MP-RRT#: a Model Predictive Sampling-based Motion Planning Algorithm for Unmanned Aircraft Systems

This paper introduces a kinodynamic motion planning algorithm for Unmanned Aircraft Systems (UAS), called MP-RRT#. MP-RRT# joins the potentialities of RRT# with a strategy based on Model Predictive Control to efficiently solve motion planning problems under differential constraints. Similar to other RRT-based algorithms, MP-RRT# explores the map constructing an asymptotically optimal graph. In each iteration the graph is extended with a new vertex in the reference state of the UAS. Then, a forward simulation is performed using a Model Predictive Control strategy to evaluate the motion between two adjacent vertices, and a trajectory in the state space is computed. As a result, the MP-RRT# algorithm eventually generates a feasible trajectory for the UAS satisfying dynamic constraints. Simulation results obtained with a simulated drone controlled with the PX4 autopilot corroborate the validity of the MP-RRT# approach.

Incorporating Multi-Modal Travel Planning into an Agent-Based Model: A Case Study at the Train Station Kellinghusenstraße in Hamburg

Models can provide valuable decision support in the ongoing effort to create a sustainable and effective modality mix in urban settings. Modern transportation infrastructures must meaningfully combine public transport with other mobility initiatives such as shared and on-demand systems. The increase of options and possibilities in multi-modal travel implies an increase in complexity when planning and implementing such an infrastructure. Multi-agent systems are well-suited for addressing questions that require an understanding of movement patterns and decision processes at the individual level. Such models should feature intelligent software agents with flexible internal logic and accurately represent the core functionalities of new modalities. We present a model in which agents can choose between owned modalities, station-based bike sharing modalities, and free-floating car sharing modalities as they exit the public transportation system and seek to finish their personal multi-modal trip. Agents move on a multi-modal road network where dynamic constraints in route planning are evaluated based on an agent’s query. Modality switch points (MSPs) along the route indicate the locations at which an agent can switch from one modality to the next (e.g., a bike rental station to return a used rental bike and continue on foot). The technical implementation of MSPs within the road network was a central focus in this work. To test their efficacy in a controlled experimental setting, agents optimized only the travel time of their multi-modal routes. However, the functionalities of the model enable the implementation of different optimization criteria (e.g., financial considerations or climate neutrality) and unique agent preferences as well. Our findings show that the implemented MSPs enable agents to switch between modalities at any time, allowing for the kind of versatile, individual, and spontaneous travel that is common in modern multi-modal settings.

Design and Implementation of Symmetric Legged Robot for Highly Dynamic Jumping and Impact Mitigation

Aiming at highly dynamic locomotion and impact mitigation, this paper proposes the design and implementation of a symmetric legged robot. Based on the analysis of the three-leg topology in terms of force sensitivity, force production, and impact mitigation, the symmetric leg was designed and equipped with a high torque density actuator, which was assembled by a custom motor and two-stage planetary. Under the kinematic and dynamic constraints of the robot system, a nonlinear optimization for high jumping and impact mitigation is proposed with consideration of the peak impact force at landing. Finally, experiments revealed that the robot achieved a jump height of 1.8 m with a robust landing, and the height was equal to approximately three times the leg length.

Overuse-related instability of the elbow: the role of CT-arthrography

The elbow is a complex joint whose biomechanical function is granted by the interplay and synergy of various anatomical structures. Articular stability is achieved by both static and dynamic constraints, which consist of osseous as well as soft-tissue components. Injuries determining instability frequently involve several of these structures. Therefore, accurate knowledge of regional anatomy and imaging findings is fundamental for a precise diagnosis and an appropriate clinical management of elbow instability. This review focuses particularly on the varied appearance of overuse-related elbow injuries at CT-arthrography.

Parallel Swarm Intelligent Motion Planning with Energy-Balanced for Multirobot in Obstacle Environment

Multirobot motion planning is always one of the critical techniques in edge intelligent systems, which involve a variety of algorithms, such as map modeling, path search, and trajectory optimization and smoothing. To overcome the slow running speed and imbalance of energy consumption, a swarm intelligence solution based on parallel computing is proposed to plan motion paths for multirobot with many task nodes in a complex scene that have multiple irregularly-shaped obstacles, which objective is to find a smooth trajectory under the constraints of the shortest total distance and the energy-balanced consumption for all robots to travel between nodes. In a practical scenario, the imbalance of task allocation will inevitably lead to some robots stopping on the way. Thus, we firstly model a gridded scene as a weighted MTSP (multitraveling salesman problem) in which the weights are the energies of obstacle constraints and path length. Then, a hybridization of particle swarm and ant colony optimization (GPSO-AC) based on a platform of Compute Unified Device Architecture (CUDA) is presented to find the optimal path for the weighted MTSPs. Next, we improve the A ∗ algorithm to generate a weighted obstacle avoidance path on the gridded map, but there are still many sharp turns on it. Therefore, an improved smooth grid path algorithm is proposed by integrating the dynamic constraints in this paper to optimize the trajectory smoothly, to be more in line with the law of robot motion, which can more realistically simulate the multirobot in a real scene. Finally, experimental comparisons with other methods on the designed platform of GPUs demonstrate the applicability of the proposed algorithm in different scenarios, and our method strikes a good balance between energy consumption and optimality, with significantly faster and better performance than other considered approaches, and the effects of the adjustment coefficient q on the performance of the algorithm are also discussed in the experiments.

Tangent Based-Path Path Planning with Distinctive Topologies in a Dynamic Environment

This manuscript proposes a novel tangent-graph-based method that provides all distinctive topology paths, as a set of trajectories that can not be transformed into each other by gradual bending and stretching without colliding with obstacles. As the global optimal trajectory (limited by dynamic constraints) may not correspond to the shortest path (almost without dynamic constraints), it is important to provide all distinctive topology paths rather than just the globally shortest one. One of the application is provide initial paths for trajectory planning methods, such as time elastic band (TEB) and dynamic window approach (DWA). Considering that a mobile platform is always working in a dynamic changing environment and that update the whole graph is time consuming, a dynamic rectify method is proposed to update tangents according to local map provided by sensor reading. Finally, results under real grid maps are presented.