Multi-Robot Mission Planning with Static Energy Replenishment

Li, Bingxi; Moridian, Barzin; Kamal, Anurag; Patankar, Sharvil; Mahmoudian, Nina

doi:10.1007/s10846-018-0897-2

Cited by 24 publications

(18 citation statements)

References 25 publications

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“…Note that inside an active node (obround region), we treat the center of the master circle (illustrated with a dark blue marker in Figure 5) as the primary target/reference location, q g . If the robot's cartesian position locates inside the master circle (i.e., the circle centered around q g and has a radius of r ), we directly apply the control policy in (3). Note that this control policy can both asymptotically stabilize the motion around q g while also satisfying that trajectories strictly remains inside the master circle, which is entirely located inside the obround zone, ensuring that no constraint violation occurs inside the obround zone.…”

Section: Algorithm 1 Obround Tree Generationmentioning

confidence: 99%

“…The fundamental goal of the motion planner module in a robotic system is to compute the set of control actions such that the robot (or robots) can safely execute the given motion task without colliding with the static and dynamic obstacles in the environment. The practical duty of the planner can be to drive the robot to a final goal configuration (e.g., autonomous parking [1]), to follow the desired trajectory (e.g., welding robots [2]), to execute a complex mission which is composed of several subtasks [3], etc. Early stages of motion planning algorithms had mainly relied on generating offline open-loop trajectories (or paths) [4][5][6][7] because of dominant application domain was industrial robotic applications.…”

Section: Introductionmentioning

confidence: 99%

“…Figure 8 illustrates a sample result from our simulations in Figure 8. In this example, initial and goal positions are equal to q initial = [5,3]m, and q goal = [11,3] respectively. In these samples, D-SNG and RCT algorithms found solutions with 247 and 1769 # nodes respectively, where as Obround-Trees reached the solution with only 122 # nodes.We can see that for these specific examples, Obround-Trees is the best algorithm in terms of sparsity.…”

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confidence: 99%

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Obround trees: sparsity enhanced feedback motion planning of differential drive robotic systems

Ankaralı¹

2021

Turkish Journal of Electrical Engineering and Computer Sciences

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Robot motion planning & control is one of the most critical and prevalent problems in the robotics community. Even though original motion planning algorithms had relied on "open-loop" strategies and policies, researchers and engineers have been focusing on feedback motion planning and control algorithms due to the uncertainties, such as process and sensor noise, of autonomous robotic applications. Recently, several studies proposed some robust feedback motion planning strategies based on sparsely connected safe zones. In this class of planning and control policies, local control policy inside a single zone computes and feeds the control actions that can drive the robot to a different connected region while guaranteeing that the robot never exceeds the boundaries of the active area until convergence. While most of these studies apply only to holonomic robotic models, a recent motion planning method (RCT) can solve the motion planning and navigation problems for unicycle like robotic systems based on a randomly connected circular region tree. In this paper, we propose a new/updated feedback motion planning algorithm that substantially enhances the sparsity, computational feasibility, and input effort compared to their methodology. The new algorithm generates a sparse neighborhood tree as a set of connected obround zones. Obround regions cover larger areas inside the environment, thus leads to a more sparse tree structure. During navigation, we modify the nonlinear control policy adopted in RCT method to handle the obround shaped zones. The feedback control policy navigates the robot model from one obround zone to the adjacent area in the tree structure, ensuring it stays inside the active region's boundaries and asymptotically reaches the connected obround. We demonstrate the effectiveness and validity of the algorithm on simulation studies. Our Monte Carlo simulations show that our enhancement to the original algorithm probabilistically improves the sparsity, and produces smoother trajectories compared to two motion planning algorithms that rely on sampling based neighborhood structures.

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Section: Algorithm 1 Obround Tree Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Obround trees: sparsity enhanced feedback motion planning of differential drive robotic systems

Ankaralı¹

2021

Turkish Journal of Electrical Engineering and Computer Sciences

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“…Agents initial, maximum and common altitudes for increased connectivity are listed in Table 1. Upon completing each orbit, the recorded temperature measurements of each agent were averaged with the neighboring agents per Equation (1). Each agent separately applied a Gaussian process on the shared variable to estimate the temperature field around D points.…”

Section: Experiments Designmentioning

confidence: 99%

“…The success of an autonomous, robotic mission can be measured by the effectiveness and efficiency in completing the mission [1]. While physical resources (e.g., time, energy, power, and space) have historically dominated the metrics of success, more capable, complex cyber-physical agents require the judicious allocation of cyber resources (e.g., communication and computation) as well.…”

Section: Introductionmentioning

confidence: 99%

Co-Regulated Consensus of Cyber-Physical Resources in Multi-Agent Unmanned Aircraft Systems

2019

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Intelligent utilization of resources and improved mission performance in an autonomous agent require consideration of cyber and physical resources. The allocation of these resources becomes more complex when the system expands from one agent to multiple agents, and the control shifts from centralized to decentralized. Consensus is a distributed algorithm that lets multiple agents agree on a shared value, but typically does not leverage mobility. We propose a coupled consensus control strategy that co-regulates computation, communication frequency, and connectivity of the agents to achieve faster convergence times at lower communication rates and computational costs. In this strategy, agents move towards a common location to increase connectivity. Simultaneously, the communication frequency is increased when the shared state error between an agent and its connected neighbors is high. When the shared state converges (i.e., consensus is reached), the agents withdraw to the initial positions and the communication frequency is decreased. Convergence properties of our algorithm are demonstrated under the proposed co-regulated control algorithm. We evaluated the proposed approach through a new set of cyber-physical, multi-agent metrics and demonstrated our approach in a simulation of unmanned aircraft systems measuring temperatures at multiple sites. The results demonstrate that, compared with fixed-rate and event-triggered consensus algorithms, our co-regulation scheme can achieve improved performance with fewer resources, while maintaining high reactivity to changes in the environment and system.

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Heuristic reoptimization of time‐extended multi‐robot task allocation problems

Bischoff,

Kohn,

Hahn

et al. 2024

Networks

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Providing high quality solutions is crucial when solving NP‐hard time‐extended multi‐robot task allocation (MRTA) problems. Reoptimization, that is, the concept of making use of a known solution to an optimization problem instance when the solution to a similar problem instance is sought, is a promising and rather new research field in this application domain. However, so far no approximative time‐extended MRTA solution approaches exist for which guarantees on the resulting solution's quality can be given. We investigate the reoptimization problems of inserting as well as deleting a task to/from a time‐extended MRTA problem instance. For both problems, we can give performance guarantees in the form of an upper bound of 2 on the resulting approximation ratio for all heuristics fulfilling a mild assumption. We furthermore introduce specific solution heuristics and prove that smaller and tight upper bounds on the approximation ratio can be given for these heuristics if only temporal unconstrained tasks and homogeneous groups of robots are considered. A conclusory evaluation of the reoptimization heuristic demonstrates a near‐to‐optimal performance in application.

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Multi-Robot Mission Planning with Static Energy Replenishment

Cited by 24 publications

References 25 publications

Obround trees: sparsity enhanced feedback motion planning of differential drive robotic systems

Obround trees: sparsity enhanced feedback motion planning of differential drive robotic systems

Co-Regulated Consensus of Cyber-Physical Resources in Multi-Agent Unmanned Aircraft Systems

Heuristic reoptimization of time‐extended multi‐robot task allocation problems

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