Unified Path and Gait Planning for Safe Bipedal Robot Navigation

Safe path and gait planning are essential for bipedal robots to navigate complex real-world environments. The prevailing approaches often plan the path and gait separately in a hierarchical fashion, potentially resulting in unsafe movements due to neglecting the physical constraints of walking robots. A safety-critical path must not only avoid obstacles but also ensure that the robot's gaits are subject to its dynamic and kinematic constraints. This work presents a novel approach that unifies path planning and gait planning via a Model Predictive Control (MPC) using the Linear Inverted Pendulum (LIP) model representing bipedal locomotion. This approach considers environmental constraints, such as obstacles, and the robot's kinematics and dynamics constraints. By using discrete-time Control Barrier Functions for obstacle avoidance, our approach generates the next foot landing position, ensuring robust walking gaits and a safe navigation path within clustered environments. We validated our proposed approach in simulation using a Digit robot in 20 randomly created environments. The results demonstrate improved performance in terms of safety and robustness when compared to hierarchical path and gait planning frameworks.

Adaptive Step Duration for Precise Foot Placement: Achieving Robust Bipedal Locomotion on Terrains with Restricted Footholds

This paper introduces a novel multi-step preview foot placement planning algorithm designed to enhance the robustness of bipedal robotic walking across challenging terrains with restricted footholds. Traditional one-step preview planning struggles to maintain stability when stepping areas are severely limited, such as with random stepping stones. In this work, we developed a discrete-time Model Predictive Control (MPC) based on the step-to-step discrete evolution of the Divergent Component of Motion (DCM) of bipedal locomotion. This approach adaptively changes the step duration for optimal foot placement under constraints, thereby ensuring the robot's operational viability over multiple future steps and significantly improving its ability to navigate through environments with tight constraints on possible footholds. The effectiveness of this planning algorithm is demonstrated through simulations that include a variety of complex stepping-stone configurations and external perturbations. These tests underscore the algorithm's improved performance for navigating foothold-restricted environments, even with the presence of external disturbances.

Time-Varying ALIP Model and Robust Foot-Placement Control for Underactuated Bipedal Robot Walking on a Swaying Rigid Surface

Controller design for bipedal walking on dynamic rigid surfaces (DRSes), which are rigid surfaces moving in the inertial frame (e.g., ships and airplanes), remains largely uninvestigated. This paper introduces a hierarchical control approach that achieves stable underactuated bipedal robot walking on a horizontally oscillating DRS. The highest layer of our approach is a real-time motion planner that generates desired global behaviors (i.e., the center of mass trajectories and footstep locations) by stabilizing a reduced-order robot model. One key novelty of this layer is the derivation of the reduced-order model by analytically extending the angular momentum based linear inverted pendulum (ALIP) model from stationary to horizontally moving surfaces. The other novelty is the development of a discrete-time foot-placement controller that exponentially stabilizes the hybrid, linear, time-varying ALIP model. The middle layer of the proposed approach is a walking pattern generator that translates the desired global behaviors into the robot's full-body reference trajectories for all directly actuated degrees of freedom. The lowest layer is an input-output linearizing controller that exponentially tracks those full-body reference trajectories based on the full-order, hybrid, nonlinear robot dynamics. Simulations of planar underactuated bipedal walking on a swaying DRS confirm that the proposed framework ensures the walking stability under different DRS motions and gait types.

Entangling Practice with Artistic and Educational Aims: Interviews on Technology-based Movement Sound Interactions

Movement-sound interactive systems are at the interface of different artistic and educational practices. Within this multiplicity of uses, we examine common denominators in terms of learning, appropriation and relationship to technological systems. While these topics have been previously reported at NIME, we wanted to investigate how practitioners, coming from different perspectives, relate to these questions. We conducted interviews with 6 artists who are engaged in movementsound interactions: 1 performer, 1 performer/composer, 1 composer, 1 teacher/composer, 1 dancer/teacher, 1 dancer. Through a thematic analysis of the transcripts we identified three main themes related to (1) the mediating role of technological tools (2) usability and normativity, and (3) learning and practice. These results provide ground for discussion about the design and study of movement-sound interactive systems.

Resolved Motion Control for 3D Underactuated Bipedal Walking using Linear Inverted Pendulum Dynamics and Neural Adaptation

We present a framework to generate periodic trajectory references for a 3D under-actuated bipedal robot, using a linear inverted pendulum (LIP) based controller with adaptive neural regulation. We use the LIP template model to estimate the robot's center of mass (CoM) position and velocity at the end of the current step, and formulate a discrete controller that determines the next footstep location to achieve a desired walking profile. This controller is equipped on the frontal plane with a Neural-Network-based adaptive term that reduces the model mismatch between the template and physical robot that particularly affects the lateral motion. Then, the foot placement location computed for the LIP model is used to generate task space trajectories (CoM and swing foot trajectories) for the actual robot to realize stable walking. We use a fast, real-time QP-based inverse kinematics algorithm that produces joint references from the task space trajectories, which makes the formulation independent of the knowledge of the robot dynamics. Finally, we implemented and evaluated the proposed approach in simulation and hardware experiments with a Digit robot obtaining stable periodic locomotion for both cases.