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This paper presents a sequential Model Predictive Control (MPC) approach to reactive motion planning for bipedal robots in dynamic environments. The approach relies on a sequential polytopic decomposition of the free space, which provides an ordered collection of mutually intersecting obstaclefree polytopes and waypoints. These are subsequently used to define a corresponding sequence of MPC programs that drive the system to a goal location avoiding static and moving obstacles. This way, the planner focuses on the free space in the vicinity of the robot, thus alleviating the need to consider all the obstacles simultaneously and reducing computational time. We verify the efficacy of our approach in high-fidelity simulations with the bipedal robot Digit, demonstrating robust reactive planning in the presence of static and moving obstacles.

The task of controlling an underactuated robotic finger with a single tendon and a single actuator is difficult. Methods for controlling the class of underactuated systems are available in the literature. However, this particular system does not fall into the class of underactuated system. This paper presents a design change which introduces kinematic constraints into the system, making the system controllable. Backstepping control strategy is used to control the system. Simulation results are presented for single finger driven by a single actuator.