Masayoshi Tomizuka

Algorithmic safety measures for intelligent industrial co-robots

In factories of the future, humans and robots are expected to be co-workers and co-inhabitants in the flexible production lines. It is important to ensure that humans and robots do not harm each other. This paper is concerned with functional issues to ensure safe and efficient interactions among human workers and the next generation intelligent industrial co-robots. The robot motion planning and control problem in a human involved environment is posed as a constrained optimal control problem. A modularized parallel controller structure is proposed to solve the problem online, which includes a baseline controller that ensures efficiency, and a safety controller that addresses real time safety by making a safe set invariant. Capsules are used to represent the complicated geometry of humans and robots. The design considerations of each module are discussed. Simulation studies which reproduce realistic scenarios are performed on a planar robot arm and a 6 DoF robot arm. The simulation results confirm the effectiveness of the method.

Modeling and Controller Design of Cooperative Robots in Workspace Sharing Human-Robot Assembly Teams

Human workers and robots are two major workforces in modern factories. For safety reasons, they are separated, which limits the productive potentials of both parties. It is promising if we can combine humans flexibility and robots productivity in manufacturing. This paper investigates the modeling and controller design method of workspace sharing human-robot assembly teams and adopts a two-layer interaction model between the human and the robot. In theoretical analysis, enforcing invariance in a safe set guarantees safety. In implementation, an integrated method concerning online learning of closed loop human behavior and receding horizon control in the safe set is proposed. Simulation results in a 2D setup confirm the safety and efficiency of the algorithm.

CONTROL IN A SAFE SET: ADDRESSING SAFETY IN HUMAN-ROBOT INTERACTIONS

Human-robot interactions (HRI) happen in a wide range of situations. Safety is one of the biggest concerns in HRI. This paper proposes a safe set method for designing the robot controller and offers theoretical guarantees of safety. The interactions are modeled in a multi-agent system framework. To deal with humans in the loop, we design a parameter adaptation algorithm (PAA) to learn the closed loop behavior of humans online. Then a safe set (a subset of the state space) is constructed and the optimal control law is mapped to the set of control which can make the safe set invariant. This algorithm is applied with different safety constraints to both mobile robots and robot arms. The simulation results confirm the effectiveness of the algorithm.

Safe exploration: Addressing various uncertainty levels in human robot interactions

To address the safety issues in human robot interactions (HRI), a safe set algorithm (SSA) was developed previously. However, during HRI, the uncertainty levels are changing in different phases of the interaction, which is not captured by SSA. A safe exploration algorithm (SEA) is proposed in this paper to address the uncertainty levels in the robot control. To estimate the uncertainty levels online, a learning method in the belief space is developed. A comparative study between SSA and SEA is conducted. The simulation results confirm that SEA can capture the uncertainty reduction behavior which is observed in human-human interactions.

Teach industrial robots peg-hole-insertion by human demonstration

Programming robotic assembly tasks usually requires delicate force tuning. In contrast, human may accomplish assembly tasks with much less time and fewer trials. It will be a great benefit if robots can learn the human inherent skill of force control and apply it autonomously. Recent works on Learning from Demonstration (LfD) have shown the possibility to teach robots by human demonstration. The basic idea is to collect the force and corrective velocity that human applies during assembly, and then use them to regress a proper gain for the robot admittance controller. However, many of the LfD methods are tested on collaborative robots with compliant joints and relatively large assembly clearance. For industrial robots, the non-backdrivable mechanism and strict tolerance requirement make the assembly tasks more challenging. This paper modifies the original LfD to be suitable for industrial robots. A new demonstration tool is designed to acquire the human demonstration data. The force control gains are learned by Gaussian Mixture Regression (GMR) and the closed-loop stability is analysed. A series of peg-hole-insertion experiments with H7h7 tolerance on a FANUC manipulator validate the performance of the proposed learning method.

Robotic manipulation of deformable objects by tangent space mapping and non-rigid registration

Recent works of non-rigid registration have shown promising applications on tasks of deformable manipulation. Those approaches use thin plate spline-robust point matching (TPS-RPM) algorithm to regress a transformation function, which could generate a corresponding manipulation trajectory given a new pose/shape of the object. However, this method regards the object as a bunch of discrete and independent points. Structural information, such as shape and length, is lost during the transformation. This limitation makes the objects final shape to differ from training to test, and can sometimes cause damage to the object because of excessive stretching. To deal with these problems, this paper introduces a tangent space mapping (TSM) algorithm, which maps the deformable object in the tangent space instead of the Cartesian space to maintain structural information. The new algorithm is shown to be robust to the changes in the objects pose/shape, and the objects final shape is similar to that of training. It is also guaranteed not to overstretch the object during manipulation. A series of rope manipulation tests are performed to validate the effectiveness of the proposed algorithm.

Enabling safe freeway driving for automated vehicles

The development of automated vehicles brings new challenges to road safety. The behavior of the automated vehicles should be carefully designed in order to interact with the environment and other vehicles efficiently and safely. This paper is focused on the learning and decision making methods for the automated vehicles towards safe freeway driving. Based on a multi-agent traffic model, the decision making problem is posed as an optimal control problem, which is solved by 1) behavior classification and trajectory prediction of the surrounding vehicles, and 2) a unique parallel planner architecture which addresses the efficiency goal and the safety goal separately. The simulation results demonstrate the effectiveness of the algorithm.

Convex feasible set algorithm for constrained trajectory smoothing

Trajectory smoothing is an important step in robot motion planning, where optimization methods are usually employed. However, the optimization problem for trajectory smoothing in a clustered environment is highly non-convex, and is hard to solve in real time using conventional non-convex optimization solvers. This paper discusses a fast online optimization algorithm for trajectory smoothing, which transforms the original non-convex problem to a convex problem so that it can be solved efficiently online. The performance of the algorithm is illustrated in various cases, and is compared to that of conventional sequential quadratic programming (SQP). It is shown that the computation time is greatly reduced using the proposed algorithm.

Human guidance programming on a 6-DoF robot with collision avoidance

In the application of physical human-robot interaction (pHRI), the collaboration between human and robot can significantly improve the production efficiency through combination of the humans flexible intelligence and the robots consistent performance. In this application, however, it is an important concern to ensure the safety of the human and the robot. In the human guidance programming scenario, the operator plans a collision-free path for the robot end-effector, but the robot body might collide with an obstacle while being guided by the operator. In this paper, a novel on-line velocity based collision avoidance algorithm is developed to solve the problem in this particular scenario. The proposed algorithm gives an explicit solution to deal with both collision avoidance and human guidance command at the same time, which provides the operator a better and safer lead through programming experience. The real-time experiment is performed on FANUC LR Mate 200 iD/7L in three different obstacle scenarios.

Autonomous alignment of peg and hole by force/torque measurement for robotic assembly

In the past years, many methods have been developed for robotic peg-hole-insertion to automate the assembly process. However, many of them are based on the assumption that the peg and hole are well aligned before insertion starts. In practice, if there is a large pose(position/orientation) misalignment, the peg and hole may suffer from a three-point contact condition where the traditional assembly methods cannot work. To deal with this problem, this paper proposes an autonomous alignment method by force/torque measurement before insertion phase. A three-point contact model is built up and the pose misalignment between the peg and hole is estimated by force and geometric analysis. With the estimated values, the robot can autonomously correct the misalignment before applying traditional assembly methods to perform insertions. A series of experiments on a FANUC industrial robot and a H7h7 tolerance peg-hole testbed validate the effectiveness of the proposed method. Experimental results show that the robot is able to perform peg-hole-insertion from three-point contact conditions with 96% success rate.