Guarding polygons with holes for robot motion planning applications

This paper presents a motion planning, guidance and control system for an Autonomous surface vessel in a practical maritime environment. The motion planning algorithm is based in the angle-guidance fast marching square method and the guidance system is based in the line-of-sight trajectory tracking algorithm. To validate the motion planning algorithm, numerical simulations were carried out to compute the optimal path in a static environment including various obstacles. The guidance system was tested with an autonomous surface vehicle tracking a pre-established trajectory including static obstacles. The autonomous surface vehicle is a self-propelled scaled ship model of 2.5 m equipped with sensors and actuators, inertial measurement unit, GNSS positioning system, propulsion DC motors, and wireless communication. From the experiments and numerical simulations, a good agreement was found.

Deep reinforcement learning for adaptive path planning and control of an autonomous underwater vehicle

We discuss the optimality and computational efficiency of sampling based motion planning (SBMP), which calculates dynamically precise and approximately optimal state transitions using arbitrarily selected nonlinear control system models. To ensure high optimality and computational efficiency, SBMP requires an approximately uniform state sampling function, though the non-linearity of the system models does not allow a perfect function. We propose linear prediction-based uniform state sampling (LPUSS) that samples approximately uniform state points while ensuring a dynamically correct state transition profile with a small calculation cost. LPUSS samples a state by using the given non-linear control system model after determining the input values by using a local linear transition model. We developed a mechanical motion planning system using LPUSS, articulated body algorithm, and parallel computing techniques. To validate LPUSS, we conducted experiments on double, triple, and sixtuple inverted pendulum models. LPUSS showed better optimality and computational efficiency with the double and triple inverted pendulum models, compared with randomized kinodynamic planning (RKP), which is based on rapid random tree (RRT), and our previously proposed rapid semi-optimal motion-planning method in which state sampling is based on uniform inputs. In particular, compared with our previous method, LPUSS was respectively 130 times and 3,000 times faster on double and triple inverted pendulum models under the condition of the same optimality. LPUSS found an approximately optimal swing up motion for the sixtuple inverted pendulum model within 40 minutes. According to our survey, there is no other optimization method that is applicable to higher than quadruple inverted pendulum models with standard constraints.

We devise a polynomial-time algorithm for partitioning a simple polygon P into a minimum number of star-shaped polygons. The question of whether such an algorithm exists has been open for more than four decades [Avis and Toussaint, Pattern Recognit., 1981] and it has been repeated frequently, for example in O’Rourke’s famous book [Art Gallery Theorems and Algorithms, 1987]. In addition to its strong theoretical motivation, the problem is also motivated by practical domains such as CNC pocket milling, motion planning, and shape parameterization.

In this paper, a motion planning system based on predictive control and temporal logic specifications is established for the welding dual-arm robot with gantry. Firstly, based on the spatial structure of the dual-arm robot with gantry, a novel mathematical model is presented between the joint variables and the terminal attitude and the quaternion replaces the Euler Angle to describe the posture for avoiding gimbal lock. Then a constraint model predictive controller is constructed for the multi-variable, nonlinear and coupling model of the robot and the constraints of the environment. Meanwhile, the temporal logic formula is used for describing the complex welding tasks and reducing the workload of repeated programming. Next the expectation values and constraints are converted by the temporal logic formula will be sent to the predictive controller one by one. Finally, the simulation result shows that the system can effectively solve the problem of motion planning for the welding dual-arm robot with gantry.

This paper proposes a rule-based motion planning system for agent-based crowd simulation, consisting of sets of rules for both collision avoidance and collision response. In order to avoid an on-coming collision, a set of rules for velocity sampling and evaluation is proposed, which aims to choose a velocity with an expected time to collision larger than a defined threshold. In order to improve the efficiency over existing methods, the sampling procedure terminates upon finding an appropriate velocity. Moreover, the proposed motion planning system does not guarantee a collision-free movement. In case of collision, another set of rules is also defined to direct the agent to make a corresponding response. The experiment results show that the proposed method can be applied into different scenarios, while making the simulation execution efficient.

This paper describes the design of an integrated system for humanoid robotics that consists of three key components, 3D vision, motion planning and bipedal control. Layered system design is adopted to achieve concurrency as well as small latency. Implementation using our humanoid type robot H7 and experiments with this architecture are described. The H7 is expected to be a common test-bed in experiments and discussion for various aspects of intelligent humanoid robotics.

One of the ultimate goals of mobile robotics research is to build robots that can safely carry out missions in hazardous and populated environments. Most of today's commercial mobile devices scale poorly along this dimension. Their motion planning relies on accurate, static models of the environments, and therefore they often fail their mission if humans or other unpredictable obstacles block their path. To build autonomous mobile robots one has to build systems that can perceive their environments, react to unforeseen circumstances, and plan dynamically in order to achieve their mission. Thus, the objective of the motion planning and control problem is to find collision-free trajectories, in static or dynamic environments containing some obstacles, between a start and a goal configuration. It has attracted much research in recent years. In this context the term control has a broad meaning that includes many different controls, such as low-level motor control, and behaviour control, where behaviour represents many complicated tasks, like obstacle avoidance and goal seeking. This article describes an intelligent motion planning and navigation system for omnidirectional mobile robots based on fuzzy logic. Owing to its simplicity and hence its short response time, the fuzzy navigator is especially suitable for on-line applications with strong real-time requirements. On-line planning is an on-going activity. The planner receives a continuous flow of information about occurring events and generates new commands in response to the incoming events, while previously planned motions are being executed. The fuzzy-rule-base of the proposed system combines the repelling influence, which is related to the distance and the angle between the robot and nearby obstacles, with the attracting influence produced by the distance and the angular difference between the actual direction and position of the robot and the final configuration, to generate actuating commands for the mobile platform. It can be considered as an on-line local navigation method for omnidirectional mobile robots for the generation of instantaneous collision-free motions. This reactive system is especially suitable for real-time applications. The use of fuzzy logic leads to a transparent system which can be tuned by hand or by a set of learning rules. Furthermore, this approach allows obstacle avoidance and navigation in dynamic environments. The functioning of the fuzzy motion planner with respect to omnidirectional mobile robots and results of simulated experiments are presented.

In this paper we present an artificial neural network based motion and path planning system of a wheeled mobile robot navigating among stationary and moving obstacles. The neural network is aware of its distance sensor readings and its relative position from the target. The neural network is used in this system as a controller, and it is trained using a previously proposed extension of the backpropagation through time algorithm, which uses potential fields for obstacle avoidance. The operability of this method is presented in a series of simulation results.

Whole-body motion and footstep planning for humanoid robots with multi-heuristic search

Skid-steered wheeled vehicles can be applied in military, agricultural, and other fields because of their flexible layout structure and strong passability. The research and application of vehicles are developing towards the direction of “intelligent” and “unmanned”. As essential parts of unmanned vehicles, the motion planning and control systems are increasingly demanding for model and road parameters. In this paper, an estimation method for tire and road parameters is proposed by combining offline and online identification. Firstly, a 3-DOF nonlinear dynamic model is established, and the interaction between tire and road is described by the Brush nonlinear tire model. Then, the horizontal and longitudinal stiffness of the tire is identified offline using the particle swarm optimization (PSO) algorithm with adaptive inertia weight. Referring to the Burckhardt adhesion coefficient formula, the extended forgetting factor recursive least-squares (EFRLS) method is applied to identify the road adhesion coefficient online. Finally, the validity of the proposed identification algorithm is verified by TruckSim simulation and real vehicle tests. Results show that the relative error of the proposed algorithm can be well controlled within 5%.

A motion planning and execution system for mobile robots driven by stepping motors

This paper presents an overview of the inaugural Amazon Picking Challenge along with a summary of a survey conducted among the 26 participating teams. The challenge goal was to design an autonomous robot to pick items from a warehouse shelf. This task is currently performed by human workers, and there is hope that robots can someday help increase efficiency and throughput while lowering cost. We report on a 28-question survey posed to the teams to learn about each team’s background, mechanism design, perception apparatus, planning, and control approach. We identify trends in this data, correlate it with each team’s success in the competition, and discuss observations and lessons learned based on survey results and the authors’ personal experiences during the challenge. Note to Practitioners —Perception, motion planning, grasping, and robotic system engineering have reached a level of maturity that makes it possible to explore automating simple warehouse tasks in semistructured environments that involve high-mix, low-volume picking applications. This survey summarizes lessons learned from the first Amazon Picking Challenge, highlighting mechanism design, perception, and motion planning algorithms, as well as software engineering practices that were most successful in solving a simplified order fulfillment task. While the choice of mechanism mostly affects execution speed, the competition demonstrated the systems challenges of robotics and illustrated the importance of combining reactive control with deliberative planning.

In this paper, a motion planning method is proposed for an autonomous photo-taking robot. The developed human-robot interactive motion planning and control system is based on face recognition and facial pose estimation of a target person. The target person's location is estimated after combined with robot self-localization result. Then the goal position and orientation of the robot are determined for autonomous photo-taking of the specific person. An APP for mobile phones has been integrated to select the target person and transmit the command to the robot for photo taking. After receiving the command, the robot autonomously navigates to the goal location and shoots pictures of the target person. Experimental results show that the experimental mobile robot can recognize the specific person, regardless the person is stand alone or in a crowd. The robot completed two types of picture-taking tasks by using the proposed method.

This paper presents a new approach for kinodynamic online motion planning for Automated Guided Vehicles (AGVs) in industrial environments. AGVs normally transport large and heavy transport units such as Euro-pallets or mesh pallets. Nowadays just-in-time inventory management and lean production requires the transport of small transportation units. Thus a flexible material flow is needed that can not be fulfilled by continuous material handling devices like belt or roll conveyors. Our approach uses small holonomic AGVs that are equipped with safety laser range finders for our semi autonomous motion and path planning system. This system plans trajectories with respect to the kinodynamic constraints of the AGVs and the environment. The operator retains as much control as necessary and the vehicle can react online on course deviations and unforeseen changes in the environment.