This work proposes an online task-scheduling method using mixed-integer programming for a multi-tasking problem regarding a dual-arm cooking robot in a controlled environment. Given each task’s processing time, their location in the working space, dependency, the required number of arms, and the kinematic constraints of the dual-arm robot, the proposed optimization algorithm can produce a feasible solution to scheduling the cooking order for each task and for each associated arms so that the total cooking time and the total moving distance for each arm are minimized. We use a subproblem optimization strategy in which the number of tasks to be planned is divided into several groups instead of planning all tasks at the same time. By doing so, the planning time can be significantly decreased, making the algorithm practical for online implementation. The feasibility of our optimization method and the effectiveness of the subproblem optimization strategy were verified through simulated experiments consisting of 30 to 120 tasks. The results showed that our strategy is advantageous in terms of computation time and makespan for large problems.

This paper presents a technique to model statically indeterminate forces based on stiffness matrices for multi-limbed climbing robots. Current wall climbing robots in literature overlook statically indeterminate forces, causing an incapability to estimate climbing failure under certain circumstances. Accounting for these forces, robot deformation can be approximated, paving the way for the proposed two-wall climbing approach. During a wall climb, two failure modes, slide and over-torque, are identified to compute feasible climbing region. A hexapod robot is used to verify the proposed technique by climbing between walls with pure friction end effectors.

This paper presents SCALER, a versatile free-climbing multi-limbed robot that is designed to achieve tightly coupled simultaneous locomotion and dexterous grasping. While existing quadrupedal-limbed robots have demonstrated impressive dexterous capabilities, achieving a balance between power-demanding locomotion and precise grasping remains a critical challenge. We design a torso mechanism and a parallel-serial limb to meet the conflicting requirements that pose unique challenges in hardware design. SCALER employs underactuated two-fingered GOAT grippers that can mechanically adapt and offer seven modes of grasping, enabling SCALER to traverse extreme terrains with multi-modal grasping strategies. We study the whole-body approach, where SCALER utilizes its body and limbs to generate additional forces for stable grasping in various environments, thereby further enhancing its versatility. Furthermore, we improve the GOAT gripper actuation speed to realize more dynamic climbing in a closed-loop control fashion. With these proposed technologies, SCALER can traverse vertical, overhanging, upside-down, slippery terrains and bouldering walls with non-convex-shaped climbing holds under the Earth's gravity.

We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal while avoiding obstacles in uncertain environments. First, we use stochastic model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints. Second, recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution, which are trained on uncertainty outputs of various simultaneous localization and mapping algorithms. When two or more robots are in communication range, these uncertainties are then updated using a distributed Kalman filtering approach. Lastly, a Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal. Our complete methods are demonstrated on a ground and aerial robot simultaneously (code available at: https://github.com/AlexS28/SABER ).