Parameter Adaptive Sliding Mode Trajectory Tracking Strategy with Initial Value Identification for the Swing in a Hydraulic Construction Robot

A novel trajectory tracking strategy is developed for a double actuated swing in a hydraulic construction robot. Specifically, a nonlinear hydraulic dynamics model of a double actuated swing is established, and a robust adaptive control strategy is designed to enhance the trajectory tracking performance. When an object is grabbed and unloaded, the inertia of a swing considerably changes, and the performance of the estimation algorithm is generally inadequate. Thus, it is necessary to establish an algorithm to identify the initial value of the moment of inertia of the object. To this end, this paper proposes a novel initial value identification algorithm based on a two-DOF robot gravity force identification method combined with computer vision information. The performance of the identification algorithm is enhanced. Simulations and experiments are performed to verify the effect of the novel control scheme.

Improved Q -Learning Method for Multirobot Formation and Path Planning with Concave Obstacles

Aiming at the formation and path planning of multirobot systems in an unknown environment, a path planning method for multirobot formation based on improved Q -learning is proposed. Based on the leader-following approach, the leader robot uses an improved Q -learning algorithm to plan the path and the follower robot achieves a tracking strategy of gravitational potential field (GPF) by designing a cost function to select actions. Specifically, to improve the Q-learning, Q -value is initialized by environmental guidance of the target’s GPF. Then, the virtual obstacle-filling avoidance strategy is presented to fill non-obstacles which is judged to tend to concave obstacles with virtual obstacles. Besides, the simulated annealing (SA) algorithm whose controlling temperature is adjusted in real time according to the learning situation of the Q -learning is applied to improve the action selection strategy. The experimental results show that the improved Q -learning algorithm reduces the convergence time by 89.9% and the number of convergence rounds by 63.4% compared with the traditional algorithm. With the help of the method, multiple robots have a clear division of labor and quickly plan a globally optimized formation path in a completely unknown environment.

Morph Composition Matters in the Gouldian Finch (Chloebia gouldiae): Involvement of Red-Headed Birds Increases Vigilance

Animals invest in costly vigilance to detect threats. Joining groups reduces these costs, which can be further reduced in mixed-species assemblages. In colour-polymorphic species, morphs often experience different predation pressure and vary in a variety of traits. However, little is known about differences in vigilance or how group composition affects vigilance. The aim was to investigate whether higher conspicuousness increased vigilance and whether vigilance was reduced in mixed-morph groups like in mixed-species assemblages. I tested vigilance in the colour-polymorphic Gouldian Finch (Chloebia gouldiae). Same sex pairs of different age and of either pure (red-red or black-black) or mixed head colour were exposed to three contexts (familiar, changed and novel environment) and head movements were recorded. All birds reduced the frequency of head movements with increasing novelty, indicating different vigilance strategies (switching from a searching to a tracking strategy) depending on the situation. While vigilance did not differ between morphs, morph composition mattered. Black-headed pairs made fewer head movements than mixed-head colour pairs. Results indicated that conspicuousness did not affect vigilance, possibly due to existing adaptations to reduce predation risk. However, whenever red-headed birds were involved, vigilance increased either because of higher group conspicuousness or prevalence of aggression.