A new footstep planning for SLIP and TD-SLIP models
Embargo Lift Date: 2021-06-28
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Spring Loaded Inverted Pendulum (SLIP) is a well-known model and an accurate descriptive tool, which can scientifically represent the dynamics of the legged locomotion. Torque actuated Dissipative SLIP (TD-SLIP), on the other hand, is fundamentally an enhanced version of the SLIP model. Inclusion of more realistic damping model and the hip torque actuation has led the researchers to develop a sufficiently better analytic approximation. This thesis proposes a new methodology to achieve footstep planning on the SLIP and TD-SLIP models, distinctly. It contributes a novel planning algorithm by utilising the constructed touchdown-totouchdown map, and a novel recursive function to plan and execute the planning. The thesis provides a background information about the modelling and simulation of both of the used models, and an auxiliary function, which administers a derivative-free method to calculate the minimum of an input function. After defining the problems and the corresponding proposed solutions, the foundations of the preparation phase is established. This phase is fundamentally constructed to accumulate required information for the algorithm implementation and simulation phase. The main phase consists of subsections, which can be composed of the combination of following properties; planning type, as online and offline, policy type; as forward and backwards and output type; as based on distance or based on minimum step count. According to the stated problem, the planning is successfully realised not only for a single desired distance, but also an array of waypoints. In addition to this, the presented illustrations of different initial states show that the planning can also be constructed via any different initial touchdown state. Therefore, the obtained results are quite promising, since all of the cases and their combinations successfully reach the destinations with a negligible error value, which is less than 1%. Although, the offline planning type provides the results in a rapid way, the obtained data to use the plan requires much more space, which also increases dramatically when the step count (level) is incremented. In addition to this, the forward planning is faster than the backwards one, but they both generate very similar results.
Online - offline planning
Forward - backwards planning