Achieving precise trajectory tracking performance from an autonomous vehicle requires a carefully tuned controller. However, such a task is arduous which necessitates iterative testing. Furthermore, changes in traction condition render the offline tuned gains less viable. Hence, this paper proposes an adaptive tuning strategy to improve the performance of lateral trajectory tracking. In essence, the tuning framework utilizes fuzzy inference to update the controller gains online. The underlying rules are based on intuitive ideas that facilitate easy deployment. Moreover, the efficacy of the tuning strategy has been experimentally evaluated in multi-scenario conditions. The obtained results validate that the adaptive fuzzy-based tuning strategy consistently improves the tracking performance with a decrease in the tracking error with values of up to 73%. This paper is an effort to showcase the importance of a reliable tuning strategy towards motion control of autonomous vehicles.

Autonomous navigation in real-world industrial environments is a challenging task in many respects. One of the key open challenges is fast planning and execution of trajectories to reach arbitrary target positions and orientations with high accuracy and precision, while taking into account non-holonomic vehicle constraints. In recent years, lattice-based motion planners have been successfully used to generate kinematically and kinodynamically feasible motions for non-holonomic vehicles. However, the discretized nature of these algorithms induces discontinuities in both state and control space of the obtained trajectories, resulting in a mismatch between the achieved and the target end pose of the vehicle. As endpose accuracy is critical for the successful loading and unloading of cargo in typical industrial applications, automatically planned paths have not be widely adopted in commercial AGV systems. The main contribution of this paper addresses this shortcoming by introducing a path smoothing approach, which builds on the output of a lattice-based motion planner to generate smooth drivable trajectories for non-holonomic industrial vehicles. In real world tests presented in this paper we demonstrate that the proposed approach is fast enough for online use (it computes trajectories faster than they can be driven) and highly accurate. In 100 repetitions we achieve mean end-point pose errors below 0.01 meters in translation and 0.002 radians in orientation. Even the maximum errors are very small: only 0.02 meters in translation and 0.008 radians in orientation.

Dynamic models describing the ball-robot motion form the basis for developments in ball-robot mechanics and motion control systems. For this paper, we have conducted a literature review of decoupled forward-motion models for pendulum-driven ball-shaped robots. The existing models in the literature apply several different conventions in system definition and parameter notation. Even if describing the same mechanical system, the diversity in conventions leads into dynamic models with different forms. As a result, it is difficult to compare, reproduce and apply the models available in the literature. Based on the literature review, we reformulate all common variations of decoupled dynamic forward-motion models using a unified notation and formulation. We have verified all reformulated models through simulations, and present the simulation results for a selected model. In addition, we demonstrate the different system behavior resulting from different ways to apply the pendulum reaction torque, a variation that can be found in the literature. For anyone working with the ball-robots, the unified compilation of the reformulated dynamic models provides an easy access to the models, as well as to the related work.

Industrial environments are rarely static and often their configuration is continuously changing due to the material transfer flow. This is a major challenge for infrastructure free localization systems. In this paper we address this challenge by introducing a localization approach that uses a dual-timescale approach. The proposed approach - Dual-Timescale Normal Distributions Transform Monte Carlo Localization (DT-NDT-MCL) - is a particle filter based localization method, which simultaneously keeps track of the pose using an apriori known static map and a short-term map. The short-term map is continuously updated and uses Normal Distributions Transform Occupancy maps to maintain the current state of the environment. A key novelty of this approach is that it does not have to select an entire timescale map but rather use the best timescale locally. The approach has real-time performance and is evaluated using three datasets with increasing levels of dynamics. We compare our approach against previously proposed NDT-MCL and commonly used SLAM algorithms and show that DT-NDT-MCL outperforms competing algorithms with regards to accuracy in all three test cases.

Unmanned autonomous systems offer safety benefits for potentially hazardous environments. The evaluation of the performance of such systems is challenging because hazardous environments, i.e. due to flood or fire, are constantly changing. In this paper we introduce a system for simulation-based risk assessment of a fleet of autonomous machines performing a rescue mission. The simulation is based on combining the dynamic propagation of the hazard and the real-time simulation of the fleet. Our use case analyses an underground rescue mission under a pipeline breakage. The propagation of the flood is simulated together with the actions of robots. We evaluate the system by simulating several scenarios and measuring the mission success for each. As a result we show that a simulation based risk assessment is a feasible option for evaluating the performance of complex distributed systems.

Autonomous vehicles operating in real-world industrial environments have to overcome numerous challenges, chief among which is the creation and maintenance of consistent 3D world models. This paper proposes to address the challenges of online real-world mapping by building upon previous work on compact spatial representation and formulating a novel 3D mapping approach - the Normal Distributions Transform Occupancy Map (NDT-OM). The presented algorithm enables accurate real-time 3D mapping in large-scale dynamic environments employing a recursive update strategy. In addition, the proposed approach can seamlessly provide maps at multiple resolutions allowing for fast utilization in high-level functions such as localization or path planning. Compared to previous approaches that use the NDT representation, the proposed NDT-OM formulates an exact and efficient recursive update formulation and models the full occupancy of the map.

Operating in vegetated environments is a major challenge for autonomous robots. Obstacle detection based only on geometric features causes the robot to consider foliage, for example, small grass tussocks that could be easily driven through, as obstacles. Classifying vegetation does not solve this problem since there might be an obstacle hidden behind the vegetation. In addition, dense vegetation typically needs to be considered as an obstacle. This paper addresses this problem by augmenting probabilistic traversability map constructed from laser data with ultra-wideband radar measurements. An adaptive detection threshold and a probabilistic sensor model are developed to convert the radar data to occupancy probabilities. The resulting map captures the fine resolution of the laser map but clears areas from the traversability map that are induced by obstacle-free foliage. Experimental results validate that this method is able to improve the accuracy of traversability maps in vegetated environments.

In this paper we introduce a method for learning motion patterns in dynamic environments. Representations of dynamic environments have recently received an increasing amount of attention in the research community. Understanding dynamic environments is seen as one of the key challenges in order to enable autonomous navigation in real-world scenarios. However, representing the temporal dimension is a challenge yet to be solved. In this paper we introduce a spatial representation, which encapsulates the statistical dynamic behavior observed in the environment. The proposed Conditional Transition Map (CTMap) is a grid-based representation that associates a probability distribution for an object exiting the cell, given its entry direction. The transition parameters are learned from a temporal signal of occupancy on cells by using a local-neighborhood cross-correlation method. In this paper, we introduce the CTMap, the learning approach and present a proof-of-concept method for estimating future paths of dynamic objects, called Conditional Probability Propagation Tree (CPPTree). The evaluation is done using a real-world dataset collected at a busy roundabout.

Industrial applications often impose hard requirements on the precision of autonomous vehicle systems. As a consequence industrial Automatically Guided Vehicle (AGV) systems still use high-cost infrastructure based positioning solutions. In this paper we propose a map based localization method that fulfills the requirements on precision and repeatability, typical for industrial application scenarios. The proposed method - Normal Distributions Transform Monte Carlo Localization (NDT-MCL) is based on a well established probabilistic framework. In a novel contribution, we formulate the MCL localization approach using the Normal Distributions Transform (NDT) as an underlying representation for both map and sensor data. By relaxing the hard discretization assumption imposed by grid-map models and utilizing the piece-wise continuous NDT representation the proposed algorithm achieves substantially improved accuracy and repeatability. The proposed NDT-MCL algorithm is evaluated using offline data sets from both a laboratory and a real-world industrial environments. Additionally, we report a comparison of the proposed algorithm to grid-based MCL and to a commercial localization system when used in a closed-loop with the control system of an AGV platform. In all tests the proposed algorithm is demonstrated to provide performance superior to that of standard grid-based MCL and comparable to the performance of the commercial infrastructure based positioning system.

Autonomous vehicles operating in real-world industrial environments have to overcome numerous challenges, chief among which is the creation and maintenance of consistent 3D world models. This paper focuses on a particularly important challenge: mapping in dynamic environments. We introduce several improvements to the recently proposed Normal Distributions Transform Occupancy Map (NDT-OM) aimed for efficient mapping in dynamic environments. A careful consistency analysis is given based on convergence and similarity metrics specifically designed for evaluation of NDT maps in dynamic environments. We show that in the context of mapping with known poses the proposed method results in improved consistency and in superior runtime performance, when compared against 3D occupancy grids at the same size and resolution. Additionally, we demonstrate that NDT-OM features real-time performance in a highly dynamic 3D mapping and tracking scenario with centimeter accuracy over a 1.5km trajectory.

Autonomous vehicles operating in real-world industrial environments have to overcome numerous challenges, chief among which are the creation of consistent 3D world models and the simultaneous tracking of the vehicle pose with respect to the created maps. In this paper we integrate two recently proposed algorithms in an online, near-realtime mapping and tracking system. Using the Normal Distributions Transform (NDT), a sparse Gaussian Mixture Model, for representation of 3D range scan data, we propose a frame-to-model registration and data fusion algorithm - NDT Fusion. The proposed approach uses a submap indexing system to achieve operation in arbitrarily-sized environments. The approach is evaluated on a publicly available city-block sized data set, achieving accuracy and runtime performance significantly better than current state of the art. In addition, the system is evaluated on a data set covering ten hours of operation and a trajectory of 7.2km in a real-world industrial environment, achieving centimeter accuracy at update rates of 5-10 Hz.

In this paper we propose a new grid based approach to model a dynamic environment. Each grid cell is assumed to be an independent Markov chain (iMac) with two states. The state transition parameters are learned online and modeled as two Poisson processes. As a result, our representation not only encodes the expected occupancy of the cell, but also models the expected dynamics within the cell. The paper also presents a strategy based on recency weighting to learn the model parameters from observations that is able to deal with non-stationary cell dynamics. Moreover, an interpretation of the model parameters with discussion about the convergence rates of the cells is presented. The proposed model is experimentally validated using offline data recorded with a Laser Guided Vehicle (LGV) system running in production use.

To know the pose of the robot is one of the central requirements in many applications. A localization algorithm should be robust, it should give the estimate of unreliability and it should be able to recover from errors. The above requirements are often trade offs with the computational complexity. This paper presents a global localization algorithm that matches a local point map (acquired e.g. with a laser range finder) to a global map. The algorithm makes a discrete search using a best-first branch and bound method to efficiently compute the globally optimal pose estimate. Moreover, the degree of ambiguity of the pose estimate can be determined as the algorithm yields all potential pose solution candidates within the search space. Experimental results are given to show the behavior and performance analysis of the algorithm.

This paper presents a human dead-reckoning system for beaconless indoor positioning. The system is based on traditional dead-reckoning sensors like compass, gyro, and accelerometers. Due to the difficult kinematics of a human, there are no ready solutions for the odometry. This problem is solved by using a self-made stride length measurement unit and laser odometry. All the sensors are integrated to a complete system including sensor fusion. The functionality of the integrated system is verified with tests in an office environment. Finally the test results are analyzed.