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.

Systematic changes in the duration and precision of interception in response to variation of amplitude and effector size

The results of two experiments are reported that examined how performance in a simple interceptive action (hitting a moving target) was influenced by the speed of the target, the size of the intercepting effector and the distance moved to make the interception. In Experiment 1, target speed and the width of the intercepting manipulandum (bat) were varied. The hypothesis that people make briefer movements, when the temporal accuracy and precision demands of the task are high, predicts that bat width and target speed will divisively interact in their effect on movement time (MT) and that shorter MTs will be associated with a smaller temporal variable error (VE). An alternative hypothesis that people initiate movement when the rate of expansion (ROE) of the target's image reaches a specific, fixed criterion value predicts that bat width will have no effect on MT. The results supported the first hypothesis: a statistically reliable interaction of the predicted form was obtained and the temporal VE was smaller for briefer movements. In Experiment 2, distance to move and target speed were varied. MT increased in direct proportion to distance and there was a divisive interaction between distance and speed; as in Experiment 1, temporal VE was smaller for briefer movements. The pattern of results could not be explained by the strategy of initiating movement at a fixed value of the ROE or at a fixed value of any other perceptual variable potentially available for initiating movement. It is argued that the results support pre-programming of MT with movement initiated when the target's time to arrival at the interception location reaches a criterion value that is matched to the pre-programmed MT. The data supported completely open-loop control when MT was less than between 200 and 240 ms with corrective sub-movements increasingly frequent for movements of longer duration.

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.

Improved joint control using a genetic algorithm for a humanoid robot

This paper describes experiments conducted in order to simultaneously tune 15 joints of a humanoid robot. Two Genetic Algorithm (GA) based tuning methods were developed and compared against a hand-tuned solution. The system was tuned in order to minimise tracking error while at the same time achieve smooth joint motion. Joint smoothness is crucial for the accurate calculation of online ZMP estimation, a prerequisite for a closedloop dynamically stable humanoid walking gait. Results in both simulation and on a real robot are presented, demonstrating the superior smoothness performance of the GA based methods.

Determining the specification of a control system from that of its environment

Well understood methods exist for developing programs from given specifications. A formal method identifies proof obligations at each development step: if all such proof obligations are discharged, a precisely defined class of errors can be excluded from the final program. For a class of closed systems such methods offer a gold standard against which less formal approaches can be measured. For open systems -those which interact with the physical world- the task of obtaining the program specification can be as challenging as the task of deriving the program. And, when a system of this class must tolerate certain kinds of unreliability in the physical world, it is still more challenging to reach confidence that the specification obtained is adequate. We argue that widening the notion of software development to include specifying the behaviour of the relevant parts of the physical world gives a way to derive the specification of a control system and also to record precisely the assumptions being made about the world outside the computer.