The priorities of a climbing legged robot are to maintain a grasp on its climbing surface and to climb efficiently against the force of gravity. These priorities profoundly constrain the choice of gait regulation methods. We propose a gait regulation and analysis method that varies foot detachment timing, effectively modifying stride length and frequency in order to maintain gait phasing, subject to kinematic and stability constraints. The method results in linear equations, leading to straightforward tests for local and global convergence when, for example, disturbances such as foot slippage cause departures from the nominal phasing. We illustrate the procedure with an example involving a bounding gait and compare it with empirical results obtained on the RiSE climbing robot.

Climbing robots are especially susceptible to thermal overload during normal operation, due to the need to oppose gravity and to frequently apply internal forces for clinging. As an alternative to setting conservative limits on the motor peak and average current, we investigate methods for measuring motor temperatures, predicting motor thermal conditions and generating thermally constrained behavior. A thermal model, verified using empirical data, predicts the motor's winding temperature based on measured case temperature and input current. We also present a control strategy that maximizes robot velocity while satisfying a constraint on the maximum permissible motor winding temperature.

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We describe the design and control of a new bio-inspired climbing robot designed to scale smooth vertical surfaces using directional adhesive materials. The robot, called Stickybot, draws its inspiration from geckos and other climbing lizards and employs similar compliance and force control strategies to climb smooth vertical surfaces including glass, tile and plastic panels. Foremost among the design features are multiple levels of compliance, at length scales ranging from centimeters to micrometers, to allow the robot to conform to surfaces and maintain large real areas of contact so that adhesive forces can support it. Structures within the feet ensure even stress distributions over each toe and facilitate engagement and disengagement of the adhesive materials. A force control strategy works in conjunction with the directional adhesive materials to obtain sufficient levels of friction and adhesion for climbing with low attachment and detachment forces.

The cerebellum has been called the brain's "engine of agility". This paper presents a cerebellum-inspired neural network that performs dynamic state estimation and predictive control. The model combines two types of learning within a radial basis function network. Its performance was demonstrated on a 2-link robot arm built with antagonistic pairs of McKibben air muscles. The arm has a gripper end effector to hold and throw a tennis ball. Trajectory data was collected during multiple throwing trials and used to train the model offline. The data were projected onto 2-dimensional state space maps, from which the network learns to estimate state variables and decision boundaries. It successfully learned to trigger the grip release at the proper state for the ball to hit a target. This algorithm should generalize to benefit a wide variety of biomorphic robots.