Amir Rahmani

Road-following formation control of autonomous ground vehicles

This work presents a novel cooperative path planning for formation keeping robots traversing along a road with obstacles and possible narrow passages. A unique challenge in this problem is a requirement for spatial and temporal coordination between vehicles while ensuring collision and obstacle avoidance. A two-step approach is used for fast real-time planning. The first step uses the A* search on a spatiotemporally extended graph to generate an obstacle-free path for the agent while the second step refines this path through local optimization to comply with dynamic and other vehicle constraints. This approach keeps robots close to their intended formation while giving them flexibility to negotiate narrow passages and obstacles, adhering to any given constraints.

Altering UAV flight path by threatening collision

The ongoing transformation of air traffic control towards a decentralized decision-making system will allow all aircraft, UAS in particular, to automatically detect and resolve collisions. The decision process will use, among others sources, ADS-B information, shared by neighboring traffic. In such distributed systems, trustworthiness among aircraft become of primary importance. In this work we show that autonomous aircraft employing deterministic sense and avoid strategies can be forced into predetermined trajectories when their precise position and velocity are available to a potentially malicious craft. In other words, malicious pursuer players (real or hoaxed) taking advantage of shared data and predictable collision avoidance properties can dictate evader agent trajectory, which might not realize the threat at all. As shown by numerical simulations and ground robot experiments, combination of arcs and straight paths can be achieved and be used to arbitrarily control the evader.

CARACaS multi-agent maritime autonomy for unmanned surface vehicles in the Swarm II harbor patrol demonstration

This paper describes new autonomy technology that enabled a team of unmanned surface vehicles (USVs) to execute cooperative behaviors in the USV Swarm II harbor patrol demonstration and provides a description of autonomy performance in the event. The new developments extend the NASA Jet Propulsion Laboratory’s CARACaS (Control Architecture for Robotic Agent Command and Sensing) autonomy architecture, which provides foundational software infrastructure, core executive functions, and several default robotic technology modules. In Swarm II, CARACaS demonstrated higher levels of autonomy and more complex cooperation than previous on-water exercises, using full-sized vehicles and real-world sensing and communication. The core autonomous behaviors to support the harbor patrol scenario included Patrol, Track, Inspect, and Trail, providing the capability of finding all vessels entering the patrol area, keeping track of them, inspecting them to infer intent, and trailing suspect vessels. Significantly, CARACaS assumed responsibility for not only executing tasks safely and efficiently but also recognizing what tasks needed to be accomplished, given the current state of the world. Since the heterogeneous USV teams shared world model that evolved, such as due to (dis)appearance of vessels in the area or a change in health or availability of a USV, CARACaS replanned to generate and reallocate the new task list. Thus, human intervention was never required in the loop to task USVs during mission execution, though a supervisory role was supported in the autonomy system for mission monitoring and exception handling. Finally, CARACaS also ensured the USVs avoided hazards and obeyed the applicable rules of the road, using its local motion planning modules.

Fast Motion Planning for Agile Space Systems with Multiple Obstacles

In this paper, we develop a novel algorithm for spacecraft trajectory planning in an environment cluttered with many geometrically-fixed obstacles. The Spherical Expansion and Sequential Convex Programming (SE-SCP) algorithm first uses a spherical-expansion-based sampling algorithm to explore the workspace. Once a path is found from the start position to the goal position, the algorithm generates a locally optimal trajectory within the homotopy class using sequential convex programming. If the number of samples tends to infinity, then the SE-SCP trajectory converges to the globally optimal trajectory in the workspace. The SE-SCP algorithm is computationally efficient, therefore it can be used for real-time applications on resource-constrained systems. We also present results of numerical simulations and comparisons with existing algorithms.

Distributed Consensus-Based Kalman Filter Estimation and Control of Formation Flying Spacecraft: Simulation and Validation

By implementing a distributed consensus-based Kalman Filter, this paper illustrates the dynamics and control of several satellites under the Hill-Clohessy-Wiltshire equations. Through the use of local communication within a connected network, the satellites were able to move themselves to a desired formation by successfully estimating the states and control of the entire formation. This control scheme was shown to exhibit resilience to possible disturbances despite the environment’s sensitivities to initial conditions.

Autonomous guidance navigation and control for agile quadrotors using polynomial trajectory planning and li adaptive control

We address the challenge to allow efficient autonomous flight in real world environments, both indoor and outdoor. We use a straight-line SE-SCP to find an initial route through the environment and minimum snap trajectory generation using piecewise polynomials. Then, we implement an adaptive robust control able to address some robustness issues for quadrotors in outdoor flight, such as mass variation and wind disturbances. Coupling these techniques we allow highspeed and aggressive autonomous flight through obstacle-dense indoor environments, as well as address outdoor disturbances.

Mission modeling, planning, and execution module for teams of unmanned vehicles

Mission Modeling, Planning, and Execution Module (M2PEM) is a user friendly graphical framework for mission design and execution. It extends a subset of the Business Process Modeling and Notation (BPMN) 2.0 for robotic applications. Hierarchical abstractions fundamental to BPMN allow the mission to be naturally decomposed into interdependent parallel sequences of BPMN elements. M2PEM adapts these elements in a role based framework which uses collaborative control modalities as an atomic building block. Designed missions are able to consider situational data, external stimuli, and direct user interaction. Missions are directly executable using a resource manager and a ROS-based execution engine.

Adaptive formation control for route-following ground vehicles

A novel cooperative path planning framework is presented for maintaining formations along a desired, unknown route where spatial and temporal objectives must be considered. It uses a reference frame based on longitudinal spacing along the route and lateral orthogonal offsets to plan for route clearance and traversal around previously unknown obstacles. By defining desired positions in terms of offsets from the route, the spatial and temporal components can be decoupled. The spatial components are planned using a two-step planner for fast real-time planning. The spatially defined paths are passed to a speed adaptation algorithm for temporal considerations including inter-vehicle collision avoidance. The approach balances real-time obstacle avoidance, spatial structure of the formation, and inter-vehicle safety considerations.

Distributed Spatiotemporal Motion Planning for Spacecraft Swarms in Cluttered Environments

This paper focuses on trajectory planning for spacecraft swarms in cluttered environments, like debris fields or the asteroid belt. Our objective is to reconfigure the spacecraft swarm to a desired formation in a distributed manner while minimizing fuel and avoiding collisions among themselves and with the obstacles. In our prior work we proposed a novel distributed guidance algorithm for spacecraft swarms in static environments. In this paper, we present the Multi-Agent Moving-Obstacles Spherical Expansion and Sequential Convex Programming (MAMO SE-SCP) algorithm that extends our prior work to include spatiotemporal constraints such as time-varying, moving obstacles and desired time-varying terminal positions. In the MAMO SE-SCP algorithm, each agent uses a spherical-expansion-based sampling algorithm to cooperatively explore the time-varying environment, a distributed assignment algorithm to agree on the terminal position for each agent, and a sequential-convex-programming-based optimization step to compute the locally-optimal trajectories from the current location to the assigned time-varying terminal position while avoiding collision with other agent and the moving obstacles. Simulations results demonstrate that the proposed distributed algorithm can be used by a spacecraft swarm to achieve a time-varying, desired formation around an object of interest in a dynamic environment with many moving and tumbling obstacles.

Edge potential residuals in low-complexity aggregation behaviors

In multiagent robotics, formation control problems usually address the maintenance of inter-agent distances equal to a set of predefined values. This problem requires either agents to be distinguishable or some combinatorial optimization to be solved in order to find the most convenient target-to-agent assignment. However, in many applications or mission stages, group connectivity and collision free trajectories are in fact the only desired requirements, while the robots themselves are allowed to freely generate their spatial distribution. To this end, the simplest possible case corresponds to all agents trying to maintain the same relative scalar distance. However, since it is not possible to have more than d+1 equally separated points in a d-dimensional space, we investigate solutions where interagent distances are as close as possible to desired one. We introduce a quadratic potential proportional to the error from the desired distance and equilibrium with non-null potentials are investigated. Stability of the behavior resulting from our protocol is described using elements from rigidity theory and final results are presented in form of numerical simulations.