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Most advanced trajectory tracking control laws for robot manipulators require a knowledge of all state variables. For lightweight flexible-joint space manipulators this objective is difficult to achieve since link positions are typically not measured. In this paper, an extended Kalman filter (EKF) observer to estimate all state variables of a manipulator system modeled with a nonlinear stiffness dynamics model is presented. In addition, it is shown that the observer can be modified in order to calibrate in real-time the sensor biases. The state variable estimates are coupled to a flexible joint adaptive controller to provide a complete closed-loop trajectory tracking solution. Simulation results show that the proposed solution provides satisfying tracking performance in a 12.6 ×12.6 m trajectory tracking scenario.

Autonomous navigation in an outdoor environment can be effectively performed by small robots but remains a challenging task as soon as real scale ground vehicles are concerned. An intelligent mobile robot which uses a real armor ground vehicle as a platform is currently being developed for security applications (Fig. 30.1(a)). The robot tasks needed to be addressed are patrolling, surveillance, and tracking suspicious objects in a limited area. Executing those tasks a security robot often faces the situation when many objects in the environment have similar geometrical characteristics. This situation is typical for navigating within the outdoor warehouse [1] as illustrated in Fig. 30.1(b). The cargo containers (the squares with pattern) of the same size are piled inside the fenced area. A robot is used to carry out the patrol along a given trajectory (dotted line) around the area. Being equipped with the basic positioning system using GPS receiver and odometry sensor, a robot is able to follow the given path if measurement no errors are negligible. In practice, due to various errors (misalignments, drifts, slippage) basic GPS/odometry-based system fails to provide reliable pose estimates required in physically restricted environments. Measurements of relative distances are necessary and they are provided in our case by an additionally installed laser range sensor (LRS) known for its accuracy and reliability.

Operational problems with space robots relate to several factors, one most importantly being structural flexibility and subsequently significant difficulties with position control. Elastic link vibrations with inherent nonminimum phase response coupled with large rotations create a complex nonlinear dynamic system for control. The ability of the flexible robot to follow a prescribe trajectory is an important issue. In case of more complex operation, good tracking control could potentially save much time and money. This paper compares the performance of control strategies with noise filtering, then combined with nonminimum phase and corrective control action modeled by time delays for a stationary spacecraft mounted flexible robot tracking a 12.6 m × l2.6 m square trajectory. Finally a fuzzy logic system (FLS) vibration suppression strategy demonstrates its effectiveness in providing intelligent and autonomous precision control. The inclusion of this fuzzy logic approach to vibration suppression is motivated by the prospect of substituting for the control of space robotic systems typically teleoperated by astronauts. Tracking results are obtained initially without vibration suppression or noise filtering, then with EKF and FLAEKF state estimators to determine tracking accuracy for robot process and measurement noise. Time delays are included in the feedback and control action loops to simulate nonminimum phase response and corrective control. Finally, a FLS is used to adapt the control law and suppress residual vibrations. Good tracking results were previously achieved using an input shaping method to reduce residual vibrations coupled with an inverse kinematics control strategy and recursive order-n algorithm to model a two-link flexible robot tracking a square trajectory [1].

Navigation of autonomous vehicles and robots can be divided into two categories: indoor navigation and outdoor navigation. In general the outdoor navigation is more difficult and complex task because often the environment does not have characteristic points that can be identified and with respect to which robots could relate their position. If environment is unstructured (e.g. off-road environment), problems related to autonomous navigation are very difficult. Problems related to navigation in the unstructured environment could be divided into mapping, localization, collision avoidance and trajectory tracking.