Robot manipulator designs are increasingly focused on low cost approaches, especially those envisioned for use in unstructured environments such as households, office spaces and hazardous environments. The cost of angular sensors varies based on the precision offered. For tasks in these environments, millimeter order manipulation errors are unlikely to cause drastic reduction in performance. In this paper, estimates the joint angles of a manipulator using low cost triaxial accelerometers by taking the difference between consecutive acceleration vectors. The accelerometer-based angle is compensated with a uniaxial gyroscope using a complementary filter to give robust measurements. Three compensation strategies are compared: complementary filter, time varying complementary filter, and extended Kalman filter. This sensor setup can also accurately track the joint angle even when the joint axis is parallel to gravity and the accelerometer data does not provide useful information. In order to analyze this strategy, accelerometers and gyroscopes were mounted on one arm of a PR2 robot. The arm was manually moved smoothly through different trajectories in its workspace while the joint angle readings from the on-board optical encoders were compared against the joint angle estimates from the accelerometers and gyroscopes. The low cost angle estimation strategy has a mean error 1.3° over the three joints estimated, resulting in mean end effector position errors of 6.1 mm or less. This system provides an effective angular measurement as an alternative to high precision encoders in low cost manipulators and as redundant measurements for safety in other manipulators.

Mobile robot tipover is a concern as it can create dangerous situations for operators and bystanders, cause collateral damage to the surrounding environment, and result in an aborted mission. Algorithms have been developed by others to assess the stability of the robot, and many of these algorithms have been demonstrated using simulated data. In order to verify that these algorithms accurately match real-world behavior, we have collected data of a mobile robot tipping over and then compared this data to the stability measures provided by three algorithms: Zero-Moment Point (ZMP), Force-Angle stability measure (FA), and Moment-Height Stability measure (MHS). A small mobile robot platform based on the iRobot PackBot drove a course including ramps and obstacles; an IMU and GPS provided inertial and positional data for the algorithms, and the actual tipover event is determined from video footage of the tests. The average normalized measure at tipover event initiation was found to be 0.665 for ZMP, -0.094 for FA, and 0.023 for MHS, where a value of 1 corresponds to resting stability. Standard deviations were 0.38, 0.84, and 0.67, respectively. The measures show a significant amount of noise, which is likely due to the vibrations caused by movement of the tracks and could be reduced by employing additional filtering during data collection. The preliminary real-world data validates these tipover algorithms as able to assess robot stability, and they can be used as part of a tipover avoidance system.