Abstract: In this paper a global reactive motion planning framework for robotic manipulators in complex dynamic environments is presented. In particular, the circular field predictions (CFP) planner from Becker et al. (2021) is extended to ensure obstacle avoidance of the whole structure of a robotic manipulator. Towards this end, a motion planning framework is developed that leverages global information about promising avoidance directions from arbitrary configuration space motion planners, resulting in improved global trajectories while reactively avoiding dynamic obstacles and decreasing the required computational power. The resulting motion planning framework is tested in multiple simulations with complex and dynamic obstacles and demonstrates great potential compared to existing motion planning approaches. Keywords: Autonomous robotic systems, Robots manipulators, Guidance navigation and control, Motion Planning, Real-Time Collision Avoidance 1. INTRODUCTION RRT planner has been improved and extended to different variants, e.g., the RRT-connect improves the runtime Motivation: Classical industrial robotic applications require (Kuffner and LaValle, 2000), whereas the asymptotically fences or other peripheral safety installations to optimal RRT* decreases the resulting path length (Karaman ensure seamless production processes and the safety of and Frazzoli, 2011).

Known as OceanOneK, the robot allows its operators to feel like they're underwater explorers, too. OceanOneK resembles a human diver from the front, with arms and hands and eyes that have 3D vision, capturing the underwater world in full color. The back of the robot has computers and eight multidirectional thrusters that help it carefully maneuver the sites of fragile sunken ships. OceanOneK, here doing an experiment in a swimming pool at Stanford University, resembles a human diver. When an operator at the ocean's surface uses controls to direct OceanOneK, the robot's haptic (touch-based) feedback system causes the person to feel the water's resistance as well as the contours of artifacts.

We present a generalized version of the Saturation in the Null Space (SNS) algorithm for the task control of redundant robots when hard inequality constraints are simultaneously present both in the joint and in the Cartesian space. These hard bounds should never be violated, are treated equally and in a unified way by the algorithm, and may also be varied, inserted or deleted online. When a joint/Cartesian bound saturates, the robot redundancy is exploited to continue fulfilling the primary task. If no feasible solution exists, an optimal scaling procedure is applied to enforce directional consistency with the original task. Simulation and experimental results on different robotic systems demonstrate the efficiency of the approach. The proposed algorithm can be viewed as a generic platform that is easily applicable to any robotic application in which robots operate in an unstructured environment and online handling of joint and Cartesian constraints is critical.

It can't dribble, let alone slam dunk, but Toyota's basketball robot hardly ever misses a free throw or a 3-pointer. The 207-centimeter-tall (6 feet 10-inches) machine made five of eight 3-point shots in a demonstration in a Tokyo suburb Monday, a ratio its engineers say is worse than usual. Toyota Motor Corp.'s robot, called Cue 3, computes a three-dimensional image where the basket is, using sensors on its torso, and adjusts motors inside its arm and knees to give the shot the right angle and propulsion for a swish. Efforts in developing human-shaped robots underline a global shift in robotics use from pre-programmed mechanical arms in limited situations like factories to functioning in the real world with people. The 2017 version of the robot was designed to make free throws.

Oussama Khatib, a professor of computer science at Stanford University, encountered a pivotal moment during the first outing of his deep-sea robot, Ocean One, off the coast of France. The robot was trapped, far too deep for human retrieval, between the cannons of a sunken ship. Weather was threatening to force the robotics crew to return to shore, but Khatib and his team resisted. "No way, I'm not leaving the robot," Khatib said before moving to the haptic controls, which simulate a sense of touch and allow for remote operation. Able to control the robot's arms, Khatib pushed.

This robot dives to depths humans dare not attempt - and it can bring people along for the ride without them getting wet. The Stanford-built OceanOne is filled with compressible oil to offset the crushing pressures experienced when 100 metres underwater, and AI-assisted navigation steers it clear of obstacles. Its operators remain on land, observing on screen everything the robot captures, using joysticks to drive it and guiding its hands through a feedback mechanism that relays tactile sensations. "It's impossible to let a robot act alone in such an environment: it will fail," says Professor Oussama Khatib, OceanOne's creator. "The only way you can guarantee success is connecting a worker through a haptic device to the robot.

Each year, Stanford Professor (and IEEE Fellow) Oussama Khatib introduces a new class of students to control theory and sets them loose on a room full of robot arms, including the Kuka LWR, the Kuka IIWA, the Barrett WAM, and the Kinova Jaco. The students in the Experimental Robotics class are charged with making the robots do something, typically, something that requires computer vision and force control. Said Khatib: each robot team had to develop "a strategy to draw or to play or to track, [because] the heart of robotics is perception connected to action." Typically, these industrial robot arms are programmed to perform factory tasks, like assembly, welding, and painting. The Stanford students get a rare opportunity to use them in more imaginative ways.

Oussama Khatib held his breath as he swam through the wreck of La Lune, 100 meters below the Mediterranean. The flagship of King Louis XIV sank here in 1664, 20 miles off the southern coast of France, and no human had touched the ruins – or the countless treasures and artifacts the ship once carried – in the centuries since. OceanOne, a humanoid robotic diver from Stanford, allows new underwater exploration capabilities. With guidance from a team of skilled deep-sea archaeologists who had studied the site, Khatib, a professor of computer science at Stanford, spotted a grapefruit-size vase. He hovered precisely over the vase, reached out, felt its contours and weight, and stuck a finger inside to get a good grip.

This "robotic mermaid" could be more than just a clever way to retrieve sunken treasure (and disappoint amorous sailors). It hints at how humans and robots may someday work together in all sorts of difficult environments. The submersible humanoid robot, called OceanOne, was developed at Stanford University. It recently retrieved priceless artifacts from King Louis XIV's La Lune, a 350-year-old galleon wrecked off Toulon in southern France in 1664. OceanOne has two arms, a head, and a tail-like appendage fitted with motorized propellers.

Mermaids, and their less famous comrades the mermen, are beautiful beings that have mastered the underwater world. They also have a more sinister rep as vicious bastards that drag sailors to their watery doom. So perhaps it's no wonder that a new mermaid-like robot from Stanford, the OceanOne--with its graceful, streamlined body but oh, also, claws and dead eyes--elicits mixed emotions. Sure, it looks like you and me, but it's just rather more, well, electronic. Grab a pitchfork and vow to hunt it down. But OceanOne is in fact an emblem of a battle over the future of robotics: Humanoid bots are getting roboticists riled up, and not just because they're creepy.