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Achieving stable bipedal walking on surfaces with unknown motion remains a challenging control problem due to the hybrid, time-varying, partially unknown dynamics of the robot and the difficulty of accurate state and surface motion estimation. Surface motion imposes uncertainty on both system parameters and non-homogeneous disturbance in the walking robot dynamics. In this paper, we design an adaptive ankle torque controller to simultaneously address these two uncertainties and propose a step-length planner to minimize the required control torque. Typically, an adaptive controller is used for a continuous system. To apply adaptive control on a hybrid system such as a walking robot, an intermediate command profile is introduced to ensure a continuous error system. Simulations on a planar bipedal robot, along with comparisons against a baseline controller, demonstrate that the proposed approach effectively ensures stable walking and accurate tracking under unknown, time-varying disturbances. 

The efficient supply of goods and transport of materials are important factors for sustainability in any urban environment where traffic and environmental issues also need to be addressed. In this paper we developed a centrally coordinated approach for routing freight in urban environments where traffic loads are unbalanced in time and space in an effort to improve mobility and reduce cost. We assume that freight is moved by trucks using the road network and truck fleets consist of a mix of diesel and electric trucks. We formulated the routing problem as an optimization problem with several constraints and we use a co-simulation load balancing approach to generate routes for trucks that reduce the overall cost. We use a simulation test of a road network in the Los Angeles/Long Beach Metropolitan areas that includes two major ports to demonstrate the results.