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Packing problems are classical optimization problems with wide-ranging applications. With the advancement of robotic manipulation, there are growing demands for the automation of packing tasks. However, the simultaneous optimization of packing and the robot’s motion planning is challenging because these two decisions are interconnected, and no previous study has addressed this optimization problem. This paper presents a framework to simultaneously determine the robot’s motion planning and packing decision to minimize the robot’s processing time and the container’s volume. This framework comprises three key components: solution encoding, surrogate modeling, and evolutionary computation. The sequence-triple representation encodes complex packing solutions by a sequence of integers. A surrogate model is trained to predict the processing time for a given packing solution to reduce the computational burden. Training data is generated by solving the motion planning problem for a set of packing solutions using the rapidly exploring random tree algorithm. The Non-Dominated Sorting Genetic Algorithm II searches for the Pareto solutions. Experimental evaluations are conducted using a 6-DOF robot manipulator. The experimental results suggest that implementing the surrogate model can reduce the computational time by 91.1%. The proposed surrogate-assisted optimization method can obtain significantly better solutions than the joint angular velocity-based estimation method.

Energy usage in robotic applications is rapidly increasing as industrial robot installations grow. This research introduces a novel approach, using the rapidly exploring random tree (RRT)-based scheme for optimizing the robot’s motion planning and minimizing energy consumption. Sampling-based algorithms for path planning, such as RRT and its many other variants, are widely used in robotic motion planning due to their efficiency in solving complex high-dimensional problems efficiently. However, standard versions of these algorithms cannot guarantee that the generated trajectories are always optimum and mostly ignore the energy consumption in robotic applications. This paper proposes an energy-efficient industrial robotics motion planning approach using the novel flight cost-based RRT (FC-RRT*) algorithm in pick-and-place operation to generate nodes in a predetermined direction and then calculate energy consumption using the circle point method. After optimizing the motion trajectory, power consumption is computed for the rotary axes of a six degree of freedom (6DOF) serial type of industrial robot using the work–energy hypothesis for the rotational motion of a rigid body. The results are compared to the traditional RRT and RRT* (RRT-star) algorithm as well as the kinematic solutions. The experimental results of axis indexing tests indicate that by employing the sampling-based FC-RRT* algorithm, the robot joints consume less energy (1.6% to 16.5% less) compared to both the kinematic solution and the conventional RRT* algorithm.

The implementation of Industry 5.0 necessitates a decrease in the energy consumption of industrial robots. This research investigates energy optimization for optimal motion planning for a dual-arm industrial robot. The objective function for the energy minimization problem is stated based on the execution time and total energy consumption of the robot arm configurations in its workspace for pick-and-place operation. Firstly, the PID controller is being used to achieve the optimal parameters. The parameters of PID are then fine-tuned using metaheuristic algorithms such as Genetic Algorithms and Particle Swarm Optimization methods to create a more precise robot motion trajectory, resulting in an energy-efficient robot configuration. The results for different robot configurations were compared with both motion planning algorithms, which shows better compatibility in terms of both execution time and energy efficiency. The feasibility of the algorithms is demonstrated by conducting experiments on a dual-arm robot, named as duAro. In terms of energy efficiency, the results show that dual-arm motions can save more energy than single-arm motions for an industrial robot. Furthermore, combining the robot configuration problem with metaheuristic approaches saves energy consumption and robot execution time when compared to motion planning with PID controllers alone.

We propose a method that corrects effects of refractions and absorptions for underwater scan data obtained by general-purpose laser range finders (LRFs) using HOKUYO UTM-30LX (Top-URG). For measurements by LRFs in water, it is known that refractions make measurement distances longer and absorptions make reflection intensities weaker. In order to confirm those characteristics of measurements in water, we first conduct two basic experiments that Top-URG measures a target in the environment with the assumed turbid deep-sea and evaluate an accuracy and a performance of Top-URG in water. The first basic experiment is that Top-URG measures surroundings in a sink filled with water by changing the turbidity of the water. The second basic experiment is conducted by changing a location and an angle of a target. Next, we develop a method that (1) corrects scan data of Top-URG by considering a refractive index of water and (2) eliminates scan data with low reliabilities based on reflection intensities obtained by Top-URG using automatically calculated thresholds. We evaluate those two methods by using two types of target objects and seven situations. Our developed methods can correct distorted scan points by refractions in water and can eliminate ghost scan points which are not existent. We confirm that their performances for underwater scan data are effective enough to use Top-URG in water.

This chapter introduces a novel four-limbed robot, WAREC-1, that has advanced locomotion and manipulation capability with versatile locomotion styles. At disaster sites, there are various types of environments through which a robot must traverse, such as rough terrain filled with rubbles, narrow places, stairs, and vertical ladders. WAREC-1 moves in hazardous environments by transitioning among various locomotion styles, such as bipedal/quadrupedal walking, crawling, and ladder climbing. WAREC-1 has identically structured limbs with 28 degrees of freedom (DoF) in total with 7-DoFs in each limb. The robot is 1,690mm tall when standing on two limbs, and weighs 155 kg. We developed three types of actuator units with hollow structures to pass the wiring inside the joints of WAREC-1, which enables the robot to move on rubble piles by creeping on its stomach. Main contributions of our research are following five topics: (1) Development of a four-limbed robot, WAREC-1. (2) Simultaneous localization and mapping (SLAM) using laser range sensor array. (3) Teleoperation system using past image records to generate a thirdperson view. (4) High-power and low-energy hand. (5) Lightweight master system for telemanipulation and an assist control system for improving the maneuverability of master-slave systems. [ABSTRACT FROM AUTHOR]

In the Tough Snake Robot Systems Group, a snake robot without wheels (nonwheeled-type snake robot) and a snake robot with active wheels (wheeled snake robot) have been developed. The main target applications of these snake robots are exploration of complex plant structures, such as the interior and exterior of pipes, debris, and even ladders, and the inspection of narrow spaces within buildings, e.g., roof spaces and underfloor spaces, which would enable plant patrol and inspection. At the head of each robot, a compact and lightweight gripper is mounted to allow the robot to grasp various types of objects, including fragile objects. To measure the contact force of each robot, a whole-body tactile sensor has been developed. A sound-based online localization method for use with the in-pipe snake robot has also been developed. To enable teleoperation of platform robots with the sensing system and the gripper, a human interface has also been developed. The results of some experimental demonstrations of the developed tough snake robot systems are presented. [ABSTRACT FROM AUTHOR]