Collaborating Authors


3D Printed Research Robotics Platform Runs Remotely


The Open Dynamic Robot Initiative Group is a collaboration between five robotics-oriented research groups, based in three countries, with the aim to build an Open Source robotics platform based around the torque-control method. Leveraging 3D printing, a few custom PCBs, and off-the-shelf parts, there is a low-barrier to entry and much lower cost compared to similar robots. The eagle-eyed will note that this is only a development platform, and all of the higher level control is off-machine, hosted by a separate PC. What's interesting here, is just how low-level the robot actually is. The motion hardware is purely a few BLDC motors driven by field-orientated control (FOC) driver units, a wireless controller and some batteries.

The new wave of robotic automation


Ask Peter Howard SM '84, CEO of Realtime Robotics and MIT Sloan School of Management alumnus, what he thinks is the biggest bottleneck facing the robotics industry, and he'll tell you without hesitation it's return on investment. "Robotics automation is capable of handling almost any single task that a human can do, but the ROI is not compelling due to the high cost of deployment and the inability to achieve commensurate throughput," he says. But Realtime Robotics has developed a combination of proprietary software and hardware that reduces system deployment time by 70 percent or more, reduces deployment costs by 30 percent or more, and reduces the programming component of building a robotic system in the industrial robot space by upwards of 90 percent. In other words, Realtime Robotics is making robot adoption well worth the investment. On some level, people are always planning -- even the most spontaneous among us.

Trajectory Optimization of Chance-Constrained Nonlinear Stochastic Systems for Motion Planning and Control Artificial Intelligence

We present gPC-SCP: Generalized Polynomial Chaos-based Sequential Convex Programming method to compute a sub-optimal solution for a continuous-time chance-constrained stochastic nonlinear optimal control problem (SNOC) problem. The approach enables motion planning and control of robotic systems under uncertainty. The proposed method involves two steps. The first step is to derive a deterministic nonlinear optimal control problem (DNOC) with convex constraints that are surrogate to the SNOC by using gPC expansion and the distributionally-robust convex subset of the chance constraints. The second step is to solve the DNOC problem using sequential convex programming (SCP) for trajectory generation and control. We prove that in the unconstrained case, the optimal value of the DNOC converges to that of SNOC asymptotically and that any feasible solution of the constrained DNOC is a feasible solution of the chance-constrained SNOC. We derive a stable stochastic model predictive controller using the gPC-SCP for tracking a trajectory in the presence of uncertainty. We empirically demonstrate the efficacy of the gPC-SCP method for the following three test cases: 1) collision checking under uncertainty in actuation, 2) collision checking with stochastic obstacle model, and 3) safe trajectory tracking under uncertainty in the dynamics and obstacle location by using a receding horizon control approach. We validate the effectiveness of the gPC-SCP method on the robotic spacecraft testbed.

IA Planner: Motion Planning Using Instantaneous Analysis for Autonomous Vehicle in the Dense Dynamic Scenarios on Highways Artificial Intelligence

In dense and dynamic scenarios, planning a safe and comfortable trajectory is full of challenges when traffic participants are driving at high speed. The classic graph search and sampling methods first perform path planning and then configure the corresponding speed, which lacks a strategy to deal with the high-speed obstacles. Decoupling optimization methods perform motion planning in the S-L and S-T domains respectively. These methods require a large free configuration space to plan the lane change trajectory. In dense dynamic scenes, it is easy to cause the failure of trajectory planning and be cut in by others, causing slow driving speed and bring safety hazards. We analyze the collision relationship in the spatio-temporal domain, and propose an instantaneous analysis model which only analyzes the collision relationship at the same time. In the model, the collision-free constraints in 3D spatio-temporal domain is projected to the 2D space domain to remove redundant constraints and reduce computational complexity. Experimental results show that our method can plan a safe and comfortable lane-changing trajectory in dense dynamic scenarios. At the same time, it improves traffic efficiency and increases ride comfort.

Projection Mapping Implementation: Enabling Direct Externalization of Perception Results and Action Intent to Improve Robot Explainability Artificial Intelligence

Existing research on non-verbal cues, e.g., eye gaze or arm movement, may not accurately present a robot's internal states such as perception results and action intent. Projecting the states directly onto a robot's operating environment has the advantages of being direct, accurate, and more salient, eliminating mental inference about the robot's intention. However, there is a lack of tools for projection mapping in robotics, compared to established motion planning libraries (e.g., MoveIt). In this paper, we detail the implementation of projection mapping to enable researchers and practitioners to push the boundaries for better interaction between robots and humans. We also provide practical documentation and code for a sample manipulation projection mapping on GitHub:

Helping robots avoid collisions


George Konidaris still remembers his disheartening introduction to robotics. "When you're a young student and you want to program a robot, the first thing that hits you is this immense disappointment at how much you can't do with that robot," he says. Most new roboticists want to program their robots to solve interesting, complex tasks -- but it turns out that just moving them through space without colliding with objects is more difficult than it sounds. Fortunately, Konidaris is hopeful that future roboticists will have a more exciting start in the field. That's because roughly four years ago, he co-founded Realtime Robotics, a startup that's solving the "motion planning problem" for robots.

Deployment and Evaluation of a Flexible Human-Robot Collaboration Model Based on AND/OR Graphs in a Manufacturing Environment Artificial Intelligence

The Industry 4.0 paradigm promises shorter development times, increased ergonomy, higher flexibility, and resource efficiency in manufacturing environments. Collaborative robots are an important tangible technology for implementing such a paradigm. A major bottleneck to effectively deploy collaborative robots to manufacturing industries is developing task planning algorithms that enable them to recognize and naturally adapt to varying and even unpredictable human actions while simultaneously ensuring an overall efficiency in terms of production cycle time. In this context, an architecture encompassing task representation, task planning, sensing, and robot control has been designed, developed and evaluated in a real industrial environment. A pick-and-place palletization task, which requires the collaboration between humans and robots, is investigated. The architecture uses AND/OR graphs for representing and reasoning upon human-robot collaboration models online. Furthermore, objective measures of the overall computational performance and subjective measures of naturalness in human-robot collaboration have been evaluated by performing experiments with production-line operators. The results of this user study demonstrate how human-robot collaboration models like the one we propose can leverage the flexibility and the comfort of operators in the workplace. In this regard, an extensive comparison study among recent models has been carried out.

A Survey of Behavior Trees in Robotics and AI Artificial Intelligence

Behavior Trees (BTs) were invented as a tool to enable modular AI in computer games, but have received an increasing amount of attention in the robotics community in the last decade. With rising demands on agent AI complexity, game programmers found that the Finite State Machines (FSM) that they used scaled poorly and were difficult to extend, adapt and reuse. In BTs, the state transition logic is not dispersed across the individual states, but organized in a hierarchical tree structure, with the states as leaves. This has a significant effect on modularity, which in turn simplifies both synthesis and analysis by humans and algorithms alike. These advantages are needed not only in game AI design, but also in robotics, as is evident from the research being done. In this paper we present a comprehensive survey of the topic of BTs in Artificial Intelligence and Robotic applications. The existing literature is described and categorized based on methods, application areas and contributions, and the paper is concluded with a list of open research challenges.

Experimental Comparison of Global Motion Planning Algorithms for Wheeled Mobile Robots Artificial Intelligence

Planning smooth and energy-efficient motions for wheeled mobile robots is a central task for applications ranging from autonomous driving to service and intralogistic robotics. Over the past decades, a wide variety of motion planners, steer functions and path-improvement techniques have been proposed for such non-holonomic systems. With the objective of comparing this large assortment of state-of-the-art motion-planning techniques, we introduce a novel open-source motion-planning benchmark for wheeled mobile robots, whose scenarios resemble real-world applications (such as navigating warehouses, moving in cluttered cities or parking), and propose metrics for planning efficiency and path quality. Our benchmark is easy to use and extend, and thus allows practitioners and researchers to evaluate new motion-planning algorithms, scenarios and metrics easily. We use our benchmark to highlight the strengths and weaknesses of several common state-of-the-art motion planners and provide recommendations on when they should be used.

The new burger chef makes $3 an hour and never goes home. (It's a robot)


In a test kitchen in a corner building in downtown Pasadena, Flippy the robot grabbed a fryer basket full of chicken fingers, plunged it into hot oil -- its sensors told it exactly how hot -- then lifted, drained and dumped maximally tender tenders into a waiting hopper. A few feet away, another Flippy eyed a beef patty sizzling on a griddle. With its camera eyes feeding pixels to a machine vision brain, it waited until the beef hit the right shade of brown, then smoothly slipped its spatula hand under the burger and plopped it on a tray. The product of decades of research in robotics and machine learning, Flippy represents a synthesis of motors, sensors, chips and processing power that wasn't possible until recently. Now, Flippy's success -- and the success of the company that built it, Miso Robotics -- depends on simple math and a controversial hypothesis of how robots can transform the service economy.