Autonomous vehicles are expected to play a key role in the future of urban transportation systems, as they offer potential for additional safety, increased productivity, greater accessibility, better road efﬁciency, and positive impact on the environment. Research in autonomous systems has seen dramatic advances in recent years, due to the increases in available computing power and reduced cost in sensing and computing technologies, resulting in maturing technological readiness level of fully autonomous vehicles. The objective of this paper is to provide a general overview of the recent developments in the realm of autonomous vehicle software systems. Fundamental components of autonomous vehicle software are reviewed, and recent developments in each area are discussed.

Perception, Planning, Control, and Coordination for Autonomous Vehicles

Humanoid robots, unmanned rovers, entertainment pets, drones, and so on are great examples of mobile robots. They can be distinguished from other robots by their ability to move autonomously, with ...

https://journals.sagepub.com/doi/pdf/10.1177/1729881419839596

A review of mobile robots: Concepts, methods, theoretical framework, and applications

This paper presents a motion control method for mobile robots in partially unknown environments populated with moving obstacles. The proposed method is based on the integration of focused D* search algorithm and dynamic window local obstacle avoidance algorithm with some adaptations that provide efficient avoidance of moving obstacles. The moving obstacles are modelled as moving cells in the occupancy grid map and their motion is predicted by applying a procedure similar to the dynamic window approach. The collision points of the robot predicted trajectory and moving cells predicted trajectories form the new active obstacles in the environment, which should be avoided. The algorithms are implemented and verified using a Pioneer 3DX mobile robot equipped with laser range finder.

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Dynamic window based approach to mobile robot motion control in the presence of moving obstacles

Most of present work for autonomous navigation in dynamic environment doesn't take into account the dynamics of the obstacles or the limits of the perception system. To face these problems we applied the probabilistic velocity obstacle (PVO) approach (Kluge and Prassler, 2004) to a dynamic occupancy grid. The paper presents a method to estimate the probability of collision where uncertainty in position, shape and velocity of the obstacles, occlusions and limited sensor range contribute directly to the computation. A simple navigation algorithm is then presented in order to apply the method to collision avoidance and goal driven control. Simulation results show that the robot is able to adapt its behaviour to the level of available knowledge and navigate safely among obstacles with a constant linear velocity. Extensions to non-linear, non-constant velocities are proposed.

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Dynamic Obstacle Avoidance in uncertain environment combining PVOs and Occupancy Grid

A key skill for mobile robots is the ability to navigate efficiently through their environment. In the case of social or assistive robots, this involves navigating through human crowds. Typical performance criteria, such as reaching the goal using the shortest path, are not appropriate in such environments, where it is more important for the robot to move in a socially adaptive manner such as respecting comfort zones of the pedestrians. We propose a framework for socially adaptive path planning in dynamic environments, by generating human-like path trajectory. Our framework consists of three modules: a feature extraction module, inverse reinforcement learning (IRL) module, and a path planning module. The feature extraction module extracts features necessary to characterize the state information, such as density and velocity of surrounding obstacles, from a RGB-depth sensor. The inverse reinforcement learning module uses a set of demonstration trajectories generated by an expert to learn the expert’s behaviour when faced with different state features, and represent it as a cost function that respects social variables. Finally, the planning module integrates a three-layer architecture, where a global path is optimized according to a classical shortest-path objective using a global map known a priori, a local path is planned over a shorter distance using the features extracted from a RGB-D sensor and the cost function inferred from IRL module, and a low-level system handles avoidance of immediate obstacles. We evaluate our approach by deploying it on a real robotic wheelchair platform in various scenarios, and comparing the robot trajectories to human trajectories.

/pdf/socially-adaptive-path-planning-in-human-environments-using-3wb9yafjiu.pdf

Socially Adaptive Path Planning in Human Environments Using Inverse Reinforcement Learning

In this paper, the problem of path planning for active simultaneous localization and mapping (SLAM) is addressed. In order to improve its localization accuracy while autonomously exploring an unknown environment the robot needs to revisit positions seen before. To that end, we propose a path planning algorithm for active SLAM that continuously improves robot’s localization while moving smoothly, without stopping, toward a goal position. The algorithm is based on the D* shortest path graph search algorithm with negative edge weights for finding the shortest path taking into account localization uncertainty. The proposed path planning algorithm is suitable for exploration of highly dynamic environments with moving obstacles and dynamic changes in localization demands. While the algorithm operation is illustrated in simulation experiments, its effectiveness is verified experimentally in real-world scenarios.

Path Planning for Active SLAM Based on the D* Algorithm With Negative Edge Weights

In this paper we present a motion control method for mobile robots in partially known indoor environments based on integration of focussed D* search algorithm and dynamic window local obstacle avoidance algorithm. While focussed D* generates global path, dynamic window local obstacle avoider generates the admissible robot trajectories that ensure safe robot motion. A simple and efficient procedure to the selection of appropriate motion commands based upon alignment of acquired trajectories and global geometric path is proposed. The initial a priori knowledge is used about the environment in the form of the occupancy grid map that is incrementally updated in runtime. The algorithms are verified both by simulation and experiments with a Pioneer 2DX mobile robot equipped with laser range finder.

An integrated approach to real-time mobile robot control in partially known indoor environments

A receding horizon control (RHC) algorithm for convergent navigation of a differential drive mobile robot is proposed. Its objective function utilizes a local-minima-free navigation function to measure the cost-to-goal over the robot trajectory. The navigation function is derived from the path-search algorithm over a discretized 2-D search space. The proposed RHC navigation algorithm includes a systematic procedure for the generation of feasible control sequences. The optimal value of the objective function is employed as a Lyapunov function to prove a finite-time convergence of the discrete-time nonlinear closed-loop system to the goal state. The developed RHC navigation algorithm inherits fast replanning capability from the $D$ * search algorithm, which is experimentally verified in changing indoor environments. The performance of the developed RHC navigation algorithm is compared with the state-of-the-art sample-based motion planning algorithm based on lattice graphs, which is combined with a trajectory tracking controller. The RHC navigation algorithm produces faster motion to the goal with significantly lower computational costs and it does not need any controller tuning to cope with diverse obstacle configurations.

Receding Horizon Control for Convergent Navigation of a Differential Drive Mobile Robot

Inspired by the Hierarchical D* (HD*) algorithm of D. Cagigas (Cagigas, 2005), in this paper we introduce a novel hierarchical path planning algorithm called Focussed Hierarchical D* (FHD*). Unlike the original HD* algorithm, the FHD* algorithm guarantees the optimality of the global path, and requires considerably less time for the path replanning operations. This is achieved by several modifications: (i) optimal placement of the so-called bridge nodes needed for hierarchy creation, (ii) focusing the search around the optimal path, which reduces the search area without loss of optimality, and (iii) introduction of partial starts and partial goals, which further reduce computational time of replanning operations. The FHD* algorithm was tested in a multiroom indoor environment and compared to the original HD* algorithm, non-hierarchical D* algorithm, and Focussed D* algorithm under the same conditions. The FHD* algorithm significantly outperforms other algorithms with respect to the computational time. Furthermore, it can be easily extended to the problem of path planning between different floors or buildings.

Marija Seder

Papers

Dynamic window based approach to mobile robot motion control in the presence of moving obstacles

Path Planning for Active SLAM Based on the D* Algorithm With Negative Edge Weights

An integrated approach to real-time mobile robot control in partially known indoor environments

Receding Horizon Control for Convergent Navigation of a Differential Drive Mobile Robot

Hierarchical path planning of mobile robots in complex indoor environments