Adaptive legged robots through exactly-constrained and non-redundant design

This paper presents a novel strategy for designing passively adaptive, statically stable walking robots with full body mobility that are exactly constrained and non-redundantly actuated during stance. In general, fully mobile legged robots include a large number of actuated joints, giving them a wide range of controllable foot placements but resulting in overconstraint during stance, requiring kinematic redundancy and redundant control for effective locomotion. The proposed design strategy allows for the elimination of actuation redundancy, thus greatly reducing the weight and complexity of the legged robots obtained and allowing for simpler control schemes. Moreover, the underconstrained nature of the resulting robots during swing allows for passive adaptability to rough terrain without large contact forces. The strategy uses kinematic mobility analysis tools to synthesize leg topologies, underactuated robotics design approaches to effectively distribute actuation constraints, and elastic elements to influence nominal leg behavior. Several examples of legged robot designs using the suggested approach are thoroughly discussed and a proof-of-concept of a non-redundant walking robot is presented

Adaptive Model Predictive Control of a Two-wheeled Robot Manipulator with Varying Mass

This paper presents the adaptive model predictive control approach for a two-wheeled robot manipulator with varying mass. The mass variation corresponds to the robot picking and placing objects or loads from one place to another. A linear parameter varying model of the system is derived consisting of local linear models of the system at different values of the varying parameter. An adaptive model predictive control controller is designed to control the fast-varying center of gravity angle in the inner loop. The reference for the inner loop is generated by a slower outer loop controlling the linear position using a linear quadratic Gaussian regulator. This adaptive model predictive control/linear quadratic Gaussian control system is simulated on the nonlinear model of the robot, and the closed-loop performance of the proposed scheme is compared with a system having inner/outer loop controllers as proportional integral derivative/proportional integral derivative, feedback linearization/linear quadratic Gaussian, and linear quadratic Gaussian/linear quadratic Gaussian. It is seen that adaptive model predictive control shows mostly superior and otherwise very good performance when compared to these benchmarks in terms of reference tracking and robustness to mass parameter variations