Multi-Robot Systems (MRS) are transforming diverse domains like logistics, cargo management, and agriculture. However, ensuring that the behavior is correct under various network conditions within such complex environments is challenging, and meeting the desired automation goals is difficult. We propose the CORNET 2.0 co-simulation framework to jointly and accurately simulate multi-agent robotic systems within physical environments and the communication network models within such environments. Our modular framework allows diverse robot and network models to seamlessly integrate to simulate the robot's autonomy, physical space, and network features, such as latency, throughput, and loss intrinsic to the network topology and communication technology. A key novelty of CORNET 2.0 is its accurate synchronizing of mobility and time, which ensures that the physical location of a robot at a point in time, and the network properties and packets that flow from that location, are aligned. This is vital to model and validate MRS coordination algorithms that rely on network interactions. We provide a detailed evaluation of CORNET 2.0 in modeling real-world MRS use cases, such as leader-follower and warehouse environments, that help highlights the benefits.

This paper describes Team Delft's robot winning the Amazon Robotics Challenge 2016. The competition involves automating pick and place operations in semistructured environments, specifically the shelves in an Amazon warehouse. Team Delft's entry demonstrated that the current robot technology can already address most of the challenges in product handling: object recognition, grasping, motion, or task planning; under broad yet bounded conditions. The system combines an industrial robot arm, 3-D cameras and a custom gripper. The robot's software is based on the robot operating system to implement solutions based on deep learning and other state-of-the-art artificial intelligence techniques, and to integrate them with off-the-shelf components. From the experience developing the robotic system, it was concluded that: 1) the specific task conditions should guide the selection of the solution for each capability required; 2) understanding the characteristics of the individual solutions and the assumptions they embed is critical to integrate a performing system from them; and 3) this characterization can be based on “levels of robot automation.” This paper proposes automation levels based on the usage of information at design or runtime to drive the robot's behavior, and uses them to discuss Team Delft's design solution and the lessons learned from this robot development experience.

A large number of novel path planning methods for a wide range of problems have been described in literature over the past few decades. These algorithms can often be configured using a set of parameters that greatly influence their performance. In a typical use case, these parameters are only very slightly tuned or even left untouched. Systematic approaches to tune parameters of path planning algorithms have been largely unexplored. At the same time, there is a rising interest in the planning and robotics communities regarding the real world application of these theoretically developed and simulation-tested planning algorithms. In this work, we propose the use of Sequential Model-based Algorithm Configuration (SMAC) tools to address these concerns. We show that it is possible to improve the performance of a planning algorithm for a specific problem without the need of in-depth knowledge of the algorithm itself. We compare five planners that see a lot of practical usage on three typical industrial pick-and-place tasks to demonstrate the effectiveness of the method.

Sampling-based methods, such as Probabilistic Roadmap Method(PRM)[1], Rapidly-Exploring Random Tree (RRT)[2], have been proposed as promising solutions for kinodynamic problems. Nevertheless, it's still a challenge for practical application especially for complex systems. In particular, most of the forward propagation is fruitless, which lead to heavy computation and be time-consuming. This paper presents a greedy kinodynamic motion planner: Guided RRT. The main characteristics are that the environments are explored by Geometric trees previously and nodes near the geometric feasible path enjoy more preference. Instead of exploring the environments uniformly, the new approach expands towards the goal greedily along a series of waypoints, with probabilistically completeness. And to guarantee the effective of the greed, a new distance metric based on Euclidean metric are proposed by considering both the current position and the following position with zero-input. We compare our technique with standard RRT and show that it achieves favorable performance when planning under kinodynamic constraints.

The dynamic feasibility of solutions to motion planning problems using Rapidly Exploring Random Trees depends strongly on the choice of the distance metric used while planning. The ideal distance metric is the optimal cost of traversal between two states in the state space. However, it is computationally intensive to find the optimal cost while planning. We propose a novel approach to overcome this barrier by using a supervised learning algorithm that learns a nonlinear function which is an estimate of the optimal cost, via offline training. We use the Iterative Linear Quadratic Regulator approach for estimating an approximation to the optimal cost and learn this cost using Locally Weighted Projection Regression. We show that the learnt function approximates the original cost with a reasonable tolerance and more importantly, gives a tremendous speed up of a factor of 1000 over the actual computation time. We also use the learnt metric for solving the pendulum swing up planning problem and show that our metric performs better than the popularly used Linear Quadratic Regulator based metric.