Automation can address labor shortage and enhance worker safety in construction. However, workers continue to perform majority of the work at construction sites that can be automated. Construction machinery require force control for automation, which can absorb external shocks and provide appropriate forces along with environmental forces. This study proposes a force-controlled excavator that fulfills these requirements by replacing the hydraulic system with a series elastic actuator (SEA). Few studies have applied SEA to large high-output construction machinery. We designed the structure of the SEA to deliver high output power in a compact form that can be mounted on an excavator. A 2.5-T class excavator equipped with this SEA is designed, which achieved a force resolution of 15–35 N at the tip. The effectiveness of this excavator in automating a major portion of the manual work is demonstrated.

Safety can be listed as one of the most important aspects of every working environment, from the low risky to the most dangerous one. Construction sites can be clearly identified among the riskiest working fields, mainly because several complex and fast maneuvers are executed in a very crowded, dynamic and uncertain scenario. Specifically referring to construction machines, typical operations such as digging, earthmoving, heavy weights loading/unloading, etc. become incredibly risky if the machine stability is not guaranteed by the operator. To this purpose, this paper proposes a practical implementation of a dynamic fall prediction system for excavators based on the concept of the Zero Moment Point (ZMP). A commercially available excavator has been dynamically modelled and equipped with a set of sensors to reconstruct the state of the machine. When the proposed system is running, an alarm rings if a critical condition is reached, thus allowing the operator to avoid risky and potentially harmful situations.

Reactive control and planning are complementary methods in robot motion control. The advantage of planning is the ability to find difficult solutions, optimize trajectories globally and not getting stuck in local minima but at higher computational cost. On the other hand, reactive control can handle dynamic or uncertain environments at low computational cost, but may get stuck in local minima.

This paper presents a decentralized planning method for generating dynamic whole body motions of multilink robots including humanoids. First, a robotic system will be modeled as a general multi-body dynamical system. The planning problem of a multi-body system will then be formulated as a constraint resolution problem. The problem will be solved by means of an extended Gauss-Seidel method, which is capable of handling multiple constraint groups with different priorities. The method will be demonstrated in whole-body motion generation tasks of a humanoid, both in numerical simulations and in experiments using a real humanoid robot.

We describe a system for visual interaction developed for humanoid robots. It enables the robot to interact with its environment using a smooth whole body motion control driven by stabilized visual targets. Targets are defined as visually extracted "proto-objects" and behavior-relevant object hypotheses and are stabilized by means of a short-term sensory memory. Selection mechanisms are used to switch between behavior alternatives for searching or tracking objects as well as different whole body motion strategies for reaching. The decision between different motion strategies like reaching with right or left hand or with and without walking is made based on internal predictions that use copies of the whole-body control algorithm. The results show robust object tracking and a smooth interaction behavior that includes a large variety of whole-body postures.

We propose a self collision avoidance system that superposes trajectories in order not only to protect the robot's hardware but also to enable continuous motions. The system runs in real-time so that the robot can work in an uncertain environment. It is based on virtual forces between close segments of the robot. The avoidance movements are blended with a whole body motion control in order to change the priority between target reaching and collision avoidance. The blending is performed autonomously without the necessity of external switching. Our method works both while the robot is standing and walking. Reaching motions from the front to the side of the body without the arm colliding with the body are possible. Even if the target is inside the body, the arm stops at the closest point to the target outside of the body. Our method can be used for other applications: We apply it to realizing a "body schema" and for "occlusion avoidance."