Sensor fusion enhances robotic perception by integrating data from multiple sensing modalities. This paper introduces a biologically inspired, closed-loop sensor fusion framework combining camera and LiDAR data to robustly estimate the position of a moving object. To mitigate non-Gaussian disturbances such as shot noise, the approach employs dynamic weighting strategies integrated with the Maximum Correntropy Criterion Kalman Filter (MCC-KF). The proposed method is validated experimentally using a custom-built platform, demonstrating enhanced robustness and accuracy under diverse and challenging noise conditions.

This work introduces a modular experimental platform for studying legged locomotion, constructed using the template-anchor methodology, which allows the generalization of designed applications to the broader spectrum of legged robots. A finite state machine governs the operation logic, enabling reliable phase-specific coordination within a hybrid dynamical system. Active hip torque control is integrated to stabilize trajectories and support continuous running, enhancing the dynamic capabilities of the platform. The experimental platform enables advanced system identification, forward prediction, and closed-loop control applications.

The increased need for accurately modeling the input-output characteristics of linear time-periodic (LTP) systems necessitates novel identification and control algorithms as well as new test benches for their experimental validation. This paper introduces a simple-to-build test bench for the identification and control of LTP systems. We mechanically coupled the shafts of two DC motors and fed back the angular velocity to the second motor with a time-periodic modulation. This allowed us to imitate a time-periodic load for the first DC motor, thereby yielding an experimental LTP plant. We used Matlab/Simulink target hardware support to implement the entire software in Simulink, which greatly simplifies input design, data collection, and analysis as compared to embedded programming. We used constant-frequency sinusoidal signals for the data collection on the experimental test bench. Subsequently, we estimated the harmonic transfer functions and identified the parameters of a state-space model of the proposed LTP system. We then designed a time-periodic controller in order to regulate the output of the LTP system. We designed a reference-tracking controller along with a Linear Quadratic Integrator (LQI) to improve the system performance. We validated our results through experiments on the physical LTP system plant.

Widely accepted utility of simple spring-mass models for running behaviors as descriptive tools, as well as literal control targets, motivates accurate analytical approximations to their dynamics. Despite the availability of a number of such analytical predictors in the literature, their validation has mostly been done in simulation, and it is yet unclear how well they perform when applied to physical platforms. In this paper, we extend on one of the most recent approximations in the literature to ensure its accuracy and applicability to a physical monopedal platform. To this end, we present systematic experiments on a well-instrumented planar monopod robot, first to perform careful identification of system parameters and subsequently to assess predictor performance. Our results show that the approximate solutions to the spring-loaded inverted pendulum dynamics are capable of predicting physical robot position and velocity trajectories with average prediction errors of 2% and 7%, respectively. This predictive performance together with the simple analytic nature of the approximations shows their suitability as a basis for both state estimators and locomotion controllers.

Practical realization of model-based dynamic legged behaviors is substantially more challenging than statically stable behaviors due to their heavy dependence on second-order system dynamics. This problem is further aggravated by the difficulty of accurately measuring or estimating dynamic parameters such as spring and damping constants for associated models and the fact that such parameters are prone to change in time due to heavy use and associated material fatigue. In this paper, we present an on-line, model-based adaptive control method for running with a planar spring-mass hopper based on a once-per-step parameter correction scheme. Our method can be used both as a system identification tool to determine possibly time-varying spring and damping constants of a miscalibrated system, or as an adaptive controller that can eliminate steady-state tracking errors through appropriate adjustments on dynamic system parameters. We present systematic simulation studies to show that our method can successfully accomplish both of these tasks.