Personal mobile robotic assistants are expected to find wide applications in industry and healthcare. For example, people with limited mobility can benefit from robots helping with daily tasks, or construction workers can have robots perform precision monitoring tasks on-site. However, manually steering a robot while in motion requires significant concentration from the operator, especially in tight or crowded spaces. This reduces walking speed, and the constant need for vigilance increases fatigue and, thus, the risk of accidents. This work presents a virtual leash with which a robot can naturally follow an operator. We use a sensor fusion based on a custom-built RF transponder, RGB cameras, and a LiDAR. In addition, we customize a local avoidance planner for legged platforms, which enables us to navigate dynamic and narrow environments. We successfully validate on the ANYmal platform [1] the robustness and performance of our entire pipeline in real-world experiments. The video is available at: obstacle-avoidant-leader-following.

In this paper, we present a local, robot-centric navigation map optimized for autonomous mobile robots operating in unknown environments, enhancing their onboard perception systems for collision-free operation with far look-ahead distances. Utilizing a novel converging covariance cell representation, our approach effectively analyzes hazards such as obstacles and hazardous slopes in both terrestrial and aerial navigation contexts. The new technique specifically targets mapping from stereo scenarios with ultra short baseline and highly oblique viewpoints close to the ground.Our methodology surpasses traditional window-based hazard analysis by resolving sub-cell size obstacles and terrain gradients at the individual cell level, thereby avoiding the computational overhead typically associated with such analyses. It leverages a multi-resolution strategy adaptive to the range errors common in stereo vision systems, making it particularly suitable for embedded systems with computational limitations.Functionality includes constant-time queries for height, obstacle presence, and slope details, boasting improvements in run time, memory usage, precision, and resolvable obstacle size compared to existing grid-based mapping algorithms. We validate our approach through rigorous simulation and real-world testing. This technique will be used for the local mapping and collision avoidance on NASA’s CADRE lunar rovers.

Automation of hydraulic material handling machinery is currently limited to semi-static pick-and-place cycles. Dynamic throwing motions which utilize the passive joints, can greatly improve time efficiency as well as increase the dumping workspace. In this work, we use Reinforcement Learning (RL) to design dynamic controllers for material handlers with under-actuated arms as commonly used in logistics. The controllers are tested both in simulation and in real-world experiments on a 12-ton test platform. The method is able to exploit the passive joints of the gripper to perform dynamic throwing motions. With the proposed controllers, the machine is able to throw individual objects to targets outside the static reachability zone with good accuracy for its practical applications. The work demonstrates the possibility of using RL to perform highly dynamic tasks with heavy machinery, suggesting a potential for improving the efficiency and precision of autonomous material handling tasks.