This paper reviews the state-of-the-art in robotic tails intended for inertial adjustment applications on-board mobile robots. Inspired by biological tails observed in nature, robotic tails provide a separate means to enhance stabilization, and maneuverability from the mobile robot's main form of locomotion, such as legs or wheels. Research over the past decade has primarily focused on implementing single-body rigid pendulum-like tail mechanisms to demonstrate inertial adjustment capabilities on-board walking, jumping and wheeled mobile robots. Recently, there have been increased efforts aimed at leveraging the benefits of both articulated and continuum tail mechanism designs to enhance inertial adjustment capabilities and further emulate the structure and functionalities of tail usage found in nature. This paper discusses relevant research in design, modeling, analysis and implementation of robotic tails onto mobile robots, and highlight how this work is being used to build robotic systems with enhanced performance capabilities. The goal of this article is to outline progress and identify key challenges that lay ahead.

The ability to reliably estimate free space is an essential requirement for efficient and safe robot navigation. This paper presents a novel system, built upon a stochastic framework, which estimates free space quickly from stereo data, using self-supervised learning. The system relies on geometric data in the close range of the robot to train a second-stage appearance-based classifier for long range areas in a scene. Experiments are conducted on board an unmanned ground vehicle, and the results demonstrate the advantages of the proposed technique over other self-supervised systems.

This paper suggests an integrated navigation system for an unmanned ground vehicle operating in an unknown cluttered environment. The navigator supports time-critical mobility making it possible for a mobile robot to reach a target from the first attempt without the need for a dedicated exploration and mapping stage. The robot only uses necessary and sufficient egocentric local sensory data collected on its way to the target. The construction of the navigation structure revolves around key properties of the harmonic potential field approach to motion planning. The robot's trajectory is well-behaved and direct-to-the-goal. It contains only the minimum number of detours necessary to accommodate the sensory data and maintain the robot in a safe, goal-oriented state. The navigation structure is developed and its theoretical basis is explained. Extensive experimental validation of its properties and performance is carried-out using the X80 robotic platform.

This paper proposes a detailed investigation on the new neural-based feed-forward PID direct force control (FNN-PID-DF) approach applied to a highly nonlinear 2-axes pneumatic artificial muscle (PAM) manipulator in order to ameliorate its force output performance. Founded on the novel inverse neural NARX model dynamically identified to learn well all nonlinear characteristics of the contact force dynamics of the 2-axes PAM-based manipulator, the novel proposed neural FNN-PID-DF force controller is innovatively implemented in order to directly force control the 2-axes PAM robot system used as a rehabilitation device subjected to internal systematic interactions and external contact force deviations. The performance of the experimental tests has proven the advantages and merits of the new force control method compared to the classical PID force control method. The new neural FNN-PID-DF force controller guides the wrist/hand of subject/patient to successfully generate the predefined desired force values.

This work proposes a strategy for position control and obstacle avoidance in a quadcopter based on constrained generalized predictive control and geometric attitude control. The approach allows real-time trajectory tracking using optimal control actions and avoids collisions with static obstacles whose position is known. An experimental validation of the proposed controller is presented.

In this paper, the kinematic accuracy problem caused by geometric errors of a 2(3HUS+S) parallel manipulator is described. The kinematic equation of the manipulator is obtained by establishing a D–H (Denavit–Hartenberg) coordinate system. A D–H transformation matrix is used as the error-modeling tool, and the kinematic error model of the manipulator integrating manufacturing and assembly errors is established based on the perturbation theory. The iterative Levenberg–Marquardt algorithm is used to identify the geometric errors in the error model. According to the experimentally measured attitudes, the kinematic calibration process is simulated using MATLAB software. The simulation and experiment results show that the attitude errors of the moving platforms after calibration are reduced compared with before the calibration, and the kinematic accuracy of the manipulator is significantly improved. The correctness and effectiveness of the error model and the kinematic calibration method of the 2(3HUS+S) parallel manipulator for simulation of hip joint motion are verified.

This paper reports the design, analysis, and control of a miniature vibration-driven planar locomotion robot called Shell. A vibration-driven system is able to achieve locomotion based on internal oscillations and anisotropic friction forces. In this robot design, two parallel oscillators are employed to provide propelling forces, and a blade-like support is designed to generate anisotropic frictional contact with the ground. If the two parallel oscillators are of different frequencies and amplitudes, two-dimensional locomotion of the robot can be achieved. To predict the robot's planar locomotion, a dynamic model is developed. Controlling the robot's locomotion and especially, the locomotion modes can be achieved by adjusting the vibration frequencies of the two internal oscillators. Experimental results show that Shell can be controlled to move rectilinearly and along circles with certain curvatures. In addition, by combining these basic trajectories, Shell can move freely on a horizontal plane.