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 robot’s action execution system then assumes the assigned role and attempts to achieve the specified sub-goal. The robot’s 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.

Influence of predominant patterns of coordination on the exploitation of interaction torques in a two-joint rhythmic arm movement

In this study we investigate the coordination between rhythmic flexion-extension (FE) and supination-pronation (SP) movements at the elbow joint-complex, while manipulating the intersegmental dynamics by means of a 2-degrees of freedom (df) robot arm. We hypothesized that constraints imposed by the structure of the neuromuscular-skeletal system would (1) result in predominant pattern(s) of coordination in the absence of interaction torques and (2) influence the capabilities of participants to exploit artificially induced interaction torques. Two experiments were conducted in which different conditions of interaction torques were applied on the SP-axis as a function of FE movements. These conditions promoted different patterns of coordination between the 2-df. Control trials conducted in the absence of interaction torques revealed that both the in-phase (supination synchronized with flexion) and the anti-phase (pronation synchronized with flexion) patterns were spontaneously established by participants. The predominance of these patterns of coordination is explained in terms of the mechanical action of bi-articular muscles acting at the elbow joint-complex, and in terms of the reflexes that link the activity of the muscles involved. Results obtained in the different conditions of interaction torques revealed that those neuromuscular-skeletal constraints either impede or favor the exploitation of intersegmental dynamics depending on the context. Interaction torques were indeed found to be exploited to a greater extent in conditions in which the profiles of interaction torques favored one of the two predominant patterns of coordination (i.e., in-phase or anti-phase) as opposed to other patterns of coordination (e.g., 90 degrees or 270 degrees). Those results are discussed in relation to recent studies reporting exploitation of interaction torques in the context of rhythmic movements.

Neuromuscular-skeletal origins of predominant patterns of coordination in a rhythmic two-joint arm movement

The authors tested for predominant patterns of coordination in the combination of rhythmic flexion-extension (FE) and supination-pronation (SP) at the elbow-joint complex. Participants (N = 10) spontaneously established in-phase (supination synchronized with flexion) and antiphase (pronation synchronized with flexion) patterns. In addition, the authors used a motorized robot arm to generate involuntary SP movements with different phase relations with respect to voluntary FE. The involuntarily induced in-phase pattern was accentuated and was more consistent than other patterns. That result provides evidence that the predominance of the in-phase pattern originates in the influence of neuro-muscular-skeletal constraints rather than in a preference dictated by perceptual-cognitive factors implicated in voluntary control. Neuromuscular-skeletal constraints involved in the predominance of the in-phase and the antiphase patterns are discussed.

A global vision system for a robot soccer team

This paper describes the real time global vision system for the robot soccer team the RoboRoos. It has a highly optimised pipeline that includes thresholding, segmenting, colour normalising, object recognition and perspective and lens correction. It has a fast ‘paint’ colour calibration system that can calibrate in any face of the YUV or HSI cube. It also autonomously selects both an appropriate camera gain and colour gains robot regions across the field to achieve colour uniformity. Camera geometry calibration is performed automatically from selection of keypoints on the field. The system acheives a position accuracy of better than 15mm over a 4m × 5.5m field, and orientation accuracy to within 1°. It processes 614 × 480 pixels at 60Hz on a 2.0GHz Pentium 4 microprocessor.

Biologically inspired joint control for a humanoid robot

The GuRm is a 1.2m tall, 23 degree of freedom humanoid consuucted at the University of Queensland for research into humanoid robotics. The key challenge being addressed by the GuRw projcct is the development of appropriate learning strategies for control and coodinadon of the robot’s many joints. The development of learning strategies is Seen as a way to sidestep the inherent intricacy of modeling a multi-DOP biped robot. This paper outlines the approach taken to generate an appmpria*e control scheme for the joinis of the GuRoo. The paper demonsrrates the determination of local feedback control parameters using a genetic algorithm. The feedback loop is then augmented by a predictive modulator that learns a form of feed-fonward control to overcome the irregular loads experienced at each joint during the gait cycle. The predictive modulator is based on thc CMAC architecture. Results from tats on the GuRoo platform show that both systems provide improvements in stability and tracking of joint control.

A modified particle filter for simultaneous robot localization and landmark tracking in an indoor environment

This paper presents the implementation of a modified particle filter for vision-based simultaneous localization and mapping of an autonomous robot in a structured indoor environment. Through this method, artificial landmarks such as multi-coloured cylinders can be tracked with a camera mounted on the robot, and the position of the robot can be estimated at the same time. Experimental results in simulation and in real environments show that this approach has advantages over the extended Kalman filter with ambiguous data association and various levels of odometric noise.

UQ RoboRoos: Achieving power and agility in a small size robot

The UQ RoboRoos have been developed to participate in the RoboCup robot soccer small size league over several years. RoboCup 2001 saw a focus on the mechanical design of the RoboRoos, with the introduction of an omni-directional drive system and a high power kicker. The change in mechanical design had implications for the rest of the system particularly navigation and multi-robot planning. In addition, the overhead vision system was upgraded to improve reliability and robustness.

Hippocampal models for simultaneous localisation and mapping on an autonomous robot

To navigate successfully in a novel environment a robot needs to be able to Simultaneously Localize And Map (SLAM) its surroundings. The most successful solutions to this problem so far have involved probabilistic algorithms, but there has been much promising work involving systems based on the workings of part of the rodent brain known as the hippocampus. In this paper we present a biologically plausible system called RatSLAM that uses competitive attractor networks to carry out SLAM in a probabilistic manner. The system can effectively perform parameter self-calibration and SLAM in onedimension. Tests in two dimensional environments revealed the inability of the RatSLAM system to maintain multiple pose hypotheses in the face of ambiguous visual input. These results support recent rat experimentation that suggest current competitive attractor models are not a complete solution to the hippocampal modelling problem.