Posture control for legged robots has been widely developed on custom-designed robotic platforms, with little work being done on commercially-available robots despite their potential as low-cost research platforms. This paper presents the implementation of a Walking Posture Control system on a commercially-available hexapod robot which utilizes low-cost joint actuators without torque control capabilities. The hierarchical control system employs Virtual Model Control with simple foot force distribution and a novel, position-based Foot Force Controller that enables direct force control during the leg's stance phase and active compliance control during the swing phase. Ground truth measurements in experimental tests, obtained with a Vicon motion capture system, demonstrate the improvement to posture made by the control system on uneven terrain, with the results comparing favorably to those obtained in similar tests on more sophisticated, custom-designed platforms.

Posture control is one of the fundamental requirements for legged robots to achieve mobility on uneven terrain. Most of the research on posture control is conducted on custom designed robot platforms, which are much more expensive than their commercially available counterparts. This paper presents the implementation of a standing posture control system on a low-cost, commercially available hexapod robot platform. The Virtual Model Control (VMC) strategy is implemented, along with a simple method for foot force distribution and a direct force control method that can be used with position control of low-cost joint actuators. The controller was experimentally tested on level and uneven terrain, as well as on a dynamic balance board. Ground truth measurements of the tests revealed that the results compared favorably to those obtained on more expensive, custom designed platforms.

The use of low-cost, camera sensors for Simultaneous Localization And Mapping and Moving Object Tracking (SLAMMOT) is a developing research area. Image features can be static or dynamic, sparse or dense, and can appear or disappear, making them difficult to track individually over an image sequence. Clustering techniques have been recommended and used to cluster image features to improve tracking results. New and affordable RGB-D cameras, provide both color and depth information. This paper compares five different clustering algorithms to determine which algorithm would be best suited to cluster features from RGB-D image sequences for tracking objects in an indoor dynamic environment. Speeded Up Robust Features (SURF) are used and the performance of k-means, mean shift, a contrario, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Gaussian Mixture Models (GMM) clustering algorithms are validated in tests with synthetic and RGB-D data. Results indicate that mean shift clustering may be suitable for the SLAMMOT task as it appeared best for overall performance as well as for execution efficiency.

This paper introduces a state estimation methodology for a hexapod robot that makes use of proprioceptive sensors and a kinematic model of the robot. The methodology focuses on providing reliable full pose state estimation for a commercially-available hexapod robot platform with the use of only commonly-available sensors. The presented methodology provides the derivation of the kinematic model and implements an Extended Kalman Filter (EKF) state estimation framework similar to that recently validated on a quadruped. The EKF fuses the kinematic model with on-board IMU measurements to estimate the pose of the robot. The methodology was tested with experiments using a physical hexapod robot and validated with independent ground truth measurements.