Wenhao Luo

A suboptimal and analytical solution to mobile robot trajectory generation amidst moving obstacles

In this paper, we present a suboptimal and analytical solution to the trajectory generation of mobile robots operating in a dynamic environment with moving obstacles. The proposed solution explicitly addresses both the robot kinodynamic constraints and the geometric constraints due to obstacles while ensuring the suboptimal performance to a combined performance metric. In particular, the proposed design is based on a family of parameterized trajectories, which provides a unified way to embed the kinodynamic constraints, geometric constraints, and performance index into a set of parameterized constraint equations. To that end, the suboptimal solution to the constrained optimization problem can be analytically obtained. The solvability conditions to the constraint equations are explicitly established, and the proposed solution enhances the methodologies of real-time path planning for mobile robots with kinodynamic constraints. Both the simulation and experiment results verify the effectiveness of the proposed method.

Asynchronous distributed information leader selection in robotic swarms

This paper presents asynchronous distributed algorithms for information leader selection in multi-robot systems based on local communication between each robot and its direct neighbours in the systems communication graph. In particular, the information leaders refer to a small subset of robots that are near the boundary of the swarm and suffice to characterize the swarm boundary information. The leader selection problem is formulated as finding a core set that can be used to compute the Minimum-Volume Enclosing Ellipsoid (MVEE) representing the swarm boundary. Our algorithms extract this core set in a fully distributed manner and select core set members as information leaders, thus extending abstract centralized MVEE core set algorithms for robotic swarm applications. We consider different communication conditions (e.g. dynamic network topology) and system configurations (e.g. anonymous robots or uniquely identified robots) and present a variety of approaches for core set selection with associated proofs for convergence. Results for simulated swarms of 50 robots and experiments with a swarm of 10 TurtleBots are provided to evaluate the effectiveness of the proposed algorithms.

Distributed dynamic priority assignment and motion planning for multiple mobile robots with kinodynamic constraints

We present a distributed on-line coordinated motion planning approach for a group of mobile robots moving amidst dynamic obstacles. The objective for the motion planning is to minimize the total distance traveled by the robots as well as the danger of deadlock. Kinematic constraints, robot-obstacle collision avoidance constraints, and velocity/acceleration constraints are explicitly considered in individual robots motion planner. A dynamic priority based scheme is proposed to deal with pair-wise inter-robot collision constraints. In particular, we model the assignment of priority into a minimum linear ordering problem (MLOP). We prove that the objective function of the MLOP is supermodular and propose a decentralized supermodular linear ordering algorithm that interleaves dynamic priority assignment and planning for the robots, such that the overall path length and the danger of deadlock are both minimized. Simulation results are provided to show the effectiveness of the proposed approach.

Distributed knowledge leader selection for multi-robot environmental sampling under bandwidth constraints

In many multi-robot applications such as target search, environmental monitoring and reconnaissance, the multi-robot system operates semi-autonomously, but under the supervision of a remote human who monitors task progress. In these applications, each robot collects a large amount of task-specific data that must be sent to the human periodically to keep the human aware of task progress. It is often the case that the human-robot communication links are extremely bandwidth constrained and/or have significantly higher latency than inter-robot communication links, so it is impossible for all robots to send their task-specific data together. Thus, only a subset of robots, which we call the knowledge leaders, can send their data at a time. In this paper, we study the knowledge leader selection problem, where the goal is to select a subset of robots with a given cardinality that transmits the most informative task-specific data for the human. We prove that the knowledge leader selection is a submodular function maximization problem under explicit conditions and present a novel distributed submodular optimization algorithm that has the same approximation guarantees as the centralized greedy algorithm. The effectiveness of our approach is demonstrated using numerical simulations.

A unified optimization method for real-time trajectory generation of mobile robots with kinodynamic constraints in dynamic environment

This paper presents an efficient analytic method for a mobile robot to determine a collision-free trajectory with unified optimization. The robot kinodynamic constraints and the geometric constraints due to obstacles are addressed by considering a set of constrained inequalities from parameterized trajectories model. Two optimal performance matrices are employed to assess optimization problems. Particularly, both the constraints and performance functions are incorporated by similar forms in terms of trajectory parameters, which thereby can be considered in a uniform way. A parameter space of adjustable trajectory parameters is constructed to solve the unified optimization problem with constraints and results in more flexible and better optimal performance. The proposed approach is complete and enhances methods of deterministic real-time planning by overcoming typical drawbacks as intermediate configuration, curvature discontinuities, vertical singularities and incomplete optimization. Simulation results verify the validity and superiority of the proposed method.

An improved parameterized approach for real time optimal motion planning of AUV moving in dynamic environment

In this paper, we present an improved analytic method to the optimal trajectory generation of an autonomous underwater vehicle (AUV) in a dynamic environment. The proposed approach explicitly incorporates both the AUV kinematic and the geometric constraints due to dynamic obstacles and the terrain while rendering the near-shortest path by a performance index related to the path length. In particular, the proposed design is based on a family of parameterized trajectories determined by three adjustable parameters, which provides a unified way to reformulate the geometric constraints and performance index into a set of parameterized constraint equations. To that end, such a constrained optimization problem boils down to optimize those adjustable parameters, which can be analytically solved in the parameter space. The proposed solution enhances the methodologies of real-time path planning for robots in 3D environment. Simulation results verify the effectiveness of the proposed method.

Optimal real-time trajectory planning for a fixed wing vehicle in 3D dynamic environment

In this paper, an on-line motion planner is described to determine an optimal and collision-free trajectory for fixed wing vehicles moving in a 3D space populated with static hills and movable obstacles. The proposed method is mainly based on the polynomial parameterization of trajectories, which is beneficial to explicitly consider the kinematic constraints and the geometric constraints resulted from obstacles. The near shortest trajectory is chosen by optimizing a performance index with respect to path length. By design, the optimal trajectory planning could boil down to solve a constrained optimization problem with respect to three adjustable path parameters, which can be well handled in a transformed 3D parameter space. The resultant trajectories satisfy all boundary conditions and the analytically derived control inputs are always smooth to be implemented on real-time planning. Computer simulation results verify the effectiveness of the proposed approach.

Decentralized coordinated motion for a large team of robots preserving connectivity and avoiding collisions

We consider the general problem of moving a large number of networked robots toward a goal position through a cluttered environment while preserving network communication connectivity and avoiding both inter-robot collisions and collision with obstacles. In contrast to previous approaches that either plan complete paths for each individual robot in the high-dimensional joint configuration space or control the robot group as a whole with explicit constraints on the groups boundary and inter-robot pairwise distance, we propose a novel decentralized online behavior-based algorithm that relies on the topological structure of the multi-robot communication and sensing graphs to solve this problem. We formally describe the communication graph as a simplicial complex that enables robots to iteratively identify the frontier nodes and coordinate forward motion through the sensing graph. This approach is proved to automatically deform robot teams for collision avoidance and always preserve connectivity. The effectiveness of our approach is demonstrated using numerical simulations. The algorithm is shown to scale linearly in the number of robots.

Online decision making for stream-based robotic sampling via submodular optimization

We consider the problem of online robotic sampling in environmental monitoring tasks where the goal is to collect k best samples from n sequentially occurring measurements. In contrast to many existing works that seek to maximize the utility of the selected samples online, we aim to find the cardinality constrained subset of streaming measurements under irrevocable sampling decisions so that the prediction over untested measurements is most accurate. Using the information theoretic criterion, we present an online submodular algorithm for stream-based sample selection with a provable performance bound. We demonstrate the effectiveness of our algorithm via simulations of information gathering from indoor static sensors.

Handling state uncertainty in distributed information leader selection for robotic swarms

In many scenarios involving human interaction with a remote swarm, the human operator needs to be periodically updated with state information from the robotic swarm. A complete representation of swarm state is high dimensional and perceptually inaccessible to the human. Thus, a summary representation is often required. In addition, it is often the case that the human-swarm communication channel is extremely bandwidth constrained and may have high latency. This motivates the need for the swarm itself to compute a summary representation of its own state for transmission to the human operator. The summary representation may be generated by selecting a subset of robots, known as the information leaders, whose own states suffice to give a bounded approximation of the entire swarm, even in the presence of uncertainty. In this paper, we propose two fully distributed asynchronous algorithms for information leader selection that only rely on inter-robot local communication. In particular, by representing noisy robot states as error ellipsoids with tunable confidence level, the information leaders are selected such that the Minimum-Volume Covering Ellipsoid (MVCE) summarizes the noisy swarm state boundary. We provide bounded optimality analysis and proof of convergence for the algorithms. We present simulation results demonstrating the performance and effectiveness of the proposed algorithms.