In this article, we propose RRT- , an online and intermittent kinodynamic motion planning framework for dynamic environments with unknown robot dynamics and unknown disturbances. We leverage RRT for global path planning and rapid replanning to produce waypoints as a sequence of boundary-value problems (BVPs). For each BVP, we formulate a finite-horizon, continuous-time zero-sum game, where the control input is the minimizer, and the worst case disturbance is the maximizer. We propose a robust intermittent Q-learning controller for waypoint navigation with completely unknown system dynamics, external disturbances, and intermittent control updates. We execute a relaxed persistence of excitation technique to guarantee that the Q-learning controller converges to the optimal controller. We provide rigorous Lyapunov-based proofs to guarantee the closed-loop stability of the equilibrium point. The effectiveness of the proposed RRT- is illustrated with Monte Carlo numerical experiments in numerous dynamic and changing environments.

Active Learning of Gaussian process (GP) surrogates is an efficient way to model unknown environments in various applications. In this paper, we propose an adaptive exploration-exploitation active learning method (ALX) that can be executed rapidly to facilitate real-time decision making. For the exploration phase, we formulate an acquisition function that maximizes the approximated, expected Fisher information. For the exploitation phase, we employ a closed-form acquisition function that maximizes the total expected variance reduction of the search space. The determination of each phase is established with an exploration condition that measures the predictive accuracy of GP surrogates. Extensive numerical experiments in multiple input spaces validate the efficiency of our method.

In this paper, we propose two decentralized approximate algorithms for nested Gaussian processes in multi-robot systems. The distributed implementation is achieved with iterative and consensus methods that facilitate local computations at the expense of inter-robot communications. Moreover, we propose a covariance-based nearest neighbor robot selection strategy that enables a subset of agents to perform predictions. In addition, both algorithms are proved to be consistent. Empirical evaluations with real data illustrate the efficiency of the proposed algorithms.

Lecturers of Engineering courses around the world are struggling to increase the engagement of students through the introduction of appropriate hands-on activities and assignments. In Biomechatronics and Robotics courses these assignments typically focus on how certain devices are designed, modelled, fabricated, or controlled. The hardware for these assignments is usually purchased by some external vendor and the students only get the chance to analyze it or program it, so as to execute a useful task (e.g., programming mobile robots to perform path following tasks). Student engagement can be increased by instructing the students to prepare the hardware for their assignment. This also increases the sense of ownership of the project outcomes. In this paper, we present how a robotic gripper / hand design project and the introduction of a grasping and manipulation competition as a course assignment, can significantly increase the student engagement and their understanding of the taught concepts. The presented best practices have been trialed over the last four years in two different courses (one undergraduate and one postgraduate) of the Department of Mechanical Engineering at the University of Auckland in New Zealand. For the particular assignment the students were asked to fully develop a robotic gripper or hand from scratch using a single actuator (only the actuator and the power electronics were provided). The performance of the developed devices was assessed through the participation in a grasping and manipulation competition. All the details of the proposed assignment are presented, hoping that they could help other lecturers and teachers to prepare similar activities.

With the emergence of agile manufacturing in highly automated industrial environments, the demand for efficient robot adaptation to dynamic task requirements is increasing. For assembly tasks in particular, classic robot programming methods tend to be rather time intensive. Thus, effectively responding to rapid production changes requires faster and more intuitive robot teaching approaches. This work focuses on combining programming by demonstration with path optimization and local replanning methods to allow for fast and intuitive programming of assembly tasks that requires minimal user expertise. Two demonstration approaches have been developed and integrated in the framework, one that relies on human to robot motion mapping (teleoperation based approach) and a kinesthetic teaching method. The two approaches have been compared with the classic, pendant based teaching. The framework optimizes the demonstrated robot trajectories with respect to the detected obstacle space and the provided task specifications and goals. The framework has also been designed to employ a local replanning scheme that adjusts the optimized robot path based on online feedback from the camera-based perception system, ensuring collision-free navigation and the execution of critical assembly motions. The efficiency of the methods has been validated through a series of experiments involving the execution of assembly tasks. Extensive comparisons of the different demonstration methods have been performed and the approaches have been evaluated in terms of teaching time, ease of use, and path length.

This paper presents an online kinodynamic motion planning algorithmic framework using asymptotically optimal rapidly-exploring random tree (RRT*) and continuous-time Q-learning, which we term as RRT-Q*. We formulate a model-free Q-based advantage function and we utilize integral reinforcement learning to develop tuning laws for the online approximation of the optimal cost and the optimal policy of continuous-time linear systems. Moreover, we provide rigorous Lyapunov-based proofs for the stability of the equilibrium point, which results in asymptotic convergence properties. A terminal state evaluation procedure is introduced to facilitate the online implementation. We propose a static obstacle augmentation and a local replanning framework, which are based on topological connectedness, to locally recompute the robot's path and ensure collision-free navigation. We perform simulations and a qualitative comparison to evaluate the efficacy of the proposed methodology.

In this paper we present an open-source design for the development of low-complexity, anthropomorphic, underactuated robot hands with a selectively lockable differential mechanism. The differential mechanism used is a variation of the whiffletree (or seesaw) mechanism, which introduces a set of locking buttons that can block the motion of each finger. The proposed design is unique since with a single motor and the proposed differential mechanism the user is able to control each finger independently and switch between different grasping postures in an intuitive manner. Anthropomorphism of robot structure and motion is achieved by employing in the design process an index of anthropomorphism. The proposed robot hands can be easily fabricated using low-cost, off-the-shelf materials and rapid prototyping techniques. The efficacy of the proposed design is validated through different experimental paradigms involving grasping of everyday life objects and execution of daily life activities. The proposed hands can be used as affordable prostheses, helping amputees regain their lost dexterity.