operator with an intuitive method for issuing vehicle motion commands whilst still facilitating simultaneous real-time haptic augmentation regarding the task at hand. The presented approach is considered in the context of mobile robotic teleoperation however offers potential across many applications. Using the 2D haptic control surface as a benchmark, preliminary evaluation of the 3D haptic cone approach demonstrates a significant improvement in the ability to command the robot to cease motion.

3D virtual haptic cone for intuitive vehicle motion control

Haptic human-machine interfaces and interaction techniques have been shown to offer advantages over conventional approaches. This work introduces the 3D virtual haptic cone with the aim of improving human remote control of a vehicle's motion. The 3D cone introduces a third dimension to the haptic control surface over existing approaches. This approach improves upon existing methods by providing the human operator with an intuitive method for issuing vehicle motion commands whilst simultaneously receiving real-time haptic information from the remote system. The presented approach offers potential across many applications, and as a case study, this work considers the approach in the context of mobile robot motion control. The performance of the approach in providing the operator with improved motion controllability is evaluated and the performance improvement determined.

Bilateral haptic teleoperation of an articulated track mobile robot

Teleoperation has been used in many applications, allowing a human operator to remotely control a robotic system in order to perform a particular task. Recently haptic teleoperation has focused mainly on improving performance in remote manipulation tasks, however the haptic approach offers similar advantages for teleoperative control of the motion of a mobile robot. This paper describes a prototype system designed to facilitate haptic teleoperation of an all-terrain, articulated track mobile robot. This system utilizes a multi-modal user interface intended to improve operator immersion, reduce operator overload and improve teleoperative task performance. The system architecture facilitates implementation of an application-specific haptic augmentation algorithm in order to improve operator performance in challenging real-world tasks. The contributions of this work can be categorized as the custom mobile platform, teleoperator interface and haptic augmentation strategy.

Multi-robot control in highly dynamic, competitive environments

The control and coordination of multiple mobile robots is a challenging task; particularly in environments with multiple, rapidly moving obstacles and agents. This paper describes a robust approach to multi-robot control, where robustness is gained from competency at every layer of robot control. The layers are: (i) a central coordination system (MAPS), (ii) an action system (AES), (iii) a navigation module, and (iv) a low level dynamic motion control system. The multi-robot coordination system assigns each robot a role and a sub-goal. Each robots action execution system then assumes the assigned role and attempts to achieve the specified sub-goal. The robots navigation system directs the robot to specific goal locations while ensuring that the robot avoids any obstacles. The motion system maps the heading and speed information from the navigation system to force-constrained motion. This multi-robot system has been extensively tested and applied in the robot soccer domain using both centralized and distributed coordination.

Hybrid robot control and SLAM for persistent navigation and mapping

For a mobile robot to operate autonomously in real-world environments, it must have an effective control system and a navigation system capable of providing robust localization, path planning and path execution. In this paper we describe the work investigating synergies between mapping and control systems. We have integrated development of a control system for navigating mobile robots and a robot SLAM system. The control system is hybrid in nature and tightly coupled with the SLAM system; it uses a combination of high and low level deliberative and reactive control processes to perform obstacle avoidance, exploration, global navigation and recharging, and draws upon the map learning and localization capabilities of the SLAM system. The effectiveness of this hybrid, multi-level approach was evaluated in the context of a delivery robot scenario. Over a period of two weeks the robot performed 1143 delivery tasks to 11 different locations with only one delivery failure (from which it recovered), travelled a total distance of more than 40km, and recharged autonomously a total of 23 times. In this paper we describe the combined control and SLAM system and discuss insights gained from its successful application in a real-world context.

Adding a receding horizon to Locally Weighted Regression for learning robot control

There have been notable advances in learning to control complex robotic systems using methods such as Locally Weighted Regression (LWR). In this paper we explore some potential limits of LWR for robotic applications, particularly investigating its application to systems with a long horizon of temporal dependence. We define the horizon of temporal dependence as the delay from a control input to a desired change in output. LWR alone cannot be used in a temporally dependent system to find meaningful control values from only the current state variables and output, as the relationship between the input and the current state is under-constrained. By introducing a receding horizon of the future output states of the system, we show that sufficient constraint is applied to learn good solutions through LWR. The new method, Receding Horizon Locally Weighted Regression (RH-LWR), is demonstrated through one-shot learning on a real Series Elastic Actuator controlling a pendulum.

Locally weighted learning methods for non-rigid robot control

This thesis develops a novel approach to robot control that learns to account for a robot's dynamic complexities while executing various control tasks using inspiration from biological sensorimotor control and machine learning. A robot that can learn its own control system can account for complex situations and adapt to changes in control conditions to maximise its performance and reliability in the real world. This research has developed two novel learning methods, with the aim of solving issues with learning control of non-rigid robots that incorporate additional dynamic complexities. The new learning control system was evaluated on a real three degree-of-freedom elastic joint robot arm with a number of experiments: initially validating the learning method and testing its ability to generalise to new tasks, then evaluating the system during a learning control task requiring continuous online model adaptation.

Robot control of a 3,500 tonne mining machine

The mining industry is highly suitable for the application of robotics and automation technology since the work is arduous, dangerous and often repetitive. This paper describes the development of an automation system for a physically large and complex field robotic system - a 3,500 tonne mining machine (a dragline). The major components of the system are discussed with a particular emphasis on the machine/operator interface. A very important aspect of this system is that it must work cooperatively with a human operator, seamlessly passing the control back and forth in order to achieve the main aim - increased productivity.

Timidity: A useful emotional mechanism for robot control?

M J Neal and J Timmis. Timidity: A useful mechanism for robot control? Informatica - special issue on perception and emotion based control, 4(27):197-204, 2003.

Knowledge engineering in robot control

N. W. Hardy, M. H. Lee, and D. P. Barnes. Knowledge engineering in robot control. In Proceedings of Expert Systems '83, pages 70-77, Cambridge, 1983.

Towards Direct Policy Search Reinforcement Learning for Robot Control

This paper proposes a high-level reinforcement learning (RL) control system for solving the action selection problem of an autonomous robot. Although the dominant approach, when using RL, has been to apply value function based algorithms, the system here detailed is characterized by the use of direct policy search methods. Rather than approximating a value function, these methodologies approximate a policy using an independent function approximator with its own parameters, trying to maximize the future expected reward. The policy based algorithm presented in this paper is used for learning the internal state/action mapping of a behavior. In this preliminary work, we demonstrate its feasibility with simulated experiments using the underwater robot GARBI in a target reaching task