This paper presents a mixed‐integer model predictive controller for walking. In the proposed scheme, mixed‐integer quadratic programs (MIQP) are solved online to simultaneously decide center of mass jerks, footsteps positions, durations, and rotations while respecting actuation, geometry, and contact constraints. Most walking controllers require preplanned footstep rotations to avoid dealing with the nonlinearity introduced by foot rotation decision. The main contribution of this work is an optimization formulation where feet rotations are automatically planned to attain a reference speed rotation. Finally, simulation results are shown to present and discuss the capabilities of the proposed formulation.
Controlling a humanoid robot with its typical many degrees of freedom is a complex task, and many methods have been proposed to solve the problem of humanoid locomotion. In this work, we generate a gait for a Hitec Robonova-I robot using a model-free approach, where fairly simple parameterized models, based on truncated Fourier series, are applied to generate joint angular trajectories. To find a parameter set that generates a fast and stable walk, optimization algorithms were used, specifically a genetic algorithm and particle swarm optimization. The optimization process was done in simulation first, and the learned walk was then adapted to the real robot. The simulated model of the Robonova-I was made using the USARSim simulator, and tests made to evaluate the resulting walks verified that the best walk obtained is faster than the ones publicly available for the Robonova-I. Later, to provide an additional validation, the same process was carried out for the simulated Nao from the RoboCup 3D Soccer Simulation League. Again, the resulting walk was fast and stable, overcoming the speed of the publicly available magma-AF base team.
Drones can play a game-changing role in reducing both cost and time in the context of last-mile deliveries. This paper addresses the last-mile delivery problem from a complex system viewpoint, where the collective performance of the drones is investigated. We consider a last-mile delivery system with a tradable permit model (TPM) for airspace use. Typically, in other research works regarding lastmile delivery drones, a fully cooperative centralized scenario is contemplated. In our approach, due to the TPM, the agents (i.e. drones) need to compete for airspace permits in a distributed manner. We simulate the system and evaluate how different parameters, such as the arrival rate and airspace dimensions, impact the system behavior in terms of the cost and time needed by the drones to acquire flight permits, and the airspace utilization. We use a simplified simulation model, where the agents' strategies are naïve, and the drones' flight dynamics are not accounted for. Nevertheless, the simulation's level of detail is adequate for capturing interesting properties from the agents' collective behavior, as our results support. The obtained results show that the system's performance is satisfactory, even with naïve agents and under high traffic conditions. Moreover, a real-world implementation of our competitive decentralized approach would lead to advantages, such as fast permit transactions, simple computational infrastructures, and error resilience.
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